Healthcare
Pharmacy Back-Office & PBM Claims Adjudication AI
Minimal RiskUnverifiedDiscuss / dispute
AI performing prescription verification, drug-interaction and formulary screening, and pharmacy-benefit-manager (PBM) claims adjudication and reimbursement calculation in US pharmacy and PBM operations, distinct from general insurer prior-authorisation.
Consensus classification rationale: Pharmacy back-office and PBM adjudication AI sits on a regulatory track distinct from insurer prior-authorisation (uc-priorauth), and its hooks are mostly US. Where the system supports a DEA-registered pharmacy, the pharmacist's 'corresponding responsibility' for controlled-substance prescriptions (21 CFR 1306.04(a)) stays with the pharmacist, and electronic Schedule II-V prescriptions may only be processed through a pharmacy application that meets 21 CFR Part 1311 Subpart C. Its drug-interaction and formulary alerts stay outside FDA device regulation only while they meet all four Non-Device CDS criteria of FD&C Act section 520(o)(1)(E), including that the pharmacist can independently review the basis of each alert (Criterion 4); the claims-adjudication and reimbursement logic is administrative rather than clinical and is reached instead by state PBM law such as Illinois's Prescription Drug Affordability Act (spread-pricing and steering ban, 100% rebate and fee pass-through) and by HIPAA, with GDPR added for EU patients. No EU AI Act Annex III point covers a private PBM's claims adjudication, so the default classification is minimal risk.
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 chainboth — 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
6 instruments across 3 of 7 regulatory domains, plus 5 standards references- AI law1 instrument
- Data protection2 instruments
- Cyber & resiliencenone triggered
- Online safety & platformsnone triggered
- Product safetynone triggered
- Financial servicesnone triggered
- Sector & employment3 instruments
- Standards5 references
By jurisdiction
- EU2European UnionEU AI Act, GDPR
- US3United States (federal)DEA Controlled Substances Act Dispensing & E-Prescribing Rules, FDA oversight of Software as a Medical Device (US), HIPAA (US Health Privacy)
- US-IL1thinUnited States — IllinoisIllinois Prescription Drug Affordability Act (PBM Reform)
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 22 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 band8 factors lowered the band — each links to the claim behind it
- Source tier: FDA oversight of Software as a Medical Device (US) 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 →
- Status certainty: FDA oversight of Software as a Medical Device (US) carries an unverified status — recorded from a secondary source and not confirmed against the primary text. 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 →
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)
- GDPR: Up to €20m or 4% of worldwide annual turnover
- DEA Controlled Substances Act Dispensing & E-Prescribing Rules: DEA registration suspension or revocation (21 U.S.C. § 824(a)), civil penalties (21 U.S.C. § 842(c)) and criminal liability for unlawful dispensing (21 U.S.C. § 841); 21 CFR 1306.04(a) itself subjects a person knowingly filling a purported prescription not issued in the usual course of professional treatment to the penalties for controlled-substance violations.
- Illinois Prescription Drug Affordability Act (PBM Reform): Administered and enforced by the Illinois Department of Insurance; Company Bulletin 2025-20 describes the Department's enforcement of the PDAA's report and fee deadlines, including a stated exercise of enforcement discretion. A statutory penalty schedule is not captured in this node. Enforcement of the reporting requirements as applied to ERISA plans is preliminarily enjoined (see status note).
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 →
Pharmacy back-office and PBM adjudication AI sits on a regulatory track distinct from insurer prior-authorisation (uc-priorauth), and its hooks are mostly US. Where the system supports a DEA-registered pharmacy, the pharmacist's 'corresponding responsibility' for controlled-substance prescriptions (21 CFR 1306.04(a)) stays with the pharmacist, and electronic Schedule II-V prescriptions may only be processed through a pharmacy application that meets 21 CFR Part 1311 Subpart C. Its drug-interaction and formulary alerts stay outside FDA device regulation only while they meet all four Non-Device CDS criteria of FD&C Act section 520(o)(1)(E), including that the pharmacist can independently review the basis of each alert (Criterion 4); the claims-adjudication and reimbursement logic is administrative rather than clinical and is reached instead by state PBM law such as Illinois's Prescription Drug Affordability Act (spread-pricing and steering ban, 100% rebate and fee pass-through) and by HIPAA, with GDPR added for EU patients. No EU AI Act Annex III point covers a private PBM's claims adjudication, so the default classification is minimal risk.
What the reading rests on — the provisions this classification actually pulls in:
- Art. 4 — AI Literacy
- EU AI Act (Regulation (EU) 2024/1689)
- GDPR (Regulation (EU) 2016/679)
- DEA Controlled Substances Act Dispensing & E-Prescribing Rules (Controlled Substances Act, 21 U.S.C. § 801 et seq., as implemented by DEA regulations at 21 CFR Part 1306 (Prescriptions) and 21 CFR Part 1311 (Requirements for Electronic Orders and Prescriptions))
- FDA oversight of Software as a Medical Device (US) (FDA SaMD / Digital Health regulatory framework)
- Illinois Prescription Drug Affordability Act (PBM Reform) (Prescription Drug Affordability Act, Public Act 104-0027 (eff. 1 July 2025; entire Act effective 1 January 2026), Article XXXIIB of the Illinois Insurance Code, 215 ILCS 5/513b1 et seq.)
- HIPAA (US Health Privacy)
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 (6)
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
DEA Controlled Substances Act Dispensing & E-Prescribing Rules (Controlled Substances Act, 21 U.S.C. § 801 et seq., as implemented by DEA regulations at 21 CFR Part 1306 (Prescriptions) and 21 CFR Part 1311 (Requirements for Electronic Orders and Prescriptions))
21 CFR 1306.04(a) puts the 'corresponding responsibility' for the legitimacy of a controlled-substance prescription on the pharmacist who fills it, and subjects a person who knowingly fills a purported prescription not issued in the usual course of professional treatment to the penalties for controlled-substance violations; an automated verification tool can surface red flags but the duty stays with the pharmacist. For electronic prescriptions, 21 CFR Part 1311 Subpart C lets a registered pharmacy process electronic Schedule II-V prescriptions only through a pharmacy application that meets the Subpart (1311.100(e)), including a daily audit-trail analysis (1311.215(b)) and two-year electronic record retention (1311.305(b)); the practitioner side requires an identity-proofed two-factor authentication credential and two-factor signing (1311.105, 1311.115, 1311.140). 1311.200(i) adds that nothing in Part 1311 relieves a pharmacy and pharmacist of the duty to dispense only pursuant to a prescription issued for a legitimate medical purpose. The electronic-prescription rules in Part 1311 come from DEA's interim final rule (75 FR 16236), effective 1 June 2010.
Sanctions: DEA registration suspension or revocation (21 U.S.C. § 824(a)), civil penalties (21 U.S.C. § 842(c)) and criminal liability for unlawful dispensing (21 U.S.C. § 841); 21 CFR 1306.04(a) itself subjects a person knowingly filling a purported prescription not issued in the usual course of professional treatment to the penalties for controlled-substance violations.
FDA oversight of Software as a Medical Device (US) (FDA SaMD / Digital Health regulatory framework)
Placeholder for the US premarket pathway applicable to AI-enabled clinical decision support. RAIN carries NO sourced FDA node yet: the applicable pathway (510(k), De Novo, PMA, or CDS enforcement discretion) depends on the intended use and is not asserted here.
Illinois Prescription Drug Affordability Act (PBM Reform) (Prescription Drug Affordability Act, Public Act 104-0027 (eff. 1 July 2025; entire Act effective 1 January 2026), Article XXXIIB of the Illinois Insurance Code, 215 ILCS 5/513b1 et seq.)
in-force — reporting requirements preliminarily enjoined as applied to ERISA plans (C.D. Ill., 31 Aug 2026) · verified 2026-09-21 in force US-ILthin
Prohibits pharmacy benefit managers from spread pricing and steering and requires them to pass through 100% of rebates and fees; IDOI states that the entire Act went into effect on 1 January 2026 (some changes began in 2025). Separately, Sections 513b2(f) and 513b1.1 require each PBM to file an annual covered-individual report and pay $15 per covered individual (IDOI Company Bulletin 2025-20). Applicability turns on the plan: per that bulletin the report and fee provisions apply to PBMs in relation to Illinois-sitused fully-insured policies, Illinois Medicaid and All Kids, and self-funded employee welfare benefit plans sitused in Illinois — including self-funded ERISA plans other than self-funded multiemployer plans, and state-employee and school plans — and do not apply to self-funded multiemployer ERISA plans, other governmental plans, Medicare Advantage, Medicare Part D, TRICARE, the Federal Employees Health Benefits Program or group policies sitused outside Illinois. The Department adds that some, but not all, Article XXXIIB requirements apply to PBMs in relation to ERISA plans they administer. That is IDOI's position as of 26 November 2025, stated as clarified by HB 767, which the bulletin says had not yet been signed into law on that date; HB 767 was signed on 2 December 2025 (Illinois Department of Public Health release of that date). On 31 August 2026 a federal court preliminarily enjoined enforcement of the Act's reporting requirements as applied to ERISA plans (see status note), so that ERISA reporting position should not be read as current for those plans.
Sanctions: Administered and enforced by the Illinois Department of Insurance; Company Bulletin 2025-20 describes the Department's enforcement of the PDAA's report and fee deadlines, including a stated exercise of enforcement discretion. A statutory penalty schedule is not captured in this node. Enforcement of the reporting requirements as applied to ERISA plans is preliminarily enjoined (see status note).
HIPAA (US Health Privacy)
in-force · verified 2026-08-04 in force US
US health-data regime (Privacy, Security & Breach Notification Rules): PHI minimum-necessary standard, BAA chains for AI vendors, audit controls and access logging. For clinical AI (scribing, diagnostics, prior-auth) it is the US-side twin of GDPR Art. 9 — evidence overlap: access logs, vendor due diligence, encryption attestation.
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
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
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 →
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 (5)
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
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.
Human-in-the-Loop Core Pattern
For high-risk decision support over people (HR, credit, benefits): confidence-thresholded escalation queues, WORM audit vault, bias testing per ISO 5259 — the human decision is architecturally enforced. CORRECTED 2026-08-15: this pattern previously recommended a post-hoc explainability API (SHAP/LIME or CoT traces) as the explanation mechanism for consequential scoring of natural persons. That is the wrong default. Post-hoc local explainers are sampling-unstable, vary with perturbation choice and are susceptible to fairwashing, and an adverse-action reason that would not change the outcome if the applicant remediated it is not a defensible reason. For consequential scoring (credit, insurance pricing, tenant screening, employment scoring) use intrinsic interpretability — glass-box additive models with monotonic constraints — and keep post-hoc methods as a supplementary diagnostic only.
Deterministic Document-Validation Pipeline
Schema, completeness and duplicate checks implemented as deterministic rules with a model used only for extraction, never for judgement. The absence of an evaluative step is what keeps a narrow procedural task narrow — and what makes an Art. 6(3)(a) claim documentable.
Required Technical Components (22)
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 · Human-in-the-Loop Core Pattern
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 · Glass-Box EBM with Monotonic Constraints
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
Retrieval Rails (ACL-aware RAG)
Relevance, freshness and per-user permission checks on every retrieved chunk; curated, versioned index.
from: GDPR Art. 32
Explainability API (SHAP/LIME/CoT)
Feature attributions for classical ML, reasoning-trace summaries for GenAI — feeds the human reviewer and the technical file.
from: Glass-Box EBM with Monotonic Constraints · Human-in-the-Loop Core Pattern
Bias Testing & Data Quality Pipeline
Representativeness checks, bias metrics and mitigation per ISO/IEC 5259; versioned datasets with lineage.
from: Glass-Box EBM with Monotonic Constraints · Human-in-the-Loop Core Pattern
Data Lineage & Versioning
Provenance tracking of datasets, features and embeddings; write-time attribution (source, actor, timestamp, confidence).
from: Glass-Box EBM with Monotonic Constraints
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: Glass-Box EBM with Monotonic Constraints · Human-in-the-Loop Core Pattern · Deterministic Document-Validation Pipeline
Durable Checkpointing (Pause & Resume)
At oversight gates the complete operational state — working memory, conversation history, tool arguments, intermediate artifacts — is serialized into a durable checkpoint (fast KV store for sub-ms lookups, transactional backend as recovery anchor, vector store for semantic caching of past human decisions). On approval the agent deserializes and resumes at the exact step; matched precedents can shortcut re-planning entirely.
from: Human-in-the-Loop Core Pattern
Kill Switch / Graceful Degradation
Operator stop controls and degraded-mode fallbacks; real-time override (veto) channels for HOTL operation.
from: Human-in-the-Loop Core Pattern
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: Human-in-the-Loop Core Pattern
Active-Learning Feedback Loop
Human corrections at oversight gates are serialized as structured data — original context, model proposal, human edit, rationale — and fed into fine-tuning pipelines and prompt registries, systematically reducing future escalation rates instead of dying in review UIs.
from: Human-in-the-Loop Core Pattern
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: Human-in-the-Loop Core Pattern
Document Intelligence Engine
OCR, layout parsing and semantic clause extraction over filings, contracts and invoices, emitting structured records with span-level source references.
from: Deterministic Document-Validation Pipeline
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: Deterministic Document-Validation Pipeline
Build or Buy — Vendor Layer (9)
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 · Explainability API (SHAP/LIME/CoT) · Bias Testing & Data Quality Pipeline · Confidence Scoring & Threshold Gate · 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 |
|---|---|---|---|---|
| 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 · 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 |
|---|---|---|---|---|
| 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) · Data Lineage & Versioning · Document Intelligence Engine
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) · Data Lineage & Versioning
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.
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.
Agentic Applications & Copilots
Finished agentic products bought rather than built: developer and productivity copilots, research assistants, SOC and support agents. The governance point is not the product but the wrapper: these tools act with delegated authority inside your estate, so they belong in the agent inventory, need scoped non-human identities and permission boundaries, and inherit deployer duties — buying the product does not buy the obligations away.
unverified · verified 2026-08-18 community-maintained
selection metrics: Permission model and identity scoping; audit log export; tenant data-handling and retention terms; deployer-duty support (disclosure, oversight, incident reporting); outcome pricing vs seat pricing.
supplies: Document Intelligence Engine
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 |
|---|---|---|---|---|
| GitHub Copilot | developer copilot | Code completion and agent modes inside the IDE and repository workflow. Typical: software engineering, code review. | not checked | SOC 2 (claimed)enterprise data-handling commitments (claimed) |
| Microsoft 365 Copilot | productivity copilot | Assistant across mail, documents and meetings inheriting existing tenant permissions. Typical: knowledge work, meeting summaries. | not checked | ISO 27001 (claimed)SOC 2 (claimed)EU data-boundary positioning |
| Perplexity Enterprise | research assistant | Cited web and internal search with source attribution per answer. Typical: market research, citation-backed search. | not checked | SOC 2 (claimed)enterprise data-handling commitments (claimed) |
| Cursor | developer copilot | AI-native editor with repository-wide agent edits. Typical: software engineering, refactoring. | not checked | SOC 2 (claimed)privacy-mode option (claimed) |
and 5 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.
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.