HR & People (employment)
Shift Rostering, Task Allocation & Gig Dispatch
High RiskUnverifiedDiscuss / dispute
An algorithm builds shift rosters or dispatches jobs to employees or platform couriers from availability, demand forecasts and individual performance data, sets per-job pay or bonuses, and flags workers for deprioritisation or deactivation.
Consensus classification rationale: Annex III point 4(b) covers allocating tasks based on individual behaviour or personal traits and monitoring or evaluating performance in work-related relationships — an algorithm that decides who works when, who gets which job and at what pay is that system, and because it profiles individual workers the Art. 6(3) override keeps it high-risk. Automated deactivation or deprioritisation with significant effects engages GDPR Art. 22; the employment-context rules of Art. 88 lead to § 26 BDSG in Germany, and the works council co-determines start and end of daily working time, its distribution over the week and technical performance monitoring (§ 87(1) Nr. 2, 3 and 6 BetrVG). Every roster the system issues is bound by the Working Time Directive's rest and maximum-hours rules. Where the work is organised through a digital labour platform the Platform Work Directive's algorithmic-management chapter applies once transposed (by 2 December 2026); the equality directives apply where allocation criteria have a disparate effect.
This profile is incomplete:no threats modelled. That is a gap in the graph, not a statement that nothing applies — propose the missing links →
Decision attributes in force
Autonomyautonomous-with-override — Drives the human-oversight duties (Art. 14, Art. 26(2)) and Art. 50 disclosure.
Profiling of natural personsyes — Feeds the Art. 6(3) second-subparagraph override directly — profiling makes the derogation categorically unavailable.
Affected subjectsnatural-person — Instruments scoped to natural persons drop out of scope when only legal entities are assessed.
Deployer typeprivate-enterprise — Selects between the recorded alternate classification readings.
Role in the value chaindeployer — Splits provider duties, deployer duties and upstream GPAI duties.
Consequential scoringyes — Consequential scoring of natural persons requires intrinsic interpretability, not post-hoc explanation only.
Indicative decision support, not legal advice. Risk classification depends on your concrete deployment context and can change with scope drift — validate the result with qualified counsel.
Target market(s)European UnionUnited States (federal)change
Changes which instruments below count as in scope for this profile.
Target markets: European Union, United States (federal)
Regulatory footprint
11 instruments across 4 of 7 regulatory domains, plus 32 standards references- AI law1 instrument
- Data protection2 instruments
- Cyber & resiliencenone triggered
- Online safety & platforms1 instrument
- Product safetynone triggered
- Financial servicesnone triggered
- Sector & employment7 instruments
- Standards32 references
By jurisdiction
- EU7European UnionDigital Services Act, EU AI Act, EU Employment Equality Directives (2000/78 et al.), GDPR, Platform Work Directive (EU) 2024/2831, Platform-to-Business Regulation (EU) 2019/1150, Working Time Directive 2003/88/EC
- DE2thinGermany§ 26 BDSG — Beschäftigtendatenschutz (DE), § 87(1) No. 6 BetrVG — Works-Council Co-Determination
- CA2CanadaDigital Platform Workers' Rights Act, 2022 (Ontario, CA), PIPEDA (CA)
The AI Act is one dimension of this footprint, not the whole of it — every domain above carries its own obligations and deadlines. See the instruments in the graph →
Confidence in this chain of evidenceConfidence: Check-worthy
The chain holds, but at least one hop rests on a secondary source, an ageing verification or a practice-derived step. Check the flagged hops before you rely on them.
Computed weakest-link over 68 evaluated hops across 1 target market: a chain is only as strong as its weakest step, so the band follows the worst hop rather than an average that would hide it. Five factors per hop — source tier, verification age, status certainty, community hardening, derivation kind — all read from graph data, never from a hand-set score.
Why this band12 factors lowered the band — each links to the claim behind it
- Source tier: § 26 BDSG — Beschäftigtendatenschutz (DE) carries no resolvable citation — the claim is uncited. open node →
- Source tier: EU Employment Equality Directives (2000/78 et al.) carries no resolvable citation — the claim is uncited. open node →
- Source tier: Digital Platform Workers' Rights Act, 2022 (Ontario, CA) carries no resolvable citation — the claim is uncited. open node →
- Source tier: NIST AI RMF 1.0 carries no resolvable citation — the claim is uncited. open node →
- Source tier: FAIR-AIR / FAIR-MAM carries no resolvable citation — the claim is uncited. open node →
- Source tier: NIST AI 600-1 (GenAI Profile) carries no resolvable citation — the claim is uncited. open node →
- Source tier: prEN 18228 (AI Risk Management) rests on a secondary source (tracker or summary), not on the primary text. open node → primary source →
- Source tier: JTC 21 Technical Package (prEN 18228/18229/18281–83) rests on a secondary source (tracker or summary), not on the primary text. open node → primary source →
- Source tier: prEN 18284 (Data Sets and Data Governance) rests on a secondary source (tracker or summary), not on the primary text. open node → primary source →
- Source tier: prEN 18283 (Bias Treatment) rests on a secondary source (tracker or summary), not on the primary text. open node → primary source →
- Source tier: TAGOF (Audit-as-Code) carries no resolvable citation — the claim is uncited. open node →
- Source tier: OWASP Agentic Security (AST10 / Core Risks) carries no resolvable citation — the claim is uncited. open node →
Compliance brief
This use case is high-risk under the EU AI Act (High Risk); the provider and deployer obligations apply in full.
What is owed
- Art. 9. Continuous, iterative risk-management system across the whole lifecycle: identify, estimate, evaluate, mitigate; testing incl.
- Art. 10. Quality criteria for training/validation/test data: relevance, representativeness, error-freeness, bias detection & mitigation, data-governance procedures.
- Art. 11. Annex IV technical file before placing on market: system description, architecture, capabilities/limitations, risk measures — kept up to date.
- Art. 12. Requires logging CAPABILITY over the system's lifetime, recording events relevant to identifying situations that may present an Art.
- Art. 13. Instructions for use: capabilities, limitations, intended purpose, human-oversight measures, expected accuracy.
Dates that bind
- 2024-08-01 — AI Act enters into force. Regulation (EU) 2024/1689 in force; countdown for all staged obligations starts.
- 2025-02-02 — Prohibitions + AI literacy. Art. 5 prohibited practices ban applies (manipulation, social scoring, untargeted face scraping, workplace emotion recognition); Art. 4 AI literacy duty.
Maximum exposure
- EU AI Act: Tiered: €35m / 7% (prohibited practices); €15m / 3% (Art. 9–15 high-risk obligations incl. data governance, documentation, logging); €7.5m / 1% (Art. 99(5) — incorrect, incomplete or misleading information to notified bodies or national competent authorities)
- GDPR: Up to €20m or 4% of worldwide annual turnover
- § 26 BDSG: The BDSG's own fine provision (§ 43) reaches only breaches of § 30 (fines up to 50,000 EUR) and does not cover § 26; breaches of § 26 are enforced through the directly applicable GDPR — administrative fines under Art. 83 and compensation claims under Art. 82 (e.g. 200 EUR damages awarded in BAG 8 AZR 209/21).
- § 87(1) No. 6 BetrVG: Injunction against use; works-council enforcement proceedings; unusable evidence in employment disputes
- Working Time Directive 2003/88/EC: Transposed nationally (Germany: Arbeitszeitgesetz); Member States set penalties and labour inspectorates enforce.
- EU Employment Equality Directives (2000/78 et al.): Art. 17 leaves penalties to the Member States, which must lay down rules on sanctions for infringements of the national transposing provisions that may comprise payment of compensation to the victim and must be effective, proportionate and dissuasive (mirrored in 2000/43 Art. 15 and 2006/54 Art. 25).
- Platform Work Directive (EU) 2024/2831: Member States lay down effective, proportionate and dissuasive penalties (Art. 27); infringements of Arts 7–11 are also GDPR infringements where personal data is concerned (Art. 7(3)).
- Platform-to-Business Regulation (EU) 2019/1150: Member-State designated-body enforcement plus representative-organisation court action under Art. 14.
- Digital Platform Workers' Rights Act, 2022 (Ontario, CA): Ontario Ministry of Labour enforcement; employment-standards-style compliance orders.
First five actions
- Confirm in writing whether this organisation builds/places the system on the market (provider) or only operates it (deployer), since the role is not yet established.
- Commission and confirm the Art. 9, Art. 10, Art. 11 obligations named above as active workstreams with an accountable owner.
- Design and document a human-oversight procedure appropriate to how this system is used.
- Produce the technical documentation and evidence artefacts already mapped to this use case (Live Risk Register / Posture Management, Watchdog Supervisor & Rate Limiting, Deterministic Policy Engine (OPA / Cedar)) before they are requested.
- Put 2024-08-01 — AI Act enters into force — into the compliance calendar with an owner and lead time.
Terms used above: · · ·
This brief is based on partial coverage — no threat profile is mapped yet.
Classification precedent
Consensus reading: High Risk open in the graph →
Annex III point 4(b) covers allocating tasks based on individual behaviour or personal traits and monitoring or evaluating performance in work-related relationships — an algorithm that decides who works when, who gets which job and at what pay is that system, and because it profiles individual workers the Art. 6(3) override keeps it high-risk. Automated deactivation or deprioritisation with significant effects engages GDPR Art. 22; the employment-context rules of Art. 88 lead to § 26 BDSG in Germany, and the works council co-determines start and end of daily working time, its distribution over the week and technical performance monitoring (§ 87(1) Nr. 2, 3 and 6 BetrVG). Every roster the system issues is bound by the Working Time Directive's rest and maximum-hours rules. Where the work is organised through a digital labour platform the Platform Work Directive's algorithmic-management chapter applies once transposed (by 2 December 2026); the equality directives apply where allocation criteria have a disparate effect.
What the reading rests on — the provisions this classification actually pulls in:
- Art. 9 — Risk Management
- Art. 10 — Data Governance
- Art. 11 — Technical Documentation
- Art. 12 — Record-Keeping / Logging
- Art. 13 — Transparency to Deployers
- Art. 14 — Human Oversight
- Art. 15 — Accuracy, Robustness, Cybersecurity
- Art. 17 — Quality Management System
No dissenting reading is recorded for this case. That means nobody has filed one yet — not that the classification is beyond argument. file a dissent with a source →
Baseline: of 100+, 40% were not definitively classifiable (18% clearly high-risk, 42% clearly low-risk). appliedAI Institute — AI Act risk classification of AI systems from a practical perspective
Applicable Regulations (11)
EU AI Act (Regulation (EU) 2024/1689)
unverified · verified 2026-08-12 source (as amended) EUR-LexAmended by Regulation (EU) 2026/1744. Verified 16 Aug 2026: the popular mirrors have not yet been updated — artificialintelligenceact.eu still serves the unamended 13 June 2024 text with no disclaimer, and the Commission's AI Act Service Desk pages still show pre-omnibus text with a visible omnibus disclaimer. Read the OJ or consolidated text on EUR-Lex. in force EU
Horizontal, risk-based product-safety law for AI systems and GPAI models. Extraterritorial market-place principle. Staged applicability 2025–2030 (Digital Omnibus: Art. 50 → 2 Aug 2026, Annex III → 2 Dec 2027, Annex I → 2 Aug 2028). (Digital Omnibus: Regulation (EU) 2026/1744, in force 27 July 2026).
Sanctions: Tiered: €35m / 7% (prohibited practices); €15m / 3% (Art. 9–15 high-risk obligations incl. data governance, documentation, logging); €7.5m / 1% (Art. 99(5) — incorrect, incomplete or misleading information to notified bodies or national competent authorities)
GDPR (Regulation (EU) 2016/679)
Applies unchanged next to the AI Act for all personal data in training, fine-tuning, RAG and inference. Key friction points: Art. 22 automated decisions, Art. 17 erasure vs. AI Act logging, Art. 35 DPIA.
Sanctions: Up to €20m or 4% of worldwide annual turnover
§ 26 BDSG — Beschäftigtendatenschutz (DE) (§ 26 Bundesdatenschutzgesetz (BDSG) vom 30. Juni 2017 (BGBl. I S. 2097) — Datenverarbeitung für Zwecke des Beschäftigungsverhältnisses)
in-force · verified 2026-09-11 in force DEthin
§ 26 BDSG is Germany's national employment-data rule adopted under the opening clause of Art. 88 GDPR: it permits processing of employees' (including applicants' and former employees') personal data only where necessary for the decision to establish, for the performance or for the termination of an employment relationship, sets stricter conditions for consent (Abs. 2) and for special categories such as health or biometric data (Abs. 3), and allows processing on the basis of works/collective agreements (Abs. 4). Any AI system that screens applicants, scores performance, analyses video interviews or identifies staff biometrically processes Beschäftigtendaten and must satisfy this necessity test. Following the CJEU judgment C-34/21 (30 March 2023), the Bundesarbeitsgericht held on 8 May 2025 (8 AZR 209/21) that § 26 Abs. 1 BDSG does not meet Art. 88(2) GDPR and must remain unapplied, so lawfulness is assessed directly under Art. 6 GDPR while § 26 remains formally in force.
Sanctions: The BDSG's own fine provision (§ 43) reaches only breaches of § 30 (fines up to 50,000 EUR) and does not cover § 26; breaches of § 26 are enforced through the directly applicable GDPR — administrative fines under Art. 83 and compensation claims under Art. 82 (e.g. 200 EUR damages awarded in BAG 8 AZR 209/21).
§ 87(1) No. 6 BetrVG — Works-Council Co-Determination
Works Constitution Act § 87(1) No. 6: the works council has a co-determination right on the introduction and use of technical devices designed to monitor the behaviour or performance of employees. In practice this makes works-council consent — usually a works agreement — a hard legal precondition for ANY employee-monitoring or performance-analytics AI in Germany, independent of the system's AI Act tier: without it the introduction is unlawful and the outputs are contestable. It is a procedural precondition, not a technical control, and no engineering measure substitutes for it.
Sanctions: Injunction against use; works-council enforcement proceedings; unusable evidence in employment disputes
Working Time Directive 2003/88/EC (Directive 2003/88/EC of the European Parliament and of the Council of 4 November 2003 concerning certain aspects of the organisation of working time)
Art. 3 guarantees a minimum daily rest of 11 consecutive hours per 24-hour period, Art. 5 a weekly rest period, Art. 6(b) an average working week of no more than 48 hours, and Art. 8 limits night work; the CJEU (C-55/18 CCOO) requires employers to set up an objective, reliable and accessible system to measure daily working time. An automated rostering or dispatch system that assigns shifts is bound by these limits for every schedule it produces.
Sanctions: Transposed nationally (Germany: Arbeitszeitgesetz); Member States set penalties and labour inspectorates enforce.
EU Employment Equality Directives (2000/78 et al.) (Council Directive 2000/78/EC of 27 November 2000 establishing a general framework for equal treatment in employment and occupation (anchor act) — bundled for the employment scope with Council Directive 2000/43/EC of 29 June 2000 implementing the principle of equal treatment between persons irrespective of racial or ethnic origin, and Directive 2006/54/EC of the European Parliament and of the Council of 5 July 2006 on the implementation of the principle of equal opportunities and equal treatment of men and women in matters of employment and occupation (recast))
in-force · verified 2026-09-11 in force EU
The three directives prohibit direct and indirect discrimination in access to employment, self-employment and occupation (including selection criteria, recruitment conditions and promotion), vocational training, and employment and working conditions including dismissals and pay — on grounds of religion or belief, disability, age and sexual orientation (2000/78), racial or ethnic origin (2000/43) and sex (2006/54). Indirect discrimination arises whenever an apparently neutral provision, criterion or practice puts persons of a protected group at a particular disadvantage unless it is objectively justified by a legitimate aim with appropriate and necessary means, and Art. 10 shifts the burden of proof to the respondent once facts from which discrimination may be presumed are established. An AI system that screens, ranks, scores or otherwise conditions access to jobs, promotion, pay or dismissal therefore operates as such a criterion or practice: the employer or intermediary must be able to justify any disparate effect it produces, and for disability must also provide reasonable accommodation (Art. 5).
Sanctions: Art. 17 leaves penalties to the Member States, which must lay down rules on sanctions for infringements of the national transposing provisions that may comprise payment of compensation to the victim and must be effective, proportionate and dissuasive (mirrored in 2000/43 Art. 15 and 2006/54 Art. 25).
Platform Work Directive (EU) 2024/2831 (Directive (EU) 2024/2831 of the European Parliament and of the Council of 23 October 2024 on improving working conditions in platform work)
enacted-not-yet-applicable · verified 2026-09-15 status source ↗ source EUR-Lex enacted — not yet applicable EU
Chapter III governs algorithmic management by digital labour platforms: Art. 7 limits the personal data automated monitoring and decision-making systems may process; Art. 9 requires transparency towards platform workers and their representatives on automated monitoring and decision-making systems; Art. 10 requires human oversight of the impact of such systems on working conditions; Art. 11 gives workers the right to an explanation and human review of significant decisions, including account restriction, suspension or termination. Member States must transpose by 2 December 2026 (Art. 29(1)) — until then the duties are upcoming, not binding.
Sanctions: Member States lay down effective, proportionate and dissuasive penalties (Art. 27); infringements of Arts 7–11 are also GDPR infringements where personal data is concerned (Art. 7(3)).
Digital Services Act (Regulation (EU) 2022/2065)
Intermediary services: recommender-system transparency, risk assessments for very large platforms.
Platform-to-Business Regulation (EU) 2019/1150 (Regulation (EU) 2019/1150 on promoting fairness and transparency for business users of online intermediation services)
Requires online platforms to disclose the main ranking parameters used for business users (e.g. restaurants on a delivery platform) and whether/how paid remuneration can influence ranking.
Sanctions: Member-State designated-body enforcement plus representative-organisation court action under Art. 14.
PIPEDA (CA)
Federal private-sector privacy law, operative until Bill C-36 passes.
Digital Platform Workers' Rights Act, 2022 (Ontario, CA) (Digital Platform Workers' Rights Act, 2022, S.O. 2022, c. 7, Sched. 1)
in-force · verified 2026-09-18 in force CA
Ontario statute requiring digital platform operators to disclose, within 24 hours of granting access, how pay is calculated and what factors determine whether work assignments are offered, regardless of the worker's employee/contractor classification.
Sanctions: Ontario Ministry of Labour enforcement; employment-standards-style compliance orders.
Legal Obligations (24)
density
Art. 9 — Risk Management
Continuous, iterative risk-management system across the whole lifecycle: identify, estimate, evaluate, mitigate; testing incl. against misuse.
unverified · no verification date read the article artificialintelligenceact.eu
Art. 10 — Data Governance
Quality criteria for training/validation/test data: relevance, representativeness, error-freeness, bias detection & mitigation, data-governance procedures.
unverified · no verification date read the article artificialintelligenceact.eu
Art. 11 — Technical Documentation
Annex IV technical file before placing on market: system description, architecture, capabilities/limitations, risk measures — kept up to date.
unverified · no verification date read the article artificialintelligenceact.eu
Art. 12 — Record-Keeping / Logging
Requires logging CAPABILITY over the system's lifetime, recording events relevant to identifying situations that may present an Art. 79(1) risk or lead to a substantial modification, and to post-market monitoring (Art. 72) and deployer monitoring (Art. 26(5)). SCOPE: the itemised minimum log content of Art. 12(3) — period of each use with start and end date and time; the reference database against which input data was checked; the input data for which the search led to a match; the identification of the natural persons involved in verification of the results per Art. 14(5) — applies ONLY to Annex III point 1(a) remote biometric identification systems, not to all high-risk systems. RETENTION: providers (Art. 19) and deployers (Art. 26(6)) must keep the logs under their control for a period appropriate to the intended purpose, at least six months, unless other Union or national law — in particular data-protection law — provides otherwise. A bolted-on logging wrapper does not satisfy the requirement: logging must be core architecture.
in-force · verified 2026-08-12 read the article artificialintelligenceact.eu
Art. 13 — Transparency to Deployers
Instructions for use: capabilities, limitations, intended purpose, human-oversight measures, expected accuracy.
unverified · no verification date read the article artificialintelligenceact.eu
Art. 14 — Human Oversight
Effective human oversight by qualified, trained natural persons with real authority to intervene, override and stop; interface duties (interpretability, automation-bias countermeasures); oversight must be commensurate with autonomy level. Competence and authority of the overseers is itself a testable control objective — training records and oversight protocols are its evidence.
unverified · no verification date read the article artificialintelligenceact.eu
Art. 15 — Accuracy, Robustness, Cybersecurity
Appropriate accuracy levels, resilience against errors and adversarial attacks (data poisoning, evasion, prompt injection), declared metrics.
unverified · no verification date read the article artificialintelligenceact.eu
Art. 17 — Quality Management System
Product-focused QMS for providers: strategy, design controls, data management, post-market monitoring — target of EN 18286:2026, the first AI Act harmonised-standard candidate to be published; presumption of conformity applies only once it is cited in the OJEU, which is still pending.
unverified · no verification date read the article artificialintelligenceact.eu
Art. 43 — Conformity Assessment
Two pathways: internal control self-assessment (Annex VI) for most Annex III systems (HR, credit, education) — provider verifies QMS (Art. 17), technical file (Annex IV) and design consistency; notified-body assessment (Annex VII) mandatory for safety components in harmonised products (medical devices, aviation, rail) and remote biometric identification. Harmonised standards in the OJEU give presumption of conformity. Art. 43(4): a substantial modification of a high-risk system within Art. 3(23) requires a fresh conformity assessment — BUT changes that were pre-determined by the provider at the time of the initial conformity assessment and documented in the technical documentation under Annex IV point 2(f) do NOT constitute a substantial modification. That carve-out is the legal basis on which a continual-learning system may keep retraining after certification, and its boundary is exactly the documented corridor.
in-force · verified 2026-08-17 read the article artificialintelligenceact.eu
Art. 72/73 — Post-Market Monitoring & Incidents
Post-market monitoring plan and serious-incident reporting (15 days; 2 days for widespread infringement) to market-surveillance authorities.
in-force · verified 2026-08-11 read the article ai-act-service-desk.ec.europa.eu
Art. 26 — Deployer Obligations
Use per instructions, assign competent human oversight, input-data control, inform workers, incident duty. Art. 26(6): keep the logs generated by the high-risk system that are under the deployer's control for a period appropriate to the intended purpose, at least six months, unless other Union or national law — in particular data-protection law — provides otherwise.
in-force · verified 2026-08-12 read the article artificialintelligenceact.eu
Art. 47/48 — CE Marking & Declaration of Conformity
After passing conformity assessment: written EU Declaration of Conformity (retained 10 years) and visible, indelible CE marking — for digital systems a digital CE mark accessible via UI or machine-readable code; notified-body number displayed where one was involved.
unverified · no verification date read the article artificialintelligenceact.eu
Art. 27 — Fundamental Rights Impact Assessment
Deployers that are public bodies, or private operators providing public services, plus deployers of creditworthiness and life/health insurance pricing systems, must assess the fundamental-rights impact before first use: processes of use, period and frequency, affected persons and groups, specific risks of harm, human-oversight implementation, and the measures taken if risks materialise. Art. 27(4): where a DPIA has already been carried out, the FRIA complements it.
in-force · verified 2026-08-11 read the article artificialintelligenceact.eu
Art. 6 — Classification rules for high-risk AI systems
Decides whether an Annex III system is high-risk. Paragraph 3 opens a derogation for systems that pose no significant risk of harm, subject to four disjunctive conditions — but profiling of natural persons always keeps the system high-risk. A provider that uses the derogation still owes a documented assessment before market placement, Art. 49(2) registration in the EU database (Art. 71) and production of the documentation on request (Art. 6(4)).
in-force · verified 2026-08-16 source (as amended) EUR-Lexconvenience mirror — not updated artificialintelligenceact.euAmended by Regulation (EU) 2026/1744. Verified 16 Aug 2026: the popular mirrors have not yet been updated — artificialintelligenceact.eu still serves the unamended 13 June 2024 text with no disclaimer, and the Commission's AI Act Service Desk pages still show pre-omnibus text with a visible omnibus disclaimer. Read the OJ or consolidated text on EUR-Lex.
Art. 86 — Right to explanation of individual decision-making
An affected person subject to a decision taken by a deployer on the basis of the output of an Annex III high-risk system, where that decision produces legal effects or similarly significantly affects them, may obtain clear and meaningful explanations of the role of the AI system in the decision procedure and the main elements of the decision taken.
in-force · verified 2026-08-17 read the article artificialintelligenceact.eu
GDPR Art. 22 — Automated Decisions
Right not to be subject to solely automated decisions with legal/similar effect; requires meaningful human involvement or explicit legal basis + safeguards.
unverified · no verification date read the article EUR-Lex
GDPR Art. 27 — EU Representative
A controller or processor not established in the Union that falls within Art. 3(2) scope (offering goods or services to, or monitoring the behaviour of, data subjects in the Union) must designate in writing a representative established in a Member State where the relevant data subjects are. The representative is mandated to be addressed by supervisory authorities and data subjects, in addition to or instead of the controller/processor, on all compliance issues — without prejudice to legal action against the controller/processor itself. Exempt: (a) occasional processing that does not involve large-scale special-category or criminal-conviction data and is unlikely to result in a risk to individuals, or (b) public authorities or bodies.
in-force · verified 2026-09-10 read the article EUR-Lex
GDPR Art. 17 — Erasure
Right to erasure collides with AI Act Art. 12 immutable logging — resolved architecturally via bitemporal data modelling + physical partition scrub.
unverified · no verification date read the article EUR-Lex
GDPR Art. 25 — Data Protection by Design
Privacy by design & default: minimisation, pseudonymisation, PII filters in pipelines and vector stores.
unverified · no verification date read the article EUR-Lex
GDPR Art. 35 — DPIA
Data-protection impact assessment for high-risk processing — pairs with AI Act fundamental-rights impact assessment (Art. 27) for public-facing high-risk systems.
unverified · no verification date read the article EUR-Lex
GDPR Art. 33/34 — Personal-Data Breach Notification
Notification of a personal-data breach to the supervisory authority and, where the risk to individuals is high, to the affected individuals themselves.
in-force · verified 2026-08-11 read the article EUR-Lex
GDPR Art. 32 — Security of Processing
Controller and processor implement technical and organisational measures appropriate to the risk, including pseudonymisation and encryption, and measures ensuring the ongoing confidentiality, integrity, availability and resilience of processing systems. Access to personal data by an unauthorised recipient — including one reached through a derived index such as a vector store — is the harm this article addresses.
in-force · verified 2026-08-17 read the article EUR-Lex
GDPR Art. 9 — Special Categories of Personal Data
Processing of health, biometric and other special-category data is prohibited unless one of the Art. 9(2) conditions applies; where it is permitted, the appropriate safeguards travel with it. This is the anchor for de-identification of clinical imaging and for the minimisation of health data in training and retrieval corpora.
in-force · verified 2026-08-17 read the article EUR-Lex
GDPR Art. 88 — Processing in the Employment Context
Opening clause: Member States may provide more specific rules for processing employees' personal data in the employment context, by law or by collective agreement, including suitable safeguards for human dignity, legitimate interests and fundamental rights, with particular regard to monitoring systems at the workplace. It is the bridge through which national employment rules — in Germany the BetrVG co-determination right and § 26 BDSG — govern workplace AI alongside the GDPR itself.
in-force · verified 2026-08-17 read the article EUR-Lex
Control Objectives (17)
obligation (article) → operationalized_by → control objective → satisfied_by → component/pattern; control objective → evidenced_by → evidence artifact
Art. 9
Pre-Deployment AI Risk & Impact Assessment
ISO 42001 Clauses 6.1 / 8.2: every workflow is risk-assessed and impact-assessed before production, covering fundamental rights, availability, privacy and legal liability.
ISO/IEC 42001 clause 8.2 (indicative)
satisfied by: AI Register & Model Registry / Factsheets
evidenced by: AI Impact Assessment (AIIA)
Art. 10
Data Quality & Origin Management
ISO 42001 Annex A.7: training, fine-tuning and RAG corpora are audited for lineage, quality, representativeness and IP clearance.
ISO/IEC 42001 clause A.7 (indicative)
satisfied by: Data Lineage & Versioning
evidenced by: Dataset Documentation & Bias Reports
Art. 11
control layer: community mandate — propose objectives
Art. 12
Log Completeness & Coverage
Every lifecycle-relevant event is captured with the FprEN ISO/IEC 24970 field set (input/output traces, timestamps, acting identity, referenced sources, human overrides) across all execution paths — including tool calls, retries and degraded modes. Testable: trace-coverage sampling against the event taxonomy; gaps are findings.
satisfied by: OpenTelemetry / FCoT TracingWORM / Immutable Audit Vault
evidenced by: Event Logs & Decision Traces
Log Integrity & Non-Repudiation
Recorded events cannot be altered, reordered or deleted without detection — including by administrators of the log store. Testable: hash-chain verification, external-anchor reconciliation, red-team attempt to rewrite history. This is the control objective the WORM vault alone does not fully satisfy.
evidenced by: Event Logs & Decision Traces · Immutable Decision Ledger (WORM)
Log Access & Retention Governance
Logs are retained per regime (deployer ≥ 6 months, financial institutions per audit rules, provider lifetime scopes), access is least-privilege and itself logged, and lawful disclosure (authority request, PLD disclosure order) is executable without exposing unrelated data. Testable: retention audit, access-review, disclosure fire drill.
evidenced by: Human-Oversight Protocol & Intervention Records
Art. 13
System Traceability & Decision Transparency
ISO 42001 Annex A.8: documented mechanisms explain how a given decision was produced, enabling root-cause analysis after an incident.
ISO/IEC 42001 clause A.8 (indicative)
satisfied by: OpenTelemetry / FCoT Tracing
evidenced by: AI System Model Card
Art. 14
Oversight Competence & Authority
The natural persons overseeing the system are trained for it, have documented authority to intervene/override/stop, actually exercise it (override rates monitored against automation bias), and their competence is refreshed on system change. Testable: training records, oversight-protocol drills, override statistics.
ISO/IEC 42001 clause 7.2 (indicative)
satisfied by: HITL Escalation Queue & Review UI
evidenced by: AI Literacy Training Records · Human-Oversight Protocol & Intervention Records · Human Oversight Operating Standard (SOP)
Art. 15
Adversarial Robustness Verified
Resilience against evasion, poisoning and extraction is demonstrated against the declared threat model at a defined cadence, with metrics per ISO/IEC 4213 / 24029 and regressions gated. Testable: red-team reports with reproducible probes; a passed probe from last year is not evidence for this year's model.
satisfied by: Bias Testing & Data Quality Pipeline
evidenced by: Accuracy, Robustness & Red-Teaming Reports
Runtime Injection Defense
Direct and indirect prompt injection attempts are detected, contained or neutralized at runtime across all ingestion paths (user input, retrieved documents, tool outputs, inter-agent messages). Testable: guardrail telemetry with measured catch rates against a maintained injection corpus; command/data separation enforced structurally, not by instruction.
satisfied by: Input Rails / Prompt ShieldsGuardrail Sidecar / InterceptionTrinity Defense (TCB + Command Gates + IFC)Tool-Use Boundary Proxy
evidenced by: Guardrail Telemetry & Sanitization Records
Non-Human Identity Governance
Every agent, tool caller and service account has a distinct identity, receives credentials that are ephemeral and scoped to a single task, and loses them automatically on completion, escalation or anomaly. No agent holds a standing credential to a production system. Testable: inventory of non-human identities vs. issued credentials, median credential lifetime, revocation drill, search for long-lived secrets in agent configuration.
evidenced by: Event Logs & Decision Traces
Art. 17
Model Registry Gate & Drift Interlock
Practice-derived control objective: no model version reaches production except through a registry gate that evaluates the candidate against the corridors recorded in the Predetermined Change Control Plan. Distribution drift is measured (population stability index, distributional distance on the decisive features, performance on the held-out and subgroup slices) and the gate BLOCKS the automated rollout when a corridor is exceeded, routing the change to re-assessment instead of shipping it. Testable: a deliberately drifted candidate must be refused; every rollout must have a gate decision record; a bypassed gate must be detectable after the fact.
ISO/IEC 42001 clause 9.1 (indicative)
evidenced by: Drift-Gate Decision Log · Predetermined Change Control Plan (PCCP)
Art. 43
Model Registry Gate & Drift Interlock
Practice-derived control objective: no model version reaches production except through a registry gate that evaluates the candidate against the corridors recorded in the Predetermined Change Control Plan. Distribution drift is measured (population stability index, distributional distance on the decisive features, performance on the held-out and subgroup slices) and the gate BLOCKS the automated rollout when a corridor is exceeded, routing the change to re-assessment instead of shipping it. Testable: a deliberately drifted candidate must be refused; every rollout must have a gate decision record; a bypassed gate must be detectable after the fact.
ISO/IEC 42001 clause 9.1 (indicative)
evidenced by: Drift-Gate Decision Log · Predetermined Change Control Plan (PCCP)
Art. 72/73
Performance Monitoring & Drift Management
ISO 42001 Clauses 9 & 10: continuous accuracy, drift and incident monitoring feeding internal audit and recertification.
ISO/IEC 42001 clause 9.1 (indicative)
satisfied by: Model Drift & Accuracy Monitor
evidenced by: Post-Market Monitoring Plan & Incident Reports
Model Registry Gate & Drift Interlock
Practice-derived control objective: no model version reaches production except through a registry gate that evaluates the candidate against the corridors recorded in the Predetermined Change Control Plan. Distribution drift is measured (population stability index, distributional distance on the decisive features, performance on the held-out and subgroup slices) and the gate BLOCKS the automated rollout when a corridor is exceeded, routing the change to re-assessment instead of shipping it. Testable: a deliberately drifted candidate must be refused; every rollout must have a gate decision record; a bypassed gate must be detectable after the fact.
ISO/IEC 42001 clause 9.1 (indicative)
evidenced by: Drift-Gate Decision Log · Predetermined Change Control Plan (PCCP)
Art. 26
Embedded-AI Vendor Governance
Every third-party system with embedded AI (closed SaaS, productized services, model APIs) is inventoried, risk-tiered and governed by contract and attestation — because inline runtime inspection is architecturally impossible in a vendor's closed execution path. Required: intake-based vendor risk workflow, contractual ZDR & non-training clauses, AI-BOM/Factsheet disclosure, attestation review cadence, exit strategy. Testable: sample vendor list vs. register completeness; check contracts for AI clauses; verify attestations current.
ISO/IEC 42001 clause A.10 (indicative)
satisfied by: Vendor & Model Due-Diligence KitAI Register & Model Registry / FactsheetsShadow-AI Discovery & Asset Inventory Scanner
evidenced by: Vendor & Model Due-Diligence Records · C5 / AIC4 Cloud Attestation · AI Bill of Materials (AI-BOM) & Factsheets · AI System Inventory / Registry Entry
Art. 47/48
control layer: community mandate — propose objectives
Art. 27
control layer: community mandate — propose objectives
Art. 6
control layer: community mandate — propose objectives
Art. 86
control layer: community mandate — propose objectives
GDPR Art. 22
control layer: community mandate — propose objectives
GDPR Art. 27
control layer: community mandate — propose objectives
GDPR Art. 17
control layer: community mandate — propose objectives
GDPR Art. 25
Vector & Chunk-Level Access Control
Practice-derived control objective (not named by any provision's own text): the requesting principal's read rights on every retrieved source segment are enforced before generation. Token- or claim-based ACL filtering is applied twice — at the chunker, which writes the source ACL into chunk metadata at ingestion, and at query time in the vector store, which filters candidates by the caller's entitlements — and a response-grounding check re-validates the caller's rights on each cited segment BEFORE the answer is composed. Testable: retrieval probe with a low-privilege principal against a restricted corpus; ACL drift reconciliation between source system and index; red-team reconstruction attempt from similarity results alone.
ISO/IEC 42001 clause A.7 (indicative)
evidenced by: Vector ACL Verification Report
GDPR Art. 35
control layer: community mandate — propose objectives
GDPR Art. 33/34
control layer: community mandate — propose objectives
GDPR Art. 32
Vector & Chunk-Level Access Control
Practice-derived control objective (not named by any provision's own text): the requesting principal's read rights on every retrieved source segment are enforced before generation. Token- or claim-based ACL filtering is applied twice — at the chunker, which writes the source ACL into chunk metadata at ingestion, and at query time in the vector store, which filters candidates by the caller's entitlements — and a response-grounding check re-validates the caller's rights on each cited segment BEFORE the answer is composed. Testable: retrieval probe with a low-privilege principal against a restricted corpus; ACL drift reconciliation between source system and index; red-team reconstruction attempt from similarity results alone.
ISO/IEC 42001 clause A.7 (indicative)
evidenced by: Vector ACL Verification Report
GDPR Art. 9
control layer: community mandate — propose objectives
GDPR Art. 88
control layer: community mandate — propose objectives
Take this into your GRC tooling
A control mapping your ISO/IEC 42001 or CSA AICM workbook can ingest, and an Annex IV skeleton to start the technical file from. Indicative mappings only — cells we are not confident about are exported empty rather than filled in.
Standards & Evidence
ISO/IEC 23894 (AI Risk Management)
AI risk-management guidance extending ISO 31000 — feeds the Art. 9 risk-management system.
unverified · no verification date publisher ISO
evidence for: Art. 9
NIST AI RMF 1.0
Govern–Map–Measure–Manage risk framework; Map/Measure functions populate the Art. 9 risk register with quantified values; the transatlantic mapping reference.
unverified · no verification date
evidence for: Art. 9
FAIR-AIR / FAIR-MAM
AI extension of Factor Analysis of Information Risk: Expected Financial Loss = Loss Event Frequency (threat frequency × vulnerability) × Loss Magnitude (primary + secondary), run as Monte Carlo distributions — the standard bridge from technical AI failure modes to board-level monetary exposure.
unverified · no verification date
evidence for: Art. 9
NIST AI 600-1 (GenAI Profile)
Companion profile to the AI RMF covering twelve GenAI-specific failure modes — confabulation, prompt injection, value-chain propagation and others — as the technical checklist behind Map/Measure for generative systems.
unverified · no verification date
evidence for: Art. 9 · Art. 15
prEN 18228 (AI Risk Management)
AI risk-management requirements deliverable of the JTC 21 core package — the harmonised-standard candidate behind Art. 9.
evidence for: Art. 9
ISO/IEC 5259 (Data Quality for ML)
Five-part data-quality framework (governance, process, management) — direct evidence path for Art. 10 representativeness and completeness.
unverified · no verification date publisher ISO
evidence for: Art. 10
JTC 21 Technical Package (prEN 18228/18229/18281–83)
CEN-CENELEC JTC 21 technical package under standardisation request M/593 (prEN 18228 trustworthiness, 18229 risk management, 18281–83 CV/NLP evaluation et al.); staged drafts, none OJEU-cited yet — Annex III applicability (Dec 2027) is Omnibus-coupled to their availability.
evidence for: Art. 10 · EU AI Act
prEN 18284 (Data Sets and Data Governance)
Data-set and data-governance deliverable of the JTC 21 core package — the concrete evidence path for Art. 10.
evidence for: Art. 10
prEN 18283 (Bias Treatment)
Bias-treatment deliverable of the JTC 21 core package — operational target for the Art. 10 data-governance duties on bias.
evidence for: Art. 10
EN ISO/IEC 22989 (AI Concepts)
Published terminology and concepts standard — the shared vocabulary layer for documentation and audits.
unverified · no verification date publisher ISO
evidence for: Art. 11
FprEN ISO/IEC 24970 (AI Logging)
Specifies event logging in AI systems — the concrete implementation target for Art. 12 record-keeping.
evidence for: Art. 12
TAGOF (Audit-as-Code)
Operationalizes governance as code in CI/CD: policy-as-code enforcement, continuous runtime telemetry and automatically generated audit evidence — the execution layer that replaces periodic audits with continuous assurance.
unverified · no verification date
evidence for: Art. 12
prEN ISO/IEC 12792 (Transparency Taxonomy)
Transparency taxonomy for AI systems: structured disclosure of system composition, data provenance, capabilities and limitations. The harmonised-norm candidate backing Art. 13 instructions-for-use and deployer-information duties — defines what a complete transparency package must contain.
evidence for: Art. 13
OWASP Agentic Security (AST10 / Core Risks)
Threat framework for autonomous agents: tool misuse, excessive agency, confused-deputy, memory poisoning — with AIVSS scoring.
unverified · no verification date
evidence for: Art. 14
IMDA Agentic AI Governance Framework (SG)
Model AI Governance Framework for Agentic AI (Jan 2026, updated Jun 2026) — first state-issued agentic-specific guidance: bounded autonomy levels, action-space and interface restrictions, human-in-command checkpoints, automation-bias controls, logging & attribution expectations. No legal force in the EU, but the most concrete public benchmark for Art. 14-style oversight design of agent systems.
published · verified 2026-08-04 status unsourced
evidence for: Art. 14
DIN SPEC 92001-1/-2/-3
AI life-cycle quality metamodel: functionality, robustness (adversarial & corruption), traceability/explainability — German operationalisation for Art. 15.
unverified · no verification date
evidence for: Art. 15
ISO/IEC 24029 (NN Robustness)
Robustness assessment of neural networks incl. formal methods (part 2) — supports Art. 15 evidence.
unverified · no verification date publisher ISO
evidence for: Art. 15
OWASP Top 10 for LLM Apps (2026)
The de-facto technical security standard for GenAI applications; maps to Art. 10/14/15 and ISO 42001 Annex A controls.
evidence for: Art. 15
ENISA Multilayer Framework & AI Threat Landscape
Three-layer good-practice model (cyber foundations → AI-specific → sectoral) and lifecycle threat landscape — the operational base for Art. 15 and CRA.
unverified · no verification date
evidence for: Art. 15
BSI C5:2026
Cloud compliance catalogue (168 requirements): post-quantum crypto, confidential computing, container security — infrastructure evidence layer for NIS2/CRA/Art. 15; binding baseline from June 2027.
unverified · no verification date
evidence for: Art. 15
ISO/IEC TS 4213 (ML Performance Measurement)
Assessment of machine-learning classification performance: standardized metrics, test-set discipline, reporting format. The metric backbone for Art. 15 'declared accuracy' — test reports that cite it are comparable across vendors and audits.
evidence for: Art. 15
BSI GenAI Criteria Catalogue
Criteria for integrating external generative models via API: named AI owner, central AI register, case-by-case risk analysis, multi-stage input/output validation, least privilege, prompt/permission separation.
unverified · no verification date
evidence for: Art. 15
IEC 61508 — Functional safety of E/E/PE safety-related systems
The base functional-safety standard: safety lifecycle, safety integrity levels (SIL) and systematic-capability requirements for electrical/electronic/programmable electronic safety-related systems. Referenced here for the safety-component reading of grid control; sector derivatives (e.g. IEC 61511, IEC 62443 for security) are not asserted as harmonised under the AI Act.
evidence for: Art. 15
EN 18286:2026 (QMS for AI Act)
Harmonised-norm candidate translating Art. 17 QMS into a product-focused governance framework; mappings to ISO 9001 and ISO/IEC 42001 Annex A (Annexes C & D); published as EN 18286:2026 in July 2026, OJEU citation (and with it the presumption of conformity) still pending.
evidence for: Art. 17
ISO/IEC 42001:2023 (AIMS)
ISO/IEC 42001:2023 — certifiable AI management system (Annex SL harmonized structure, PDCA logic, synergy discount when an ISO 27001 ISMS exists). Clauses 4–10 plus Annex A controls (control count 38 vs 39 is a live community dispute — counting method differs by edition/guide). Covers an estimated 40–50% of AI Act organizational duties; organizational certificate, no product presumption of conformity.
evidence for: Art. 17
IEC 62304:2006+AMD1:2015 (Medical device software life cycle)
Medical device software — software life-cycle processes: software safety classification (A/B/C), development planning, architecture, unit verification, integration and system testing, release, maintenance and problem resolution, and management of SOUP/off-the-shelf components. The recognised life-cycle spine for MDR software, and the process framework a notified body expects an AI-based diagnostic to be built inside.
evidence for: Art. 17
ISO/IEC 42006 (Audit Bodies)
Requirements for bodies auditing/certifying AIMS — accreditation basis (e.g. DAkkS) for ISO 42001 certificates.
unverified · no verification date publisher ISO
evidence for: Art. 43
prEN 18282 (AI Conformity Assessment)
Conformity-assessment deliverable of the JTC 21 core package — the assessment procedure behind Art. 43.
evidence for: Art. 43
ISO/IEC 42005 (AI Impact Assessment)
Guidance for AI system impact assessments — supports DPIA/FRIA-style analyses.
unverified · no verification date publisher ISO
evidence for: Art. 27 · GDPR Art. 35
ISO/IEC 27001:2022 + A.8.28
Information-security management; control A.8.28 (secure coding) is the natural anchor for AI code-generation and QA workflows alongside ISO 42001.
unverified · no verification date publisher ISO
evidence for: GDPR Art. 32
IEEE CertifAIEd™
Ethics certification (transparency, accountability, algorithmic bias, privacy) for products and professionals; interfaces with the EU ALTAI assessment list.
unverified · no verification date
evidence for: EU AI Act
prEN 18229-1 (Trustworthiness Framework, part 1)
Part 1 of the JTC 21 trustworthiness deliverable — the framework layer other prEN 18xxx documents build on.
evidence for: EU AI Act
Evidence you will need (29)
The concrete deliverables this use case's obligations ask for — grouped by what kind of artifact they are. Documentation is the largest single conformity cost block, so the list is a work plan, not a reading list. Full evidence matrix →
Documents & files (10)
Written deliverables an authority or auditor can request as a file.
Post-Market Monitoring Plan & Incident Reportstext-derivedserves 4 obligations
Art. 72 monitoring plan plus Art. 73 serious-incident reports (15 days; 2 days for widespread infringement) — reconciled in one runbook with GDPR Art. 33 (72h), NIS2 (24h/72h) and DORA timelines.
verifiability: documented artefact — verifiable on inspection
chain: Art. 72/73 — Post-Market Monitoring & Incidents · Art. 72/73 — Post-Market Monitoring & Incidents → CO: Performance Monitoring & Drift Management · DORA · NIS2 Directive · Clinical Imaging Triage & Patient Follow-Up · Cross-Border Statutory Tax & Wealth Filing · +2 more
AI Bill of Materials (AI-BOM) & Factsheetspractice-derived — dispute welcomeserves 2 obligations
Machine-readable composition manifest per AI application: base-model metadata (identifier, version, parameters, supplier tag), dataset provenance (fine-tune/RAG sources, scrubbing logs, consent records), vector-namespace bindings and access rules, active runtime-policy ruleset versions and thresholds, performance & safety verification history (bias scores, accuracy benchmarks, red-team reports). Complements the SBOM (software dependencies) with the AI-specific supply chain; auto-published into the register on every change. Feeds the Art. 11 technical file, vendor due diligence (contractually demanded from providers) and Colorado/LL144-class disclosure duties. Factsheets are its human-readable projection for auditors.
verifiability: tamper-evident
chain: Art. 11 — Technical Documentation · Art. 26 — Deployer Obligations → CO: Embedded-AI Vendor Governance · Continuous Technical Documentation Generation
AI System Model Cardpractice-derived — dispute welcomeserves 2 obligations
Model lineage, architecture, pre-training data sources, context limits, evaluation benchmarks and known failure modes.
verifiability: tamper-evident
chain: Art. 11 — Technical Documentation · Art. 13 — Transparency to Deployers → CO: System Traceability & Decision Transparency · Algorithmic Portfolio Execution & Advisory · Automated Financial Forecasting & Audit Trails · Continuous Technical Documentation Generation · Enterprise SDLC Code Automation & QA · +1 more
Dataset Documentation & Bias Reportstext-derivedserves 2 obligations
Art. 10 evidence: provenance and lineage of training/validation/test data, representativeness analysis, bias metrics and mitigation reports per ISO/IEC 5259 and BSI QUAIDAL data-quality metrics; baseline assumptions documented (Art. 10(2)(d)).
verifiability: documented artefact — verifiable on inspection
chain: Art. 10 — Data Governance · Art. 10 — Data Governance → CO: Data Quality & Origin Management · Digital Shelf Analytics & Competitive Intelligence · Generative Asset Production & Virtual Try-On · Supplier Master Data & ESG Risk Screening
EU Declaration of Conformitytext-derivedserves 2 obligations
Art. 47 written declaration that the high-risk system meets the AI Act requirements; renewed on substantial modification.
verifiability: documented artefact — verifiable on inspection
chain: Art. 43 — Conformity Assessment · Art. 47/48 — CE Marking & Declaration of Conformity · Continuous Technical Documentation Generation · Enterprise Marketing Disclosure Compliance · Regulatory Change Management & Policy Updating
Instructions for Use / Transparency Docstext-derivedserves 2 obligations
Art. 13 deployer-facing documentation: intended purpose, capabilities, limitations, expected accuracy, oversight measures — plus Art. 50 user-facing disclosures.
verifiability: documented artefact — verifiable on inspection
chain: Art. 13 — Transparency to Deployers · Art. 50 — Transparency Duties · Generative Asset Production & Virtual Try-On · Omnichannel Virtual Support & Voice Bots
Predetermined Change Control Plan (PCCP)text-derivedserves 2 obligations
Text-derived from Art. 43(4) read with Annex IV point 2(f): the documented description of the changes the provider pre-determined at the initial conformity assessment — the performance and data corridors within which automated retraining and redeployment may proceed, the methods used to make the change, and the limits beyond which the modification becomes substantial and a new conformity assessment is owed.
verifiability: self-asserted
chain: Art. 43 — Conformity Assessment · Art. 43 — Conformity Assessment → CO: Model Registry Gate & Drift Interlock
Technical Documentation (Annex IV)text-derivedserves 2 obligations
The Art. 11 technical file: system description, architecture, capabilities/limitations, risk measures, development process. Built incrementally during development — retro-reconstruction is an audit red flag. Reviewed by a notified body where the Annex VII route applies.
verifiability: self-asserted
chain: Art. 11 — Technical Documentation · Art. 43 — Conformity Assessment · Clinical Imaging Triage & Patient Follow-Up · Continuous Technical Documentation Generation
CE Marking (incl. digital)text-derived
Art. 48 visible/indelible marking; digital CE via UI or machine-readable code for digitally provided systems; notified-body number displayed where involved.
verifiability: documented artefact — verifiable on inspection
chain: Art. 47/48 — CE Marking & Declaration of Conformity
QMS Documentation (Art. 17 / EN 18286:2026)text-derived
Documented quality management system: design controls, development testing, validation, supplier management, post-market processes — the documentation set that EN 18286:2026 is expected to address once the standard is cited in the OJEU.
verifiability: documented artefact — verifiable on inspection
chain: Art. 17 — Quality Management System
Assessments (3)
A structured judgement about risk, rights or a management system.
FRIA / AI Impact Assessment (AIIA)text-derivedserves 3 obligations
Fundamental-rights impact assessment (Art. 27, deployer-side) generalized to the AI Impact Assessment: societal, legal and operational risk evaluation per ISO/IEC 42005 and ISO 42001 Clause 8.2, defining HITL intervention parameters and acceptable-use bounds. Cadence: pre-deployment, refreshed annually and on major model updates — a stale AIIA is a finding, not a document.
verifiability: documented artefact — verifiable on inspection
chain: Art. 26 — Deployer Obligations · Art. 27 — Fundamental Rights Impact Assessment · EU AI Act · Clinical Imaging Triage & Patient Follow-Up
AI Impact Assessment (AIIA)practice-derived — dispute welcomeserves 2 obligations
Societal, legal and operational risk evaluation per workflow, including the defined HITL intervention parameters and residual-risk acceptance.
verifiability: independently-attested
chain: Art. 9 — Risk Management · Art. 9 — Risk Management → CO: Pre-Deployment AI Risk & Impact Assessment · Clinical Imaging Triage & Patient Follow-Up · KYC & Client Onboarding Automation (Managed Service)
Data Protection Impact Assessment (DPIA)text-derivedserves 2 obligations
GDPR Art. 35 assessment for high-risk processing; supervisory-authority consultation where residual risk stays high. ISO/IEC 42005 provides the AI-specific method.
verifiability: documented artefact — verifiable on inspection
chain: § 26 Abs. 1 S. 1 — Erforderlichkeit für Begründung, Durchführung, Beendigung · GDPR Art. 35 — DPIA
Test reports (3)
Measured results from testing, evaluation or red-teaming.
Algorithmic Bias & Fairness Audit Reportpractice-derived — dispute welcomeserves 5 obligations
Quantitative demographic-parity, disparate-impact and false-positive distribution analysis against a fixed test baseline.
verifiability: independently-attested
chain: § 3 Abs. 2 — mittelbare Benachteiligung (neutral criteria or procedures) · 42 U.S.C. § 12112(b)(6) — screen-out selection criteria · Art. 10 — Data Governance · Art. 2(2)(b) — indirect discrimination through apparently neutral criteria · Art. 2(b) — indirect discrimination through apparently neutral criteria · KYC & Client Onboarding Automation (Managed Service)
Accuracy, Robustness & Red-Teaming Reportspractice-derived — dispute welcomeserves 2 obligations
Art. 15 evidence: declared accuracy metrics, adversarial and corruption robustness results (DIN SPEC 92001-2, ISO 24029), penetration and jailbreak-resistance testing, groundedness evaluation scores.
verifiability: independently-attested
chain: Art. 15 — Accuracy, Robustness, Cybersecurity · Art. 15 — Accuracy, Robustness, Cybersecurity → CO: Adversarial Robustness Verified · Enterprise SDLC Code Automation & QA · LLM01 Prompt Injection
Vector ACL Verification Reportpractice-derived — dispute welcome
Practice-derived artifact: the measured result of probing the retrieval path with low-privilege principals, the ACL reconciliation between source repositories and the index, and the outcome of the response-grounding rights re-check. Records which corpora were probed, which principals were used and every segment that was returned without an entitlement.
verifiability: self-asserted
chain: GDPR Art. 32 — Security of Processing → CO: Vector & Chunk-Level Access Control
Log records (4)
Machine-generated records produced while the system runs.
Event Logs & Decision Tracestext-derivedserves 17 obligations
The single highest-leverage artifact: hash-chained, WORM-stored logs with structured decision traces. Required capability fields per FprEN ISO/IEC 24970: input/output traces, execution timestamps, acting user/agent identity, referenced sources, human overrides. Audit-packet spec per event: model version, system-prompt/context hash, hyper-parameters (temperature, top-p), output payload, confidence score, active policy-ruleset versions, human override record. Simultaneously serves AI Act Art. 12, GDPR accountability, DORA incident reporting, NIS2 logging, PLD disclosure duties and its rebuttable defect presumption; financial-sector regimes push retention to 7 years (SEC 17a-4-class WORM rules). Credibility bar: anchor hash-chain heads externally (qualified timestamp / eIDAS ledger) so integrity survives an insider with admin rights.
verifiability: externally-anchored
chain: Art. 12 — Record-Keeping / Logging · CRA Art. 14 — Vulnerability & Severe-Incident Reporting · DORA Art. 19 — Major ICT-Incident Reporting · GDPR Art. 33/34 — Personal-Data Breach Notification · HIPAA Breach Notification Rule · NIS2 Art. 23 — Significant-Incident Reporting · +21 more
Guardrail Telemetry & Sanitization Recordspractice-derived — dispute welcomeserves 3 obligations
Control-level evidence for the OWASP mappings: guardrail trigger records, blocked-prompt statistics (LLM01), runtime output-sanitization logs (LLM05), groundedness-check outcomes — the empirical proof that declared controls actually execute.
verifiability: tamper-evident
chain: Art. 15 — Accuracy, Robustness, Cybersecurity · Art. 50 — Transparency Duties → CO: AI Interaction & Content Disclosure · Art. 15 — Accuracy, Robustness, Cybersecurity → CO: Runtime Injection Defense · Dynamic Deal Desk & Quoting Engine · Enterprise Marketing Disclosure Compliance · LLM01 Prompt Injection · +3 more
Immutable Decision Ledger (WORM)practice-derived — dispute welcomeserves 3 obligations
Per-execution audit packet: timestamp, model version, system prompt, input-context hash, hyper-parameters, output payload, confidence score and human override record.
verifiability: externally-anchored
chain: Art. 12 — Record-Keeping / Logging · Art. 12 — Record-Keeping / Logging → CO: Log Integrity & Non-Repudiation · IFRS / US GAAP Reporting Assurance · Algorithmic Portfolio Execution & Advisory · Automated Financial Forecasting & Audit Trails · Cross-Border Statutory Tax & Wealth Filing · +3 more
Drift-Gate Decision Logpractice-derived — dispute welcome
Practice-derived artifact: one record per deployment attempt — candidate version, measured drift statistics against the PCCP corridors, gate verdict, and where a rollout was blocked, the re-assessment it was routed to. This is what shows the corridor was honoured rather than merely documented.
verifiability: tamper-evident
chain: Art. 43 — Conformity Assessment → CO: Model Registry Gate & Drift Interlock
Process records (7)
Traces that a process actually happened, and who did it.
Human-Oversight Protocol & Intervention Recordspractice-derived — dispute welcomeserves 5 obligations
Art. 14 evidence: documented oversight design (gates, thresholds, veto powers), reviewer qualification, and the record of actual approvals, overrides and escalations — also the GDPR Art. 22 meaningful-human-involvement proof.
verifiability: documented artefact — verifiable on inspection
chain: Art. 11 — automated individual decision-making · Art. 14 — Human Oversight · GDPR Art. 22 — Automated Decisions · Art. 12 — Record-Keeping / Logging → CO: Log Access & Retention Governance · Art. 14 — Human Oversight → CO: Oversight Competence & Authority · Clinical Imaging Triage & Patient Follow-Up
Individual Explanation Letters & Counterfactual Recordspractice-derived — dispute welcomeserves 5 obligations
Practice-derived artifact: the issued adverse-decision explanations together with the attribution run, model version and counterfactual scenario that each letter rested on, so an authority or a court can check that the stated reasons are the reasons the system actually used.
verifiability: self-asserted
chain: Art. 11 — automated individual decision-making · Art. 18 — Obligation to assess the creditworthiness of the consumer · Art. 21 — examination of an application · Art. 86 — Right to explanation of individual decision-making · GDPR Art. 22 — Automated Decisions
Vendor & Model Due-Diligence Recordspractice-derived — dispute welcomeserves 4 obligations
DORA Art. 30 / AI Act deployer evidence: scored vendor assessments (jurisdiction, ZDR, BYOK, C5/AIC4/42001 evidence, tenant isolation), contract register, exit strategies for critical third parties.
verifiability: documented artefact — verifiable on inspection
chain: Art. 26 — Deployer Obligations · Art. 26 — Deployer Obligations → CO: Embedded-AI Vendor Governance · DORA · HIPAA (US Health Privacy) · Procurement Variance & Vendor KPI Monitoring
AI Literacy Training Recordspractice-derived — dispute welcomeserves 2 obligations
Art. 4 evidence: role-based training curricula and completion records for staff dealing with AI systems — the one obligation that applies at every risk level.
verifiability: documented artefact — verifiable on inspection
chain: Art. 4 — AI Literacy · Art. 14 — Human Oversight → CO: Oversight Competence & Authority
Personal-Data Breach Notification Recordtext-derivedserves 2 obligations
The GDPR Art. 33(5) record of every personal-data breach: facts, effects, remedial action, plus the notification sent to the supervisory authority and, where required, the data subjects.
verifiability: tamper-evident
chain: GDPR Art. 33/34 — Personal-Data Breach Notification · HIPAA Breach Notification Rule
Human Oversight Operating Standard (SOP)practice-derived — dispute welcome
Binding procedure defining supervisor roles, competence, review-queue handling, override authority and escalation thresholds θ per workflow.
verifiability: tamper-evident
chain: Art. 14 — Human Oversight → CO: Oversight Competence & Authority · KYC & Client Onboarding Automation (Managed Service) · Legal Contract & Regulatory Clause Extraction · Omnichannel Virtual Support & Voice Bots · Regulatory Change Management & Policy Updating
Risk Management File / Live Registertext-derived
Art. 9 continuous risk-management record: identified risks, quantified values (FAIR-AIR/NIST Map-Measure), mitigations, residual-risk acceptance, testing incl. misuse scenarios.
verifiability: documented artefact — verifiable on inspection
chain: Art. 9 — Risk Management · Algorithmic Portfolio Execution & Advisory · Supplier Master Data & ESG Risk Screening
Registry entries (2)
An entry in a register — internal inventory or public registry.
Serious-Incident Registerpractice-derived — dispute welcomeserves 5 obligations
One register carrying every reportable incident with its detection time, classification and the clocks it started, so overlapping AI Act, NIS2, DORA, CRA and sectoral reports run from the same recorded facts.
verifiability: tamper-evident
chain: Art. 72/73 — Post-Market Monitoring & Incidents · CRA Art. 14 — Vulnerability & Severe-Incident Reporting · DORA Art. 19 — Major ICT-Incident Reporting · NIS2 Art. 23 — Significant-Incident Reporting · SEC Form 8-K Item 1.05 — Material Cybersecurity Incident
AI System Inventory / Registry Entrypractice-derived — dispute welcomeserves 3 obligations
The organizational register of AI systems in use — role (provider or deployer), classification, owner, vendor and lifecycle state — from which per-system obligations are assigned.
verifiability: self-asserted
chain: Art. 17 — Quality Management System · Art. 26 — Deployer Obligations · Art. 26 — Deployer Obligations → CO: Embedded-AI Vendor Governance
Architecture Blueprint
Glass-Box EBM with Monotonic Constraints
Intrinsically interpretable scoring: an explainable boosting machine / generalised additive model whose per-feature shape functions ARE the model, exported as exact lookup tables so an adverse-action reason is read off the model rather than approximated after the fact. Monotonicity is enforced per feature at fit time — higher utilisation can never improve a score — which makes the direction of every reason contractual instead of empirical, and makes a remediation instruction ('reduce utilisation') actually true of the model. Interaction terms are capped and enumerated so the score decomposition sums exactly.
Required Technical Components (33)
Live Risk Register / Posture Management
Continuously updated risk register wired to runtime posture: threat-model deltas, open defects, control status, exposure per system. Includes Shadow-AI discovery — continuous scanning for unsanctioned agents, MCP servers and AI API usage outside the register; an unregistered agent is an unmanaged Art. 12/26 liability and the empirical driver of proportionate (not blanket) controls.
from: Art. 9 · GDPR Art. 35
Watchdog Supervisor & Rate Limiting
Cost/iteration caps, loop detection, anomaly-triggered mandatory approval (CodeBuddy 'suspicious command override').
from: Art. 9 · Art. 15
Deterministic Policy Engine (OPA / Cedar)
Policy-as-code decision point (PDP) with enforcement points (PEP) in front of every tool call: versioned policies in Git, microsecond evaluation, typed action schemas — authorization decided outside the model's reasoning space, never in the prompt.
from: Art. 9
Guardian Agents (Runtime Policy Enforcement)
Autonomous supervisory agents outside the supervised agent's reasoning loop: stateful threat engines with graph-based cross-session history (catch multi-turn injection, gradual exfiltration, incremental privilege escalation), event-driven exposure visibility (permission drift, new connectors), and contextual risk correlation into unified issues — interception before execution, not post-hoc logging.
from: Art. 9 · Art. 72/73
Bias Testing & Data Quality Pipeline
Representativeness checks, bias metrics and mitigation per ISO/IEC 5259; versioned datasets with lineage.
from: Art. 10 · Glass-Box EBM with Monotonic Constraints
Data Lineage & Versioning
Provenance tracking of datasets, features and embeddings; write-time attribution (source, actor, timestamp, confidence).
from: Art. 10 · Glass-Box EBM with Monotonic Constraints
PII Scrubbing / DLP-NER Layer
Automated detection, pseudonymisation and blocking of personal data in inputs, retrievals and outputs.
from: Art. 10 · GDPR Art. 25 · PIPEDA (CA)
Retrieval Rails (ACL-aware RAG)
Relevance, freshness and per-user permission checks on every retrieved chunk; curated, versioned index.
from: Art. 10 · GDPR Art. 32
Sovereign Context Layer
Governed runtime workspace operationalizing Art. 10: traceable lineage for every RAG chunk and training record at execution time, canonical version-controlled business glossary (documents Art. 10(2)(d) baseline assumptions), and continuous data-quality monitoring with threshold alerts and logged remediation for the Art. 10(3) 'error-free and complete' standard.
from: Art. 10
Inline PII/PHI Tokenisation
Personal and health data are detected and replaced with reversible cryptographic tokens before the payload leaves the isolation layer; re-identification happens only inside the tenant boundary.
from: Art. 10
AI Register & Model Registry / Factsheets
AI register & model registry: central inventory of every model, agent, RAG pipeline and embedded third-party SaaS AI across the estate, with factsheets per asset. v2.0 duty: every application — internal, open-source or procured — continuously publishes a machine-readable AI-BOM and Factsheet into the register; an asset without a current AI-BOM is an inventory gap, not a formality. Feeds Colorado AIA/ LL144 disclosure duties and the Art. 11 technical file; the enforcement backstop is Shadow-AI discovery on the risk register.
from: Art. 11 · Art. 13
WORM / Immutable Audit Vault
Append-only, hash-chained audit vault (WORM object-lock storage, AES-256 at rest, TLS 1.3 in transit). Guarantees tamper-evidence within the organization's trust domain — which stops your own team, but not an admin who can rebuild the vault. Pair with an external trust anchor and key ceremonies outside the operating team for evidence that holds against the insider scenario.
from: Art. 12 · Art. 26 · Glass-Box EBM with Monotonic Constraints
OpenTelemetry / FCoT Tracing
Hierarchical trace spans for every sub-task, prompt, retrieved document and API call — the reconstructible decision path for Art. 12/14 and PLD disclosure.
from: Art. 12
External Trust Anchor (Qualified Timestamp / Ledger)
Takes integrity proofs out of the operator's trust domain: periodic anchoring of log hash-chain heads via qualified electronic timestamps or a (qualified) electronic ledger per eIDAS 2, with signing keys held outside the operating team (key ceremony, HSM, separation of duties). Answers the insider test — a party who controls the vault cannot rewrite history without the anchor exposing it. Cost profile: anchoring is periodic and cheap; it upgrades every downstream log-based artifact at once.
from: Art. 12
Explainability API (SHAP/LIME/CoT)
Feature attributions for classical ML, reasoning-trace summaries for GenAI — feeds the human reviewer and the technical file.
from: Art. 13 · Art. 14 · Art. 86 · Glass-Box EBM with Monotonic Constraints
Adverse-Decision Reason Generator
Practice-derived component: converts feature attributions (SHAP or an equivalent attribution method) into an individually understandable, legally defensible explanation of an adverse decision — the role the AI system played, the main elements the decision rested on, and counterfactual scenarios stating what would have had to differ for a different outcome. Reason codes are generated from the decisioning path, not from a marketing template, and every issued letter is retained with the model version and the attribution run behind it. Honesty condition: a reason is only usable if acting on it would actually change the outcome, which non-monotonic feature interactions can break (see the post-hoc instability threat).
from: Art. 13 · Art. 86 · GDPR Art. 22 · Glass-Box EBM with Monotonic Constraints
Visual Explainability for Clinical Review
Practice-derived component: saliency, heatmap or region-proposal overlays rendered on the study itself, so the reviewing clinician can check the anatomical plausibility of the finding — whether the model looked where the pathology is — instead of accepting a score. Overlay stability across reconstructions is monitored, because an unstable overlay is a false assurance rather than an explanation.
from: Art. 13 · Art. 14
HITL Escalation Queue & Review UI
HITL escalation queue & review UI ('Human-as-a-Tool': the agent calls the human like any other tool via propose-action objects). Confidence- and risk-threshold routing, SLA timers, structured accept/modify/reject verdicts with digital reviewer signature at gate release — each verdict is itself Art. 14 evidence and feeds the active-learning loop.
from: Art. 14 · Art. 26 · Art. 86 · GDPR Art. 22
Kill Switch / Graceful Degradation
Operator stop controls and degraded-mode fallbacks; real-time override (veto) channels for HOTL operation.
from: Art. 14
Trust & Risk Dual Scoring
Escalation triggers built from two independent signals, because raw model confidence is uncalibrated: calibrated trust scores (prompt relevance, similarity to historic successes, cross-model consistency) plus deterministic risk scores (sensitive categories, transaction value, protected data) — either crossing its threshold forces human review.
from: Art. 14
Confidence-Threshold HITL Routing
Every output carries a confidence score C_s. C_s ≥ θ commits to the immutable ledger and downstream systems; C_s < θ pauses the transaction and routes the payload to a specialist review queue, whose verdict is logged as part of the decision record.
from: Art. 14
Confidence Scoring & Threshold Gate
Computes a probabilistic confidence score for every output and holds the transaction when the score falls below the workflow's regulatory threshold.
from: Art. 14
Input Rails / Prompt Shields
Pre-model validation of user input: injection detection, topic blocking, encoding checks.
from: Art. 15
Output Rails / Groundedness Check
Faithfulness scoring of answers against retrieved sources; deterministic fallback instead of hallucination; schema-validated structured output.
from: Art. 15
Confidential Computing Enclaves
AMD SEV / Intel TDX: data protected from the cloud operator even in memory during inference.
from: Art. 15
Model Abstraction & Graceful Fallback
Application logic addresses capabilities, not providers; the router degrades to a secondary or local model on error-rate or latency breach instead of failing the workflow.
from: Art. 15
AI Intake Portal & Use-Case Triage
The operational front door of the translational pipeline: structured intake profile (business objective, autonomy degree, data sensitivity, deployment context, target users) → automated tier proposal (detectors + evaluator pipeline) → risk-proportionate approval workflow → register entry with AI-BOM stub. Prevents both over-engineering (blanket high-tier controls breed Shadow AI) and under-engineering (unassessed high-risk deployment). Every governance framework assumes it; almost no failed audit had one.
from: Art. 17
Unified Incident-Response Runbook
One procedure reconciling AI Act Art. 73, GDPR Art. 33 (72h), DORA and NIS2 (24h/72h) timelines and recipients.
from: Art. 72/73 · GDPR Art. 33/34
Bitemporal Memory (GDPR×Art.12)
valid_from/valid_to + transaction time on every record: GDPR erasure removes data from the active retrieval path while the HMAC-chained immutable log survives for Art. 12 / PLD defence; tenant-scoped partitions allow physical scrub of PII.
from: GDPR Art. 17
Per-Tenant Retrieval Segmentation
Retrieval is scoped by tenant and by caller entitlement at query time, preventing cross-client and cross-role leakage through shared indexes.
from: GDPR Art. 25
Segmented Vector Store (RBAC + CMEK)
Vector indexes, embeddings and document stores are logically and physically partitioned per client, with role-based access and customer-managed encryption keys.
from: GDPR Art. 25 · GDPR Art. 32
PII/PHI Redaction & Tokenisation Engine
The engine behind inline tokenisation: pre-model interception that replaces identifiers with reversible tokens before a payload leaves the isolation boundary, plus a detokenisation gate that re-identifies only for authorised callers inside the boundary and logs every re-identification. Complements the DLP/NER scrubbing layer, which blocks or masks rather than preserving reversible reference.
from: GDPR Art. 25
DICOM De-Identification Pipeline
Practice-derived component: removal and replacement of identifying attributes in imaging studies before they leave the clinical system — header attributes per the DICOM confidentiality profiles, burned-in pixel text detected and masked, private tags dropped rather than trusted, and a consistent pseudonym per patient so longitudinal studies stay linkable without re-identifying anyone. Re-identification risk on the de-identified corpus is measured, not assumed.
from: GDPR Art. 25 · GDPR Art. 9
Build or Buy — Vendor Layer (13)
The graph models vendor CATEGORIES as first-class nodes and keeps named vendors as community-maintained, disputable desc content with lastVerified dates. A category is stable; a vendor list is a currency-layer object like any standard node.
AI GRC & Governance Platforms
Second-line systems of record: model/agent inventory incl. third-party SaaS AI, automated risk tiering, policy administration, cross-framework mapping and control deduplication, audit-evidence generation, intake workflows. Named products live in marketExamples, which is the single source of truth for this layer — prose here describes the class, not the field. What the class buys you: one register a second line can defend, and evidence assembled once and reused across frameworks. Selection metrics: see meta.marketLandscape.selectionMetrics.grc. One compilation-reported item is deliberately kept as unverified: a claimed updated US banking model-risk guidance 'SR 26-2'. Two secondary compilations repeating it is corroboration of the rumour, not of the guidance; it stays flagged pending verification against Federal Reserve primary sources, and a curator verification proposal is filed. All alignments in this layer are vendor-positioned claims, never certifications.
unverified · verified 2026-08-18 community-maintained
selection metrics: multi-model/multi-cloud cataloging incl. third-party SaaS, automated risk tiering, regulatory reporting, independent-2nd-line deployability, cross-framework control deduplication
supplies: Live Risk Register / Posture Management · AI Register & Model Registry / Factsheets · Adverse-Decision Reason Generator · AI Intake Portal & Use-Case Triage · Vendor & Model Due-Diligence Kit
Filters to self-hostable, customer-VPC and open-source options when personal or confidential data cannot leave the EU.
| Example | Sub-category | What it does | Hosting | Claimed alignments |
|---|---|---|---|---|
| Credo AI | AI governance platform | Policy packs, risk tiering and evidence workflows mapped across frameworks. Typical: AI registry, policy administration. Scope overlap: Its scope overlaps this platform's own; we have a commercial interest in the comparison. | not checked | ISO 42001 alignment (claimed)EU AI Act readiness positioning |
| Holistic AI | AI governance & audit | Risk assessment, bias auditing and regulatory reporting workflows. Typical: bias audit, regulatory reporting. Scope overlap: Its scope overlaps this platform's own; we have a commercial interest in the comparison. | not checked | NYC LL144 audit support (claimed)EU AI Act readiness positioning |
| IBM watsonx.governance | AI governance platform | Governance, factsheets and monitoring integrated with the IBM stack. Typical: factsheets, model monitoring. Scope overlap: Its scope overlaps this platform's own; we have a commercial interest in the comparison. | not checked | ISO 42001 alignment (claimed)Art. 11 documentation support (claimed) |
| ModelOp | AI/model governance | Model and agent inventory with automated lifecycle controls for large estates. Typical: model inventory, control automation. Scope overlap: Its scope overlaps this platform's own; we have a commercial interest in the comparison. | not checked | model-risk positioning (SR 11-7 style, claimed)ISO 42001 alignment (claimed) |
and 3 more in the stack advisor →
Community-maintained, disputable examples — not an endorsement and not a ranking. Alignments are as claimed by vendors or the source compilation, not verified by RAIN; a certification is shown as a certification only where a certificate or registry reference is recorded.
Disclosure: RAI·N·avigator operates in this category too, so we have a commercial interest in any comparison here. That is why this layer maps product classes to control objectives and lists named products as community-maintained examples — we publish no rankings, no quadrants and no coverage assertions about any vendor, including ourselves.
Runtime Guardrails & Enforcement
Policy enforcement in the request path: input/output validation, injection and exfiltration defence, structured-output constraints and action blocking. Distinct from observability layers because these products are in-line and can refuse. Selection questions: added latency at p95, whether enforcement is fail-open or fail-closed, whether policies are versioned artefacts, and whether the layer can be self-hosted inside your data boundary.
unverified · verified 2026-08-18 community-maintained
selection metrics: Where enforcement sits (inline proxy, sidecar, SDK) and the added latency at your token volumes; whether policy is versioned and testable as code; fail-open vs. fail-closed behaviour under guardrail outage; language and modality coverage; whether every block writes an evidence record you can cite later.
supplies: Deterministic Policy Engine (OPA / Cedar) · Input Rails / Prompt Shields · Output Rails / Groundedness Check · Tool-Use Boundary Proxy
Filters to self-hostable, customer-VPC and open-source options when personal or confidential data cannot leave the EU.
| Example | Sub-category | What it does | Hosting | Claimed alignments |
|---|---|---|---|---|
| Guardrails AI | validation framework | Open-source validator framework for structured output and content policies in the request path. Typical: output validation, structured output. | open source | supports Art. 15 robustness measures (claimed) |
| NVIDIA NeMo Guardrails | dialogue policy rails | Programmable dialogue and topic rails placed around an LLM application. Typical: topic control, dialogue policy. | open source | supports Art. 50 interaction disclosure patterns (claimed) |
| Lakera AI | guardrail proxy | Inline prompt-injection and content detection at request time. Typical: injection defence, content filtering. | SaaS (vendor cloud) | SOC 2 (claimed)supports Art. 15 robustness measures (claimed) |
| Credal AI | enterprise access & policy layer | Permission-aware access layer with data-loss controls in front of enterprise assistants. Typical: access control, DLP. | SaaS (vendor cloud) | SOC 2 (claimed) |
Community-maintained, disputable examples — not an endorsement and not a ranking. Alignments are as claimed by vendors or the source compilation, not verified by RAIN; a certification is shown as a certification only where a certificate or registry reference is recorded.
Disclosure: RAI·N·avigator operates in this category too, so we have a commercial interest in any comparison here. That is why this layer maps product classes to control objectives and lists named products as community-maintained examples — we publish no rankings, no quadrants and no coverage assertions about any vendor, including ourselves.
Agent Observability & Model Risk Management
Tracing, evaluation, drift monitoring and model-validation records. This layer is where Art. 12 record-keeping becomes technically real (step-level traces, prompt/response records, retention control) and where model-risk practice in the SR 11-7 tradition — validation evidence, performance and drift monitoring, challenger comparison — is operated. Gateways and tracing tools produce the logs; the retention, integrity and access regime around them is still yours.
unverified · verified 2026-08-18 community-maintained
selection metrics: Trace completeness per agent step; log retention and immutability options; drift/quality metrics available out of the box; evaluation dataset support; export into your audit vault; self-host option.
supplies: Bias Testing & Data Quality Pipeline · AI Register & Model Registry / Factsheets · OpenTelemetry / FCoT Tracing · Explainability API (SHAP/LIME/CoT) · Adverse-Decision Reason Generator · Visual Explainability for Clinical Review · Confidence Scoring & Threshold Gate · Model Drift & Accuracy Monitor
Filters to self-hostable, customer-VPC and open-source options when personal or confidential data cannot leave the EU.
| Example | Sub-category | What it does | Hosting | Claimed alignments |
|---|---|---|---|---|
| LangSmith | agent tracing & evaluation | Trace capture and evaluation over LangChain/LangGraph runs with dataset-based scoring. Typical: step tracing, regression evaluation. | not checked | SOC 2 (claimed)supports Art. 12 record-keeping (claimed) |
| Langfuse | agent tracing & evaluation | Open-source tracing, prompt management and evaluation; self-hostable for retention control. Typical: self-hosted tracing, cost/latency analytics. | open source | GDPR-positionedsupports Art. 12 record-keeping (claimed) |
| Arize AI / Phoenix | ML & LLM observability | Production monitoring with drift and performance analysis; Phoenix is the open-source tracing side. Typical: drift monitoring, production analytics. | not checked | SOC 2 (claimed)drift-monitoring positioning (SR 11-7 style, claimed) |
| Helicone | LLM gateway & logging | Proxy-level logging of prompts, costs and latency across providers. Typical: gateway logging, cost control. | not checked | SOC 2 (claimed)supports Art. 12 record-keeping (claimed) |
and 11 more in the stack advisor →
Community-maintained, disputable examples — not an endorsement and not a ranking. Alignments are as claimed by vendors or the source compilation, not verified by RAIN; a certification is shown as a certification only where a certificate or registry reference is recorded.
Disclosure: RAI·N·avigator operates in this category too, so we have a commercial interest in any comparison here. That is why this layer maps product classes to control objectives and lists named products as community-maintained examples — we publish no rankings, no quadrants and no coverage assertions about any vendor, including ourselves.
Secure Data Infrastructure & Vector Storage
Governed retrieval substrate: vector databases, lakehouses and catalogs with tenant/namespace isolation, RBAC and client-managed keys (CMEK), lineage into RAG chunks, air-gap options, and code-level data and AI lineage. Named products live in marketExamples; prose here describes the class. What the class buys you: retrieval that can be scoped per requester and traced back to a source record. The Art. 10 runtime data-governance duties land here. Selection metrics: see meta.marketLandscape.selectionMetrics.data.
unverified · verified 2026-08-18 community-maintained
selection metrics: namespace/tenant isolation, RBAC + CMEK, lineage into RAG chunks, SOC 2 / ISO 27001 attestations, air-gap capability
supplies: Data Lineage & Versioning · Retrieval Rails (ACL-aware RAG) · Sovereign Context Layer · Agent Memory Record Store
Filters to self-hostable, customer-VPC and open-source options when personal or confidential data cannot leave the EU.
| Example | Sub-category | What it does | Hosting | Claimed alignments |
|---|---|---|---|---|
| Azure AI Search | managed retrieval | Managed hybrid search with security trimming against tenant identities. Typical: ACL-aware RAG, enterprise search. | not checked | ISO 27001 (claimed)SOC 2 (claimed) |
| Databricks Unity Catalog | governed lakehouse | Catalog and lineage spanning tables, features and RAG chunks. Typical: lineage evidence, governed RAG. | not checked | SOC 2 (claimed)lineage/Art. 10 support (claimed) |
| Relyance AI | code-level data & AI lineage | Parses source repositories to map data and inference flows at code level, with CI checks on changes to those flows. Typical: data lineage, shift-left privacy review. Scope overlap: Its AI-governance reporting scope overlaps this platform's own; we have a commercial interest in the comparison. | SaaS (vendor cloud) | GDPR programme tooling (claimed)EU AI Act readiness positioning |
| Snowflake Cortex | governed lakehouse | Model calls inside the warehouse boundary with masking and clean rooms. Typical: in-warehouse inference, governed analytics. | not checked | SOC 2 (claimed)ISO 27001 (claimed)HIPAA-eligible (claimed) |
Community-maintained, disputable examples — not an endorsement and not a ranking. Alignments are as claimed by vendors or the source compilation, not verified by RAIN; a certification is shown as a certification only where a certificate or registry reference is recorded.
Disclosure: RAI·N·avigator operates in this category too, so we have a commercial interest in any comparison here. That is why this layer maps product classes to control objectives and lists named products as community-maintained examples — we publish no rankings, no quadrants and no coverage assertions about any vendor, including ourselves.
Grounding, Retrieval & Agent Memory
The grounding layer between raw sources and the model: document parsers, embedding models, vector databases and — new in the agentic era — persistent agent memory stores. Memory is the hard part: once a personal fact is embedded, GDPR Art. 17 erasure has to reach the vector and the memory record, not just the source row, and embeddings are partially reconstructable (see IronCore in the privacy layer). Retrieval quality is also a data-governance question under Art. 10: what got parsed, chunked and indexed is what the system 'knows'.
unverified · verified 2026-08-18 community-maintained
selection metrics: Parsing fidelity on your worst document class; retrieval precision/recall on a labelled set; tenant and ACL isolation model; per-vector encryption and erasure path; memory TTL and record semantics; self-host option.
supplies: Data Lineage & Versioning · Retrieval Rails (ACL-aware RAG) · Bitemporal Memory (GDPR×Art.12) · Per-Tenant Retrieval Segmentation · Segmented Vector Store (RBAC + CMEK) · Agent Memory Record Store
Filters to self-hostable, customer-VPC and open-source options when personal or confidential data cannot leave the EU.
| Example | Sub-category | What it does | Hosting | Claimed alignments |
|---|---|---|---|---|
| Docling | document parser | Open-source layout-aware parsing of PDFs and office formats into structured chunks. Typical: RAG ingestion, air-gapped pipelines. | self-hostable | EU sovereignty positioning |
| LlamaParse | document parser | Managed parsing service tuned for tables and complex documents feeding RAG. Typical: RAG ingestion, table extraction. | not checked | SOC 2 (claimed) |
| Amazon Textract | document parser | OCR and form/table extraction with per-page pricing inside AWS. Typical: document intake, claims processing. | not checked | SOC 2 (claimed)HIPAA-eligible (claimed)ISO 27001 (claimed) |
| Diffbot | web/knowledge extraction | Structured extraction and knowledge-graph construction from web sources. Typical: market monitoring, entity resolution. | not checked | vendor-stated security posture |
and 12 more in the stack advisor →
Community-maintained, disputable examples — not an endorsement and not a ranking. Alignments are as claimed by vendors or the source compilation, not verified by RAIN; a certification is shown as a certification only where a certificate or registry reference is recorded.
Disclosure: RAI·N·avigator operates in this category too, so we have a commercial interest in any comparison here. That is why this layer maps product classes to control objectives and lists named products as community-maintained examples — we publish no rankings, no quadrants and no coverage assertions about any vendor, including ourselves.
Sovereign Infrastructure
Compute and storage under EU jurisdictional control. Two structurally different offers, and the difference is the decision: native EU providers give full jurisdictional isolation with narrower service catalogs and thinner managed-AI tooling; hyperscaler sovereign constructions give the broad catalog with contractual and operational isolation, where the residual question is the control plane, support access and operational metadata rather than the data plane. Named offers live in marketExamples, which is the single source of truth for this layer — prose here describes the class, not the field. Claimed alignments recorded per entry: positioning for BSI C5 / C3A and ANSSI SecNumCloud attestation, NIS2 and DORA third-party requirements. Nothing here is an endorsement, and no provider in this category is 'CLOUD-Act-proof' by label alone — ask who holds the keys and who administers the plane.
unverified · verified 2026-08-18 community-maintained
selection metrics: jurisdiction of the control plane (not only the data plane), operator nationality and support-access paths, key custody, C5 / C3A / SecNumCloud attestation scope, managed-AI service depth vs. isolation trade-off, exit and repatriation terms
supplies: Sovereign Context Layer
Filters to self-hostable, customer-VPC and open-source options when personal or confidential data cannot leave the EU.
| Example | Sub-category | What it does | Hosting | Claimed alignments |
|---|---|---|---|---|
| OVHcloud | native EU | French provider with EU-only jurisdiction and a narrower managed-AI catalog than the hyperscalers. Typical: EU-resident inference, regulated workload hosting. | not checked | ISO 27001 (claimed)SecNumCloud-positionedGDPR-positioned |
| Scaleway | native EU | EU-operated cloud with GPU instances and managed inference under French corporate control. Typical: EU-resident inference, fine-tuning. | not checked | ISO 27001 (claimed)GDPR-positioned |
| STACKIT | native EU | German provider (Schwarz Group) positioned for data residency in Germany. Typical: public sector, retail data platforms. | not checked | C5-positionedGDPR-positioned |
| AWS European Sovereign Cloud | sovereign hyperscaler | Separately operated EU region set with EU-resident personnel and keys; full hyperscaler catalog. Typical: large-scale enterprise AI, regulated hosting. | not checked | ISO 27001 (claimed)SOC 2 (claimed)EU data-boundary positioning |
and 11 more in the stack advisor →
Community-maintained, disputable examples — not an endorsement and not a ranking. Alignments are as claimed by vendors or the source compilation, not verified by RAIN; a certification is shown as a certification only where a certificate or registry reference is recorded.
Disclosure: RAI·N·avigator operates in this category too, so we have a commercial interest in any comparison here. That is why this layer maps product classes to control objectives and lists named products as community-maintained examples — we publish no rankings, no quadrants and no coverage assertions about any vendor, including ourselves.
Public Transparency Registers & System Cards
Authoring and publishing the outward-facing record: public AI registers, system and model cards, conformity declarations and plain-language notices, with versioning so a published statement can be tied to the system version it described. The register content is produced elsewhere; this class is the publication and version-control surface for it. Selection metrics: see meta.marketLandscape.selectionMetrics.transparency.
unverified · verified 2026-08-17 community-maintained
selection metrics: Versioning of published statements against the system version they describe; whether a card is generated from your governance record or re-authored by hand; language coverage and accessibility of the published surface; export and self-hosting of the public register; whether unpublishing leaves an auditable trail.
supplies: AI Register & Model Registry / Factsheets
Filters to self-hostable, customer-VPC and open-source options when personal or confidential data cannot leave the EU.
| Example | Sub-category | What it does | Hosting | Claimed alignments |
|---|---|---|---|---|
| Saidot | public AI register | AI register with published system cards and regulation-mapped documentation workflows. Typical: public AI register, system cards. Scope overlap: Its documentation and register scope overlaps this platform's own; we have a commercial interest in the comparison. | SaaS (vendor cloud) | EU AI Act documentation positioningISO 42001 alignment (claimed) |
Community-maintained, disputable examples — not an endorsement and not a ranking. Alignments are as claimed by vendors or the source compilation, not verified by RAIN; a certification is shown as a certification only where a certificate or registry reference is recorded.
Disclosure: RAI·N·avigator operates in this category too, so we have a commercial interest in any comparison here. That is why this layer maps product classes to control objectives and lists named products as community-maintained examples — we publish no rankings, no quadrants and no coverage assertions about any vendor, including ourselves.
Cryptographic Evidence & Audit Ledger
Tamper-evident recording of what a system did: content-addressed decision records, hash chains and external anchoring, so a log can be shown not to have been rewritten after the fact. This is the layer that turns Art. 12 logging and Art. 19 retention from a storage question into an evidentiary one. AI Verify is carried in RAIN as a STANDARD node (sg-ai-verify), not duplicated here as a vendor.
unverified · verified 2026-08-18 community-maintained
selection metrics: Append-only guarantees and who can rotate or delete (including the vendor); anchoring mechanism (qualified timestamp, transparency log, notarisation) and whether verification works without the vendor; retention and export in a readable format at end of contract; throughput and cost at your event volume.
supplies: WORM / Immutable Audit Vault · External Trust Anchor (Qualified Timestamp / Ledger)
Filters to self-hostable, customer-VPC and open-source options when personal or confidential data cannot leave the EU.
| Example | Sub-category | What it does | Hosting | Claimed alignments |
|---|---|---|---|---|
| Fact0 | cryptographic evidence ledger | Positions itself as a tamper-evident ledger for AI decision records. Typical: decision records, audit trail. | not checked | supports Art. 12 record-keeping (claimed) |
| Traccia | audit trail & traceability | Positions itself around traceability of AI pipeline steps and artefacts. Typical: traceability, artifact lineage. | not checked | supports Art. 12 record-keeping (claimed) |
Community-maintained, disputable examples — not an endorsement and not a ranking. Alignments are as claimed by vendors or the source compilation, not verified by RAIN; a certification is shown as a certification only where a certificate or registry reference is recorded.
Disclosure: RAI·N·avigator operates in this category too, so we have a commercial interest in any comparison here. That is why this layer maps product classes to control objectives and lists named products as community-maintained examples — we publish no rankings, no quadrants and no coverage assertions about any vendor, including ourselves.
Agent Orchestration & SDLC Toolkits
Developer middleware for multi-agent networks, tool-use chains, RAG abstraction, state and memory persistence, and model routing. Named products live in marketExamples; prose here describes the class. Regulatory posture: orchestration code is where autonomy tiering, propose-action objects and fallback routing get implemented — the framework choice constrains which controls are cheap and which are retrofits. Selection metrics: see meta.marketLandscape.selectionMetrics.orchestration.
unverified · verified 2026-08-18 community-maintained
selection metrics: broad model-API abstraction, state/memory management, error recovery, fallback routing hooks
supplies: HITL Escalation Queue & Review UI
Filters to self-hostable, customer-VPC and open-source options when personal or confidential data cannot leave the EU.
| Example | Sub-category | What it does | Hosting | Claimed alignments |
|---|---|---|---|---|
| LangChain / LangGraph | agent framework | Graph-structured agent runtime; interrupt/pause nodes support implementing human approval at defined steps. Typical: multi-step agents, approval workflows. | not checked | supports implementing Art. 14 oversight (claimed)supports Art. 12 step logging (claimed) |
| LlamaIndex | RAG framework | Indexing and query abstractions over documents and structured sources. Typical: enterprise RAG, document agents. | open source | retrieval-governance positioning |
| Microsoft AutoGen | multi-agent framework | Conversational multi-agent patterns with pluggable tool executors. Typical: multi-agent research, code agents. | not checked | research/OSS, no vendor certification |
| CrewAI | multi-agent framework | Role-based agent teams with task delegation and process templates. Typical: process automation, role-based agents. | not checked | vendor-stated security posture |
and 6 more in the stack advisor →
Community-maintained, disputable examples — not an endorsement and not a ranking. Alignments are as claimed by vendors or the source compilation, not verified by RAIN; a certification is shown as a certification only where a certificate or registry reference is recorded.
Disclosure: RAI·N·avigator operates in this category too, so we have a commercial interest in any comparison here. That is why this layer maps product classes to control objectives and lists named products as community-maintained examples — we publish no rankings, no quadrants and no coverage assertions about any vendor, including ourselves.
Runtime Security & Guardrail Vendors
First-line inline enforcement: single-pass parallel input/output evaluation proxies, injection and exfiltration defense, PII masking, grounding checks, SecOps routing. Named products live in marketExamples; prose here describes the class. What the class buys you: a policy decision point in the request path that fails closed and emits telemetry an auditor can read. Selection metrics: single-pass latency (<20 ms class), catch rates, policy-version telemetry into the AI-BOM. Consolidation matters commercially: a guardrail acquired by a platform vendor tends to follow that platform's roadmap, which is a lock-in question rather than a security one — reported acquisitions are recorded per entry as reported, not asserted here.
unverified · verified 2026-08-18 community-maintained
selection metrics: single-pass parallel evaluation latency (<20 ms class), injection/hallucination catch rates, SecOps/SIEM routing, policy versioning surfaced into the AI-BOM
supplies: Input Rails / Prompt Shields · Output Rails / Groundedness Check · Guardrail Sidecar / Interception · Tool-Use Boundary Proxy
Filters to self-hostable, customer-VPC and open-source options when personal or confidential data cannot leave the EU.
| Example | Sub-category | What it does | Hosting | Claimed alignments |
|---|---|---|---|---|
| Lakera | guardrail proxy | Inline prompt-injection and content detection at request time. Typical: injection defence, content filtering. | not checked | SOC 2 (claimed)supports Art. 15 robustness measures (claimed) |
| HiddenLayer | model/agent detection & response | Model-layer detection and response with adversarial-attack telemetry. Typical: model threat detection, red-team telemetry. | not checked | SOC 2 (claimed)supports Art. 15 robustness measures (claimed) |
| Palo Alto Prisma AIRS | network-integrated AI security | AI runtime security folded into an existing enterprise network security estate. Typical: enterprise rollout, egress control. | not checked | SOC 2 (claimed)enterprise security integration (claimed) |
| Cisco AI Defense | network-integrated AI security | Discovery of AI usage plus inline enforcement across the corporate network. Typical: shadow-AI discovery, inline enforcement. | not checked | enterprise security integration (claimed) |
and 4 more in the stack advisor →
Community-maintained, disputable examples — not an endorsement and not a ranking. Alignments are as claimed by vendors or the source compilation, not verified by RAIN; a certification is shown as a certification only where a certificate or registry reference is recorded.
Disclosure: RAI·N·avigator operates in this category too, so we have a commercial interest in any comparison here. That is why this layer maps product classes to control objectives and lists named products as community-maintained examples — we publish no rankings, no quadrants and no coverage assertions about any vendor, including ourselves.
Confidential Computing & Privacy Engines
Data-in-use protection and pre-model privacy interception: enclave and runtime encryption, key management, tokenisation vaults, PII detection and redaction, application-layer and vector encryption. Named products live in marketExamples; prose here describes the class. Select on: enclave attestation support, key custody model (external HSM / BYOK), detokenisation audit trail, latency added per call, and coverage of the identifier classes your regime actually names. Selection metrics: see meta.marketLandscape.selectionMetrics.privacy.
unverified · verified 2026-08-18 community-maintained
selection metrics: enclave attestation support, key custody (external HSM / BYOK), detokenisation audit trail, added latency per call, coverage of the identifier classes your regime names, in-boundary deployment option
supplies: Confidential Computing Enclaves · PII/PHI Redaction & Tokenisation Engine · DICOM De-Identification Pipeline
Filters to self-hostable, customer-VPC and open-source options when personal or confidential data cannot leave the EU.
| Example | Sub-category | What it does | Hosting | Claimed alignments |
|---|---|---|---|---|
| Anjuna | confidential computing | Runs workloads inside hardware enclaves without application rewrites. Typical: data-in-use protection, regulated inference. | not checked | confidential-computing positioningDORA-positioned (claimed) |
| Fortanix | confidential computing & KMS | Enclave runtime plus key management and tokenisation services. Typical: key management, data-in-use protection. | not checked | FIPS 140-2 (claimed)DORA-positioned (claimed)HIPAA-positioned (claimed) |
| Skyflow | privacy vault | Polymorphic data vault de-identifying records before they reach a model. Typical: PII vaulting, pre-model redaction. | not checked | SOC 2 (claimed)HIPAA-positionedGDPR-positioned |
| Private AI | PII detection & redaction | Detection and redaction of identifiers across text, documents and audio. Typical: inline redaction, document de-identification. | not checked | GDPR-positionedHIPAA-positioned |
and 1 more in the stack advisor →
Community-maintained, disputable examples — not an endorsement and not a ranking. Alignments are as claimed by vendors or the source compilation, not verified by RAIN; a certification is shown as a certification only where a certificate or registry reference is recorded.
Disclosure: RAI·N·avigator operates in this category too, so we have a commercial interest in any comparison here. That is why this layer maps product classes to control objectives and lists named products as community-maintained examples — we publish no rankings, no quadrants and no coverage assertions about any vendor, including ourselves.
Agentic Execution Governance
The youngest tier: governance of what an agent is allowed to do at execution time — non-human identity, per-task scoping, action approval, agent inventory and agent-level red-teaming. Named products live in marketExamples; prose here describes the class. Because the category is new, capability claims outrun deployments: ask for a reference in your own regime before believing a control is covered, and treat entries with limited public verification as unconfirmed. Selection metrics: see meta.marketLandscape.selectionMetrics.agentgov.
unverified · verified 2026-08-18 community-maintained
selection metrics: non-human identity inventory completeness, credential time-to-live and revocation latency, per-action approval hooks, agent-level red-team coverage, evidence export a 2nd line can read, deployment references in your regime
supplies: Non-Human Identity Credential Broker · Agent Identity & Access (IdP)
Filters to self-hostable, customer-VPC and open-source options when personal or confidential data cannot leave the EU.
| Example | Sub-category | What it does | Hosting | Claimed alignments |
|---|---|---|---|---|
| Pillar Security | agent security & inventory | Discovery, inventory and runtime policy for agents in the estate. Typical: agent registry, policy enforcement. | not checked | agent-inventory positioning |
| Lyzr | agent governance & observability | Agent platform with governance, approval and observability features. Typical: agent approval, agent analytics. | not checked | vendor-stated security posture |
| Astrix Security | non-human identity | Lifecycle governance of machine and agent identities and their grants. Typical: credential scoping, NHI inventory. | not checked | SOC 2 (claimed)NHI governance positioning |
| Britive | just-in-time access | Ephemeral, per-task privileges instead of standing credentials. Typical: JIT credentials, privilege reduction. | not checked | SOC 2 (claimed)least-privilege positioning |
Community-maintained, disputable examples — not an endorsement and not a ranking. Alignments are as claimed by vendors or the source compilation, not verified by RAIN; a certification is shown as a certification only where a certificate or registry reference is recorded.
Disclosure: RAI·N·avigator operates in this category too, so we have a commercial interest in any comparison here. That is why this layer maps product classes to control objectives and lists named products as community-maintained examples — we publish no rankings, no quadrants and no coverage assertions about any vendor, including ourselves.
AI Asset Discovery & Shadow-AI Inventory
Automated discovery of AI in an existing estate: model and agent detection in source repositories, LLM API egress in cloud and network telemetry, AI features switched on inside employee SaaS, reconciliation against the CMDB and the AI register, ownership attribution and drift between declared and observed estate. Distinct from the GRC layer, which is the system of record for what is already known: this class finds the population that record is supposed to cover. Selection metrics: see meta.marketLandscape.selectionMetrics.discovery.
unverified · verified 2026-08-17 community-maintained
selection metrics: Coverage of the estate you actually have (repos, cloud accounts, SaaS tenants, network egress) rather than the connector count; false-positive rate on detected AI usage; whether findings reconcile into your existing register rather than a second inventory; ownership attribution quality; agent-based vs. agentless deployment; whether discovery data leaves your tenancy.
supplies: Shadow-AI Discovery & Asset Inventory Scanner
Filters to self-hostable, customer-VPC and open-source options when personal or confidential data cannot leave the EU.
| Example | Sub-category | What it does | Hosting | Claimed alignments |
|---|---|---|---|---|
| Truyo | shadow-AI discovery | Discovery of AI usage across SaaS and cloud accounts with intake and governance workflow on top. Typical: shadow-AI inventory, AI intake. Scope overlap: Its governance-workflow scope overlaps this platform's own; we have a commercial interest in the comparison. | SaaS (vendor cloud) | EU AI Act readiness positioningGDPR programme tooling (claimed) |
Community-maintained, disputable examples — not an endorsement and not a ranking. Alignments are as claimed by vendors or the source compilation, not verified by RAIN; a certification is shown as a certification only where a certificate or registry reference is recorded.
Disclosure: RAI·N·avigator operates in this category too, so we have a commercial interest in any comparison here. That is why this layer maps product classes to control objectives and lists named products as community-maintained examples — we publish no rankings, no quadrants and no coverage assertions about any vendor, including ourselves.
Procurement rule: Derived from three-lines-of-defense separation: the second-line GRC platform must be procured and deployed independently of any first-line runtime or model vendor — a governance tool that only sees its own vendor's models cannot govern a multi-model estate, and closed third-party SaaS AI can only be governed contractually (intake, attestation, AI-BOM disclosure), never by inline inspection.
Outsourced delivery BPO · SaaS · Service-as-a-Software caveats
Delivery Model — BPO · SaaS · Service-as-a-Software
Spectrum
BPO: input-priced (billable hours/FTEs), linear headcount scaling, human error & attrition as primary risk
SaaS: capability-priced (software access), client operates the workload, implementation/adoption failure as primary risk
Service-as-a-Software: outcome-priced (SLA on completed work), provider-managed AI executes 60–80% of cognitive tasks with specialist supervision, algorithmic bias & non-compliance as primary risk
Caveats in regulated markets
— Outcome SLAs move compliance risk onto the provider — but NOT the buyer's deployer duties: Art. 26 oversight, log retention and FRIA obligations stay with the enterprise even when execution is outsourced.
— Provider role analysis is the central legal question: a productized platform that fine-tunes, re-purposes or chains models can flip into the Art. 25 provider role with full high-risk obligations.
— Certified operations (ISO 42001) function as a procurement moat and shortcut third-party risk assessment — but organizational certificate ≠ product conformity (never conflate, see meta.assuranceEcosystem).
— The buyer's evidence chain must reach into the provider: contractually mandated AI-BOM disclosure, ZDR certificates, bias-audit reports and logging-ledger access are the artifacts that make an outsourced workflow auditable.
Threat Profile
No elevated threat is modelled for this use case yet.