Consumer products & IoT
Connected Consumer Product with Embedded AI (doorbell, monitor, toy)
Minimal RiskUnverifiedDiscuss / dispute
A Wi-Fi-connected consumer device — video doorbell, baby monitor, smart toy, wearable — runs on-device or cloud models for event detection, voice interaction or recognition, with a companion app and firmware updates from the manufacturer.
Consensus classification rationale: The regulated object is the product, not a decision about a person. As a product with digital elements the device falls under the Cyber Resilience Act (Art. 3(1)), whose vulnerability and incident reporting duties apply since 11 September 2026 and whose remaining manufacturer duties apply from 11 December 2027; as internet-connected radio equipment it must meet the RED essential requirements on network protection and privacy activated by Delegated Regulation (EU) 2022/30 since 1 August 2025; the GPSR applies residually, the Data Act's access-by-design duty applies to connected products placed on the market after 12 September 2026, and the revised Product Liability Directive treats software as a product. Video, audio and face data make GDPR Art. 25 (data protection by design) and, for products used by children, Art. 8 and the Toy Safety rules relevant; in the United States the FTC Act and — for services directed to children under 13 — the COPPA Rule apply. Under the AI Act the manufacturer is a provider (Art. 2(1)(a)); on-device event classification is not an Annex III use, so the system is minimal-risk, while a device that converses with users owes the Art. 50(1) disclosure.
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
Autonomyadvisory — Drives the human-oversight duties (Art. 14, Art. 26(2)) and Art. 50 disclosure.
Profiling of natural personsno — 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 chainprovider — Splits provider duties, deployer duties and upstream GPAI duties.
Consequential scoringno — 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
10 instruments across 5 of 7 regulatory domains, plus 7 standards references- AI law1 instrument
- Data protection2 instruments
- Cyber & resilience1 instrument
- Online safety & platformsnone triggered
- Product safety2 instruments
- Financial servicesnone triggered
- Sector & employment4 instruments
- Standards7 references
By jurisdiction
- EU8European UnionCyber Resilience Act, Data Act, EU AI Act, EU Toy Safety (Directive 2009/48/EC → Regulation (EU) 2025/2509), GDPR, General Product Safety Regulation, RED Cybersecurity Delegated Regulation (EU) 2022/30, Revised Product Liability Directive
- US2United States (federal)COPPA Rule (16 CFR Part 312), FTC Act §5 & Endorsement / AI-Claims Guidance
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 28 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: RED Cybersecurity Delegated Regulation (EU) 2022/30 carries no resolvable citation — the claim is uncited. open node →
- Source tier: FTC Act §5 & Endorsement / AI-Claims Guidance carries no resolvable citation — the claim is uncited. open node →
- 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: IEEE CertifAIEd™ carries no resolvable citation — the claim is uncited. open node →
- Source tier: prEN 18229-1 (Trustworthiness Framework, part 1) rests on a secondary source (tracker or summary), not on the primary text. open node → primary source →
- Source tier: ENISA Multilayer Framework & AI Threat Landscape carries no resolvable citation — the claim is uncited. open node →
- Source tier: NIST SP 800-218 (SSDF) carries no resolvable citation — the claim is uncited. open node →
- Status certainty: JTC 21 Technical Package (prEN 18228/18229/18281–83) is "draft", not settled in-force law. open node → primary source →
- Status certainty: prEN 18229-1 (Trustworthiness Framework, part 1) is "enquiry", not settled in-force law. open node → primary source →
- Verification age: IEEE CertifAIEd™ has no recorded verification date. open node →
- Verification age: ENISA Multilayer Framework & AI Threat Landscape has no recorded verification date. open node →
- Verification age: NIST SP 800-218 (SSDF) has no recorded verification date. open node →
Compliance brief
This use case is minimal-risk under the EU AI Act (Minimal Risk); no product-specific obligations beyond general AI literacy apply.
What is owed
- Art. 4. Providers and deployers must ensure sufficient AI literacy of staff dealing with AI systems.
- GDPR Art. 22. Right not to be subject to solely automated decisions with legal/similar effect; requires meaningful human involvement or explicit legal basis + safeguards.
- GDPR Art. 27. A controller or processor not established in the Union that falls within Art.
- GDPR Art. 17. Right to erasure collides with AI Act Art.
- GDPR Art. 25. Privacy by design & default: minimisation, pseudonymisation, PII filters in pipelines and vector stores.
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)
- Cyber Resilience Act: Up to €15m or 2.5% of worldwide annual turnover
- RED Cybersecurity Delegated Regulation (EU) 2022/30: Enforced through the Radio Equipment Directive's market-surveillance regime (Directive 2014/53/EU Chapter V and Regulation (EU) 2019/1020): non-compliant equipment can be withdrawn or recalled and Member States set penalties.
- GDPR: Up to €20m or 4% of worldwide annual turnover
- FTC Act §5 & Endorsement / AI-Claims Guidance: FTC enforcement, civil penalties, redress and injunctive conduct remedies.
- COPPA Rule (16 CFR Part 312): FTC enforcement under 15 U.S.C. § 45; civil penalties per violation (adjusted annually); state attorneys general may also sue (15 U.S.C. § 6504).
- EU Toy Safety (Directive 2009/48/EC → Regulation (EU) 2025/2509): Market surveillance under Regulation (EU) 2019/1020: withdrawal, recall, Member-State penalties.
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. 4, GDPR Art. 22, GDPR Art. 27 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 (HITL Escalation Queue & Review UI, Adverse-Decision Reason Generator, Bitemporal Memory (GDPR×Art.12)) 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: Minimal Risk open in the graph →
The regulated object is the product, not a decision about a person. As a product with digital elements the device falls under the Cyber Resilience Act (Art. 3(1)), whose vulnerability and incident reporting duties apply since 11 September 2026 and whose remaining manufacturer duties apply from 11 December 2027; as internet-connected radio equipment it must meet the RED essential requirements on network protection and privacy activated by Delegated Regulation (EU) 2022/30 since 1 August 2025; the GPSR applies residually, the Data Act's access-by-design duty applies to connected products placed on the market after 12 September 2026, and the revised Product Liability Directive treats software as a product. Video, audio and face data make GDPR Art. 25 (data protection by design) and, for products used by children, Art. 8 and the Toy Safety rules relevant; in the United States the FTC Act and — for services directed to children under 13 — the COPPA Rule apply. Under the AI Act the manufacturer is a provider (Art. 2(1)(a)); on-device event classification is not an Annex III use, so the system is minimal-risk, while a device that converses with users owes the Art. 50(1) disclosure.
What the reading rests on — the provisions this classification actually pulls in:
- Art. 4 — AI Literacy
- EU AI Act (Regulation (EU) 2024/1689)
- Cyber Resilience Act (Regulation (EU) 2024/2847)
- RED Cybersecurity Delegated Regulation (EU) 2022/30 (Commission Delegated Regulation (EU) 2022/30 of 29 October 2021 supplementing Directive 2014/53/EU (Radio Equipment Directive) with regard to the application of the essential requirements referred to in Article 3(3), points (d), (e) and (f))
- General Product Safety Regulation (Regulation (EU) 2023/988)
- Data Act (Regulation (EU) 2023/2854)
- Revised Product Liability Directive (Directive (EU) 2024/2853)
- GDPR (Regulation (EU) 2016/679)
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 (10)
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)
Cyber Resilience Act (Regulation (EU) 2024/2847)
Security-by-design for products with digital elements over the full lifecycle: vulnerability management, patching, SBOM. Complements AI Act Art. 15 at product level.
Sanctions: Up to €15m or 2.5% of worldwide annual turnover
RED Cybersecurity Delegated Regulation (EU) 2022/30 (Commission Delegated Regulation (EU) 2022/30 of 29 October 2021 supplementing Directive 2014/53/EU (Radio Equipment Directive) with regard to the application of the essential requirements referred to in Article 3(3), points (d), (e) and (f))
in-force · verified 2026-09-15 in force EU
Activates the Radio Equipment Directive's essential requirements on network protection (Art. 3(3)(d)), protection of personal data and privacy (Art. 3(3)(e)) and protection from fraud (Art. 3(3)(f)) for internet-connected radio equipment (Art. 1(1)) and for radio equipment that processes personal data, traffic or location data, including childcare, toy and wearable equipment (Art. 1(2)). Applies from 1 August 2025; the harmonised standards EN 18031-1/-2/-3 give presumption of conformity.
Sanctions: Enforced through the Radio Equipment Directive's market-surveillance regime (Directive 2014/53/EU Chapter V and Regulation (EU) 2019/1020): non-compliant equipment can be withdrawn or recalled and Member States set penalties.
General Product Safety Regulation (Regulation (EU) 2023/988)
Residual product-safety net for consumer products not covered by sector law.
Data Act (Regulation (EU) 2023/2854)
Access and re-use rights for (industrial) data, switching and interoperability duties — affects data sourcing for RAG pipelines and connected products.
Revised Product Liability Directive (Directive (EU) 2024/2853)
Extends strict defect liability to standalone software and AI — including systems that continue learning post-deployment. Covers physical harm, property damage, psychological health impairment and non-professional data loss; disclosure duties and rebuttable defect presumption shift the burden of proof toward the injured party. Makes reconstructible decision evidence an economic necessity.
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
FTC Act §5 & Endorsement / AI-Claims Guidance
in-force · verified 2026-08-17 in force US
Unfair or deceptive acts and practices, including unsubstantiated AI capability claims ('AI washing'), undisclosed synthetic endorsements and missing material disclosures in automated campaigns. Marketing generated end-to-end by agents must be scanned against a substantiated claim inventory before publication.
Sanctions: FTC enforcement, civil penalties, redress and injunctive conduct remedies.
COPPA Rule (16 CFR Part 312) (Children's Online Privacy Protection Act, 15 U.S.C. §§ 6501–6506, and the Children's Online Privacy Protection Rule, 16 CFR Part 312 (as amended 22 April 2025))
16 CFR § 312.3 requires an operator of a website or online service directed to children, or with actual knowledge that it collects personal information from a child under 13, to give notice and obtain verifiable parental consent before collecting, using or disclosing personal information (§ 312.5), including voice recordings, persistent identifiers and, since the 2025 amendments, biometric identifiers. A connected toy or companion app with cloud voice processing is an online service directed to children.
Sanctions: FTC enforcement under 15 U.S.C. § 45; civil penalties per violation (adjusted annually); state attorneys general may also sue (15 U.S.C. § 6504).
EU Toy Safety (Directive 2009/48/EC → Regulation (EU) 2025/2509) (Directive 2009/48/EC on the safety of toys, to be repealed with effect from 1 August 2030 by Regulation (EU) 2025/2509 on the safety of toys)
Directive 2009/48/EC applies to products designed or intended for use in play by children under 14 (Art. 2(1)) and requires compliance with the essential safety requirements before placing on the market (Art. 10); it remains the binding regime for toys placed on the market until the Toy Safety Regulation (EU) 2025/2509 applies from 1 August 2030 (Art. 56 of the Regulation repeals the Directive from that date). The Regulation adds digital-safety and privacy essential requirements for connected toys and a digital product passport. A connected, conversational toy is a toy in this sense whatever its AI features.
Sanctions: Market surveillance under Regulation (EU) 2019/1020: withdrawal, recall, Member-State penalties.
Legal Obligations (10)
density
Art. 4 — AI Literacy
Providers and deployers must ensure sufficient AI literacy of staff dealing with AI systems. In force since 2 Feb 2025.
unverified · no verification date 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 — read the OJ or consolidated text on EUR-Lex.
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 (2)
obligation (article) → operationalized_by → control objective → satisfied_by → component/pattern; control objective → evidenced_by → evidence artifact
Art. 4
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 42005 (AI Impact Assessment)
Guidance for AI system impact assessments — supports DPIA/FRIA-style analyses.
unverified · no verification date publisher ISO
evidence for: 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 · Cyber Resilience Act
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: EU AI Act
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
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: Cyber Resilience Act
NIST SP 800-218 (SSDF)
Secure Software Development Framework: practices for provenance, review and vulnerability handling of generated and third-party code; SSDF-AI companion covers AI-assisted development.
unverified · no verification date
evidence for: Cyber Resilience Act
Evidence you will need (9)
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 (1)
Written deliverables an authority or auditor can request as a file.
SBOM & Vulnerability Management Recordspractice-derived — dispute welcomeserves 2 obligations
CRA evidence: software bill of materials incl. model weights and datasets, vulnerability handling and patch history — reused for NIS2 supply-chain security and DORA third-party registers.
verifiability: documented artefact — verifiable on inspection
chain: Cyber Resilience Act · NIS2 Directive · Enterprise SDLC Code Automation & QA
Assessments (2)
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
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 (1)
Measured results from testing, evaluation or red-teaming.
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 (1)
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
Process records (4)
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
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
Architecture Blueprint
Hardened Edge / IoT Pattern
For critical-infrastructure and industrial AI: robustness hardening against corruption/covariate shift (DIN SPEC 92001-2), secure boot, SBOM management, physical kill switch, NIS2 incident telemetry.
Required Technical Components (16)
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: GDPR Art. 22
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: GDPR Art. 22
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
PII Scrubbing / DLP-NER Layer
Automated detection, pseudonymisation and blocking of personal data in inputs, retrievals and outputs.
from: GDPR Art. 25
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
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: GDPR Art. 35
Unified Incident-Response Runbook
One procedure reconciling AI Act Art. 73, GDPR Art. 33 (72h), DORA and NIS2 (24h/72h) timelines and recipients.
from: GDPR Art. 33/34 · Hardened Edge / IoT Pattern
Retrieval Rails (ACL-aware RAG)
Relevance, freshness and per-user permission checks on every retrieved chunk; curated, versioned index.
from: GDPR Art. 32
SBOM & Dependency Management
Software bill of materials incl. model weights and datasets; automated vulnerability patching pipeline.
from: Cyber Resilience Act · Hardened Edge / IoT Pattern
Secure Boot & Hardened Runtime
Verified boot chain and hardened runtimes for edge/IoT deployments per CRA security-by-design.
from: Cyber Resilience Act · Hardened Edge / IoT Pattern
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: Revised Product Liability Directive
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: Revised Product Liability Directive
Kill Switch / Graceful Degradation
Operator stop controls and degraded-mode fallbacks; real-time override (veto) channels for HOTL operation.
from: Hardened Edge / IoT Pattern
Build or Buy — Vendor Layer (8)
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.
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.
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: Adverse-Decision Reason Generator · OpenTelemetry / FCoT Tracing
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.
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: Adverse-Decision Reason Generator · Live Risk Register / Posture Management
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.
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: Bitemporal Memory (GDPR×Art.12) · Per-Tenant Retrieval Segmentation · Segmented Vector Store (RBAC + CMEK) · Retrieval Rails (ACL-aware RAG)
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.
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: 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.
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: Retrieval Rails (ACL-aware RAG)
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.
AI Supply-Chain Security & AIBOM
Bills of materials for AI: base architecture, training-data dependencies, fine-tuning history and licence lineage of a model, plus scanning of third-party pretrained weights and model artifacts for backdoors, poisoning and tampering before ingestion. Adjacent to, but not the same as, software SBOM tooling — the unit of analysis is a weights artifact and its provenance. Selection metrics: see meta.marketLandscape.selectionMetrics.supplychain.
unverified · verified 2026-08-17 community-maintained
selection metrics: Whether the AIBOM records training-data and fine-tuning lineage or only package dependencies; artifact formats scanned (safetensors, pickle, GGUF, container images); detection basis for tampering and poisoning (signature, behavioural, provenance attestation) and its false-positive rate; support for signing and verifying weights in your own pipeline; whether ingestion can be blocked, not just reported.
supplies: SBOM & Dependency Management
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 |
|---|---|---|---|---|
| Cranium AI | AIBOM & model provenance | AI bill-of-materials generation, model-provenance capture and third-party model risk scanning. Typical: AIBOM, third-party model ingestion. Scope overlap: Its AI-governance reporting scope overlaps this platform's own; we have a commercial interest in the comparison. | SaaS (vendor cloud) | NIST AI RMF alignment (claimed)EU AI Act readiness 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.
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
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.
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.