Banking & Insurance
Algorithmic Portfolio Execution & Advisory
Limited Risk (Transparency)UnverifiedDiscuss / dispute
Trade execution against target parameters, automated conflict-of-interest analysis, generated client advisory communications and full attribution logging of every model inference.
Consensus classification rationale: Retail-facing advice engages Reg BI and the fiduciary standard; the proposed PDA rules require conflicts embedded in a covered technology to be eliminated or neutralised, not merely disclosed. Under 204-2 and 17a-4 every recommendation, prompt and inference is a record that must be WORM-archived and attributed to a supervising person.
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.
pricingBasis points on automated AUM + usage/compute tier
oversightPre-set trade thresholds and kill-switches; named supervisor attribution on every recommendation
Target market(s)European UnionUnited States (federal)change
Changes which instruments below count as in scope for this profile.
Target markets: European Union, United States (federal)
Regulatory footprint
11 instruments across 4 of 7 regulatory domains, plus 6 standards references- AI law2 instruments
- Data protectionnone triggered
- Cyber & resilience1 instrument
- Online safety & platformsnone triggered
- Product safetynone triggered
- Financial services4 instruments
- Sector & employment4 instruments
- Standards6 references
By jurisdiction
- EU5European UnionArt. 12 — Record-Keeping / Logging, Art. 26 — Deployer Obligations, Art. 50 — Transparency Duties, DORA, EU AI Act
- US5United States (federal)FINRA Rule 4511 (General Books & Records), SEC Advisers Act Rule 204-2 (Books & Records), SEC Predictive Data Analytics Rules (withdrawn 2025), SEC Regulation Best Interest, SEC Rule 17a-4 (US Records Retention)
- DE1thinGermanyBaFin MaRisk (Mindestanforderungen an das Risikomanagement)
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 21 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: SEC Predictive Data Analytics Rules (withdrawn 2025) carries no resolvable citation — the claim is uncited. open node →
- Source tier: FINRA Rule 4511 (General Books & Records) 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: TAGOF (Audit-as-Code) carries no resolvable citation — the claim is uncited. open node →
- Status certainty: SEC Predictive Data Analytics Rules (withdrawn 2025) is "withdrawn", not settled in-force law. 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: FINRA Rule 4511 (General Books & Records) has no recorded verification date. open node →
- Verification age: IEEE CertifAIEd™ has no recorded verification date. open node →
- Verification age: TAGOF (Audit-as-Code) has no recorded verification date. open node →
Compliance brief
This use case is limited-risk under the EU AI Act (Limited Risk (Transparency)); transparency obligations apply.
What is owed
- Art. 50. Disclose AI interaction to natural persons; machine-readable marking of synthetic content; deepfake labelling; emotion-recognition disclosure.
- Art. 4. Providers and deployers must ensure sufficient AI literacy of staff dealing with AI systems.
- Art. 25. A deployer becomes the provider (full Art.
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
- SEC Advisers Act Rule 204-2 (Books & Records): SEC enforcement, deficiency letters, books-and-records penalties.
- SEC Predictive Data Analytics Rules (withdrawn 2025): No rule in force — withdrawn before adoption. Existing fiduciary duty, Reg BI and books-and-records obligations continue to carry the same subject matter in examinations.
- SEC Regulation Best Interest: SEC/FINRA enforcement, restitution, censure.
- SEC Rule 17a-4 (US Records Retention): SEC enforcement; multi-hundred-million-dollar off-channel/recordkeeping penalties are routine.
- FINRA Rule 4511 (General Books & Records): FINRA disciplinary action, fines and supervisory findings.
- 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)
- DORA: Administrative penalties; periodic penalty payments for critical third parties
- BaFin MaRisk (Mindestanforderungen an das Risikomanagement): Supervisory measures under the KWG (findings, capital add-ons, orders)
First five actions
- Confirm in writing whether this organisation builds/places the system on the market (provider) or only operates it (deployer), since the role is not yet established.
- Commission and confirm the Art. 50, Art. 4, Art. 25 obligations named above as active workstreams with an accountable owner.
- Stand up the named oversight design — Mode 2 — with a documented human-review procedure.
- Produce the technical documentation and evidence artefacts already mapped to this use case (Synthetic-Content Labelling / Watermarking, Interface Transparency & Content-Marking Layer, Zero-Data-Retention Vendor Binding) 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: · · ·
Classification precedent
Consensus reading: Limited Risk (Transparency) open in the graph →
Retail-facing advice engages Reg BI and the fiduciary standard; the proposed PDA rules require conflicts embedded in a covered technology to be eliminated or neutralised, not merely disclosed. Under 204-2 and 17a-4 every recommendation, prompt and inference is a record that must be WORM-archived and attributed to a supervising person.
What the reading rests on — the provisions this classification actually pulls in:
- Art. 50 — Transparency Duties
- Art. 4 — AI Literacy
- SEC Advisers Act Rule 204-2 (Books & Records) (17 CFR 275.204-2 (Advisers Act books and records))
- SEC Predictive Data Analytics Rules (withdrawn 2025)
- SEC Regulation Best Interest (17 CFR 240.15l-1)
- SEC Rule 17a-4 (US Records Retention) (17 CFR 240.17a-4)
- FINRA Rule 4511 (General Books & Records) (FINRA Rule 4511)
- EU AI Act (Regulation (EU) 2024/1689)
No dissenting reading is recorded for this case. That means nobody has filed one yet — not that the classification is beyond argument. file a dissent with a source →
Baseline: of 100+, 40% were not definitively classifiable (18% clearly high-risk, 42% clearly low-risk). appliedAI Institute — AI Act risk classification of AI systems from a practical perspective
Applicable Regulations (11)
SEC Advisers Act Rule 204-2 (Books & Records) (17 CFR 275.204-2 (Advisers Act books and records))
Registered investment advisers must preserve records of recommendations, advisory communications and the data behind them. Where an AI agent evaluates portfolios, drafts client communications or generates recommendations, its inferences and prompts become advisory records that need an attribution chain to a named supervising person — generic system service accounts are not acceptable.
Sanctions: SEC enforcement, deficiency letters, books-and-records penalties.
SEC Predictive Data Analytics Rules (withdrawn 2025)
Withdrawn proposal for Exchange Act Rule 15l-2 and Advisers Act Rule 211(h)(2)-4, which would have covered any 'covered technology' — algorithm, model, correlation matrix or computational process that optimises, predicts or guides investor behaviour. Firms would have had to inventory covered technologies, test them for conflicts of interest, and eliminate or neutralise any optimisation putting the firm's interest ahead of the client, with disclosure and consent explicitly insufficient as a remedy. The conflicts analysis remains the clearest articulation of the supervisory concern even though the rule text is no longer pending.
Sanctions: No rule in force — withdrawn before adoption. Existing fiduciary duty, Reg BI and books-and-records obligations continue to carry the same subject matter in examinations.
SEC Regulation Best Interest (17 CFR 240.15l-1)
Broker-dealers must act in the retail customer's best interest at the time a recommendation is made, with care, disclosure, conflict and compliance obligations. Automated recommendation engines inherit the full standard, including documented conflict mitigation.
Sanctions: SEC/FINRA enforcement, restitution, censure.
SEC Rule 17a-4 (US Records Retention) (17 CFR 240.17a-4)
Broker-dealer record retention: electronic records must be preserved in non-rewriteable, non-erasable (WORM) or audit-trail form, indexed and reproducible on demand. Retention is tiered, not flat — 17a-4(a) requires six years for blotters, ledgers and customer account records, while 17a-4(b) requires three years for the broader category of communications, trade confirmations and supporting records.
Sanctions: SEC enforcement; multi-hundred-million-dollar off-channel/recordkeeping penalties are routine.
FINRA Rule 4511 (General Books & Records) (FINRA Rule 4511)
unverified · no verification date in force US
Requires member firms to preserve books and records not otherwise specified for at least six years in a format compliant with SEA Rule 17a-4(f).
Sanctions: FINRA disciplinary action, fines and supervisory findings.
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)
DORA (Regulation (EU) 2022/2554)
Digital operational resilience for the financial sector — in force and applying since 17 January 2025: ICT third-party risk (CTPP oversight), change management, resilience testing — applies to AI-based trading, credit and KYC systems. ICT third-party risk management extends to AI supply chains: model and dependency inventories (AIBOM/SBOM-style), contractual audit and exit rights, and concentration risk across model providers.
Sanctions: Administrative penalties; periodic penalty payments for critical third parties
Art. 50 — Transparency Duties
in-force · verified 2026-08-16 source (as amended) EUR-Lexconvenience mirror — not updated artificialintelligenceact.euAmended by Regulation (EU) 2026/1744. Verified 16 Aug 2026: the popular mirrors have not yet been updated — artificialintelligenceact.eu still serves the unamended 13 June 2024 text with no disclaimer, and the Commission's AI Act Service Desk pages still show pre-omnibus text with a visible omnibus disclaimer. Read the OJ or consolidated text on EUR-Lex. in force EU
Disclose AI interaction to natural persons; machine-readable marking of synthetic content; deepfake labelling; emotion-recognition disclosure.
Art. 26 — Deployer Obligations
Use per instructions, assign competent human oversight, input-data control, inform workers, incident duty. Art. 26(6): keep the logs generated by the high-risk system that are under the deployer's control for a period appropriate to the intended purpose, at least six months, unless other Union or national law — in particular data-protection law — provides otherwise.
Art. 12 — Record-Keeping / Logging
Requires logging CAPABILITY over the system's lifetime, recording events relevant to identifying situations that may present an Art. 79(1) risk or lead to a substantial modification, and to post-market monitoring (Art. 72) and deployer monitoring (Art. 26(5)). SCOPE: the itemised minimum log content of Art. 12(3) — period of each use with start and end date and time; the reference database against which input data was checked; the input data for which the search led to a match; the identification of the natural persons involved in verification of the results per Art. 14(5) — applies ONLY to Annex III point 1(a) remote biometric identification systems, not to all high-risk systems. RETENTION: providers (Art. 19) and deployers (Art. 26(6)) must keep the logs under their control for a period appropriate to the intended purpose, at least six months, unless other Union or national law — in particular data-protection law — provides otherwise. A bolted-on logging wrapper does not satisfy the requirement: logging must be core architecture.
BaFin MaRisk (Mindestanforderungen an das Risikomanagement)
BaFin circular on the minimum requirements for risk management, issued on the basis of § 25a KWG. For German credit institutions it means an ML-based decision procedure must be transparent, mathematically validated and audit-proof: the model, its data basis and its validation are part of the institution's documented risk-management system and are examined by internal audit and by the supervisor. It applies alongside the AI Act rather than instead of it — the AI Act governs the system, MaRisk governs the institution that uses it.
Sanctions: Supervisory measures under the KWG (findings, capital add-ons, orders)
Legal Obligations (3)
density
Art. 50 — Transparency Duties
Disclose AI interaction to natural persons; machine-readable marking of synthetic content; deepfake labelling; emotion-recognition disclosure.
in-force · verified 2026-08-16 source (as amended) EUR-Lexconvenience mirror — not updated artificialintelligenceact.euAmended by Regulation (EU) 2026/1744. Verified 16 Aug 2026: the popular mirrors have not yet been updated — artificialintelligenceact.eu still serves the unamended 13 June 2024 text with no disclaimer, and the Commission's AI Act Service Desk pages still show pre-omnibus text with a visible omnibus disclaimer. Read the OJ or consolidated text on EUR-Lex.
Art. 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.
Art. 25 — Value Chain / Role Flip
A deployer becomes the provider (full Art. 8–17 duties) by re-branding, changing intended purpose, or making a substantial modification — e.g. deep fine-tuning or wiring a model into autonomous agent toolchains.
unverified · no verification date read the article artificialintelligenceact.eu
Control Objectives (1)
obligation (article) → operationalized_by → control objective → satisfied_by → component/pattern; control objective → evidenced_by → evidence artifact
Art. 50
AI Interaction & Content Disclosure
Natural persons are informed they interact with an AI system, and generated/manipulated content carries both human-visible labels and machine-readable provenance (C2PA-class) that survives publication pipelines. Testable: disclosure presence across all interaction surfaces; watermark validity sampling post-publication; deepfake-path red-team (does stripped metadata get caught at the gate?).
satisfied by: Synthetic-Content Labelling / WatermarkingOutput Rails / Groundedness CheckInterface Transparency & Content-Marking Layer
evidenced by: Guardrail Telemetry & Sanitization Records
Art. 4
control layer: community mandate — propose objectives
Art. 25
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
C2PA Content Credentials
Open technical standard for cryptographically signed content provenance: manifests binding origin, toolchain and edit history to media assets. The de-facto machine-readable implementation path for Art. 50 synthetic-content marking (machine-readable format + detectability duty) — visible labels satisfy the human side, C2PA manifests the machine side. Verification at publication gates produces the disclosure evidence stream.
evidence for: Art. 50 · Art. 50 — Transparency Duties
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
FprEN ISO/IEC 24970 (AI Logging)
Specifies event logging in AI systems — the concrete implementation target for Art. 12 record-keeping.
evidence for: Art. 12 — Record-Keeping / Logging
TAGOF (Audit-as-Code)
Operationalizes governance as code in CI/CD: policy-as-code enforcement, continuous runtime telemetry and automatically generated audit evidence — the execution layer that replaces periodic audits with continuous assurance.
unverified · no verification date
evidence for: Art. 12 — Record-Keeping / Logging
Evidence you will need (10)
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 (2)
Written deliverables an authority or auditor can request as a file.
Post-Market Monitoring Plan & Incident Reportstext-derivedserves 4 obligations
Art. 72 monitoring plan plus Art. 73 serious-incident reports (15 days; 2 days for widespread infringement) — reconciled in one runbook with GDPR Art. 33 (72h), NIS2 (24h/72h) and DORA timelines.
verifiability: documented artefact — verifiable on inspection
chain: Art. 72/73 — Post-Market Monitoring & Incidents · Art. 72/73 — Post-Market Monitoring & Incidents → CO: Performance Monitoring & Drift Management · DORA · NIS2 Directive · Clinical Imaging Triage & Patient Follow-Up · Cross-Border Statutory Tax & Wealth Filing · +2 more
Instructions for Use / Transparency Docstext-derivedserves 2 obligations
Art. 13 deployer-facing documentation: intended purpose, capabilities, limitations, expected accuracy, oversight measures — plus Art. 50 user-facing disclosures.
verifiability: documented artefact — verifiable on inspection
chain: Art. 13 — Transparency to Deployers · Art. 50 — Transparency Duties · Generative Asset Production & Virtual Try-On · Omnichannel Virtual Support & Voice Bots
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
Third-Party AI Data & ZDR Certificatepractice-derived — dispute welcome
Binding vendor terms on zero data retention, non-training use, sub-processor list and security boundary, with technical verification records.
verifiability: independently-attested
chain: Art. 25 — Value Chain / Role Flip · Generative Asset Production & Virtual Try-On
Log records (3)
Machine-generated records produced while the system runs.
Event Logs & Decision Tracestext-derivedserves 17 obligations
The single highest-leverage artifact: hash-chained, WORM-stored logs with structured decision traces. Required capability fields per FprEN ISO/IEC 24970: input/output traces, execution timestamps, acting user/agent identity, referenced sources, human overrides. Audit-packet spec per event: model version, system-prompt/context hash, hyper-parameters (temperature, top-p), output payload, confidence score, active policy-ruleset versions, human override record. Simultaneously serves AI Act Art. 12, GDPR accountability, DORA incident reporting, NIS2 logging, PLD disclosure duties and its rebuttable defect presumption; financial-sector regimes push retention to 7 years (SEC 17a-4-class WORM rules). Credibility bar: anchor hash-chain heads externally (qualified timestamp / eIDAS ledger) so integrity survives an insider with admin rights.
verifiability: externally-anchored
chain: Art. 12 — Record-Keeping / Logging · CRA Art. 14 — Vulnerability & Severe-Incident Reporting · DORA Art. 19 — Major ICT-Incident Reporting · GDPR Art. 33/34 — Personal-Data Breach Notification · HIPAA Breach Notification Rule · NIS2 Art. 23 — Significant-Incident Reporting · +21 more
Guardrail Telemetry & Sanitization Recordspractice-derived — dispute welcomeserves 3 obligations
Control-level evidence for the OWASP mappings: guardrail trigger records, blocked-prompt statistics (LLM01), runtime output-sanitization logs (LLM05), groundedness-check outcomes — the empirical proof that declared controls actually execute.
verifiability: tamper-evident
chain: Art. 15 — Accuracy, Robustness, Cybersecurity · Art. 50 — Transparency Duties → CO: AI Interaction & Content Disclosure · Art. 15 — Accuracy, Robustness, Cybersecurity → CO: Runtime Injection Defense · Dynamic Deal Desk & Quoting Engine · Enterprise Marketing Disclosure Compliance · LLM01 Prompt Injection · +3 more
Immutable Decision Ledger (WORM)practice-derived — dispute welcomeserves 3 obligations
Per-execution audit packet: timestamp, model version, system prompt, input-context hash, hyper-parameters, output payload, confidence score and human override record.
verifiability: externally-anchored
chain: Art. 12 — Record-Keeping / Logging · Art. 12 — Record-Keeping / Logging → CO: Log Integrity & Non-Repudiation · IFRS / US GAAP Reporting Assurance · Algorithmic Portfolio Execution & Advisory · Automated Financial Forecasting & Audit Trails · Cross-Border Statutory Tax & Wealth Filing · +3 more
Process records (2)
Traces that a process actually happened, and who did it.
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
Registry entries (1)
An entry in a register — internal inventory or public registry.
AI System Inventory / Registry Entrypractice-derived — dispute welcomeserves 3 obligations
The organizational register of AI systems in use — role (provider or deployer), classification, owner, vendor and lifecycle state — from which per-system obligations are assigned.
verifiability: self-asserted
chain: Art. 17 — Quality Management System · Art. 26 — Deployer Obligations · Art. 26 — Deployer Obligations → CO: Embedded-AI Vendor Governance
Architecture Blueprint
Sovereign Resilient Enterprise Pattern
For regulated finance / high-sensitivity workloads: EU-jurisdiction or EUCS-High+ cloud, confidential computing, BYOK via external HSM, multi-region failover, full FCoT/OpenTelemetry tracing, DORA-grade third-party auditing.
Mode 2 — Supervised Autonomy
Execution within a delay window during which a human can intervene; dominant mode in well-designed regulated production systems.
Required Technical Components (18)
Synthetic-Content Labelling / Watermarking
Synthetic-content labelling & watermarking: visible disclosure plus machine-readable provenance (C2PA Content Credentials) embedded in generated images, audio and video; metadata identifying artificial origin survives common transformations. Discharges Art. 50(2)/(4) for deepfakes and synthetic media; verification telemetry (watermark presence/validity checks at publication gates) is the corresponding evidence stream.
from: Art. 50
Interface Transparency & Content-Marking Layer
The disclosure surface at the engagement layer: an AI-interaction notice on every channel a natural person can reach (web, app, voice, chat, social, marketplace), machine-readable provenance marking on generated or manipulated output, and a disclosure record per interaction that can be produced on request. Sits at the interface, not in the model — a model-side label that the frontend drops is not a disclosure.
from: Art. 50
Zero-Data-Retention Vendor Binding
Sensitive inference is contractually and technically restricted to endpoints under zero-data-retention and non-training terms, evidenced per vendor and re-validated annually.
from: Art. 25
Agent Discovery & Registry Endpoint
The marketplace/discovery API through which external agents find, authenticate against and transact with your agents: published capability descriptors, counterparty authentication, per-counterparty rate and value limits, and a resolvable record of which external principal initiated which transaction. Without it, business-to-agent traffic is anonymous inbound automation.
from: Art. 25
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: SEC Rule 17a-4 (US Records Retention) · FINRA Rule 4511 (General Books & Records) · BaFin MaRisk (Mindestanforderungen an das Risikomanagement)
Agent Memory Record Store
Agent conversational and working memory managed as a books-and-records object rather than a cache: retention schedules per regime, immutability where records rules demand it, deletion paths for erasure requests that do not break the audit trail, and export in a form a supervisor can read. Where an agent's memory carries a business communication or a decision rationale, it is a record — the storage tier does not decide that.
from: SEC Rule 17a-4 (US Records Retention) · FINRA Rule 4511 (General Books & Records)
Multi-Region Failover & Resilience Testing
DORA-grade continuity: regional redundancy, chaos testing, exit strategies for critical third parties.
from: DORA · Sovereign Resilient Enterprise Pattern
Vendor & Model Due-Diligence Kit
Scoring model: jurisdiction (CLOUD Act exposure), zero-data-retention, BYOK support, audit evidence (C5/AIC4/ISO 42001/EN 18286:2026), tenant isolation.
from: DORA · Sovereign Resilient Enterprise Pattern
Explainability API (SHAP/LIME/CoT)
Feature attributions for classical ML, reasoning-trace summaries for GenAI — feeds the human reviewer and the technical file.
from: BaFin MaRisk (Mindestanforderungen an das Risikomanagement)
Data Lineage & Versioning
Provenance tracking of datasets, features and embeddings; write-time attribution (source, actor, timestamp, confidence).
from: BaFin MaRisk (Mindestanforderungen an das Risikomanagement)
Confidential Computing Enclaves
AMD SEV / Intel TDX: data protected from the cloud operator even in memory during inference.
from: Sovereign Resilient Enterprise Pattern
BYOK via External HSM
Customer-controlled key sovereignty; cascaded encryption independent of the cloud provider.
from: Sovereign Resilient Enterprise 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: Sovereign Resilient Enterprise Pattern
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: Sovereign Resilient Enterprise Pattern
Sovereign Context Layer
Governed runtime workspace operationalizing Art. 10: traceable lineage for every RAG chunk and training record at execution time, canonical version-controlled business glossary (documents Art. 10(2)(d) baseline assumptions), and continuous data-quality monitoring with threshold alerts and logged remediation for the Art. 10(3) 'error-free and complete' standard.
from: Sovereign Resilient Enterprise Pattern
Isolated Tenant Storage Enclave
Per-client storage boundary for raw payloads, intermediate artefacts and outputs, so no tenant data is co-mingled or reachable across engagements.
from: Sovereign Resilient Enterprise Pattern
Zero-Trust Ingestion Gateway
Authenticated, policy-checked entry point for client payloads; enforces tenant identity, schema validation and rate limits before any data reaches an inference path.
from: Sovereign Resilient Enterprise Pattern
Local Perimeter Execution (MCP)
Execution agents run inside the corporate perimeter and reach tools through the Model Context Protocol instead of shipping raw records to third-party model endpoints. Context is scoped to the minimum attributes the task needs, which is how data minimisation (GDPR Art. 5(1)(c)) and Art. 25 privacy-by-design survive multi-tool agent orchestration.
from: Sovereign Resilient Enterprise Pattern
Delivery Stack & Pipeline Stage (12)
Service-as-a-Software delivery: the engines, patterns and artifacts this workflow needs on top of the generic obligations. See the full pipeline
Supervisor Attribution Chain
Every model inference, data interaction and client-facing artefact is bound to an authorised supervising natural person — never to a shared service account. Required for SEC Rule 204-2 attribution, SOX segregation of duties and AI Act Art. 26 deployer oversight records.
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.
pipeline stage 4 — Output audit & human-in-the-loop gateway
Kill Switch / Graceful Degradation
Operator stop controls and degraded-mode fallbacks; real-time override (veto) channels for HOTL operation.
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.
pipeline stage 4 — Output audit & human-in-the-loop gateway
Bias Testing & Data Quality Pipeline
Representativeness checks, bias metrics and mitigation per ISO/IEC 5259; versioned datasets with lineage.
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.
Non-Human Identity Credential Broker
Issues ephemeral, per-task, narrowly scoped credentials to agents and revokes them on task completion, escalation or anomaly — the 'no standing credentials' principle in a component. Distinct from the credential vault (which holds long-lived secrets centrally): the broker's product is a credential that expires before it can be exfiltrated and reused.
Agent Memory Record Store
Agent conversational and working memory managed as a books-and-records object rather than a cache: retention schedules per regime, immutability where records rules demand it, deletion paths for erasure requests that do not break the audit trail, and export in a form a supervisor can read. Where an agent's memory carries a business communication or a decision rationale, it is a record — the storage tier does not decide that.
Materiality-Threshold Escalation
Autonomy is bounded by pre-configured limits — variance thresholds, disbursement caps, margin floors, confidence minima. Crossing a limit halts execution and routes the case to a named human with the synthesised context, rather than letting the agent proceed at degraded confidence.
Cognitive Orchestrator
The reasoning and control plane of an agentic workflow: goal decomposition, tool selection across enterprise APIs, confidence scoring per step, and a human-machine interface exposing progress, limitations and a global halt. It is the architectural home of AI Act Art. 14 oversight — oversight that lives only in a downstream UI cannot stop an executing agent.
Three Lines of Defense Separation
Structural separation between first-line runtime enforcement (owned by business/engineering: guardrail proxies, gateways, policy enforcement points) and second-line governance (independent GRC platform owned by risk/legal/compliance: inventory, tiering, policy definition, audit evidence), with third-line internal audit sampling both. Telemetry, drift scores and anomaly events flow first→second line as the feedback loop. Anti-pattern: coupling governance into a runtime or model vendor's stack — it blinds oversight to the rest of the estate and fails the independence test auditors apply.
Shadow-Mode Execution
Run governance controls in observe-and-score mode before enforcement: the policy engine and guardrails evaluate every agent action and log verdicts without blocking, yielding empirical false-positive/negative rates and calibrated thresholds. De-risks the enforcement cutover, produces baseline evidence for Art. 9 risk estimation, and is the standard migration path when retrofitting controls onto a live workflow.
Build or Buy — Vendor Layer (13)
The graph models vendor CATEGORIES as first-class nodes and keeps named vendors as community-maintained, disputable desc content with lastVerified dates. A category is stable; a vendor list is a currency-layer object like any standard node.
Regulated Foundation-Model Platforms
Frontier commercial APIs and open-weight models under enterprise controls: zero-data-retention tiers, data isolation, fine-tuning governance, safety alignment documentation, EU-sovereign options. Named products live in marketExamples, where the deployment model is recorded in the hosting field rather than asserted in prose. What the class buys you: a model supply relationship with contractual data handling and documentation you can pass to a customer. GPAI-chapter duties and provider due diligence attach at this layer. Selection metrics: see meta.marketLandscape.selectionMetrics.models.
unverified · verified 2026-08-18 community-maintained
selection metrics: ZDR enterprise tiers, data isolation, EU-sovereign options, fine-tuning controls, safety alignment documentation
supplies: Synthetic-Content Labelling / Watermarking
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 |
|---|---|---|---|---|
| OpenAI (Enterprise / API) | proprietary frontier | Enterprise tiers offer zero-data-retention and no-training commitments over the commercial API. Typical: general copilots, document reasoning. | not checked | SOC 2 (claimed)ISO 27001 (claimed)zero-data-retention tier (claimed)GDPR-positioned |
| Anthropic Claude (Enterprise) | proprietary frontier | Enterprise/ZDR tiers with published safety and model documentation practice. Typical: regulated assistants, long-context analysis. | not checked | SOC 2 (claimed)ISO 27001 (claimed)zero-data-retention tier (claimed)HIPAA-eligible (claimed) |
| Google Gemini Enterprise | proprietary frontier | Vertex-hosted frontier models with regional grounding and customer-managed keys. Typical: enterprise search, multimodal workflows. | not checked | SOC 2 (claimed)ISO 27001 (claimed)HIPAA-eligible (claimed)EU data-boundary positioning |
| Cohere | proprietary frontier | Private-cloud and on-prem deployment of retrieval-oriented models. Typical: private RAG, enterprise search. | self-hostable | SOC 2 (claimed) |
and 7 more in the stack advisor →
Community-maintained, disputable examples — not an endorsement and not a ranking. Alignments are as claimed by vendors or the source compilation, not verified by RAIN; a certification is shown as a certification only where a certificate or registry reference is recorded.
Disclosure: RAI·N·avigator operates in this category too, so we have a commercial interest in any comparison here. That is why this layer maps product classes to control objectives and lists named products as community-maintained examples — we publish no rankings, no quadrants and no coverage assertions about any vendor, including ourselves.
Runtime Security & Guardrail Vendors
First-line inline enforcement: single-pass parallel input/output evaluation proxies, injection and exfiltration defense, PII masking, grounding checks, SecOps routing. Named products live in marketExamples; prose here describes the class. What the class buys you: a policy decision point in the request path that fails closed and emits telemetry an auditor can read. Selection metrics: single-pass latency (<20 ms class), catch rates, policy-version telemetry into the AI-BOM. Consolidation matters commercially: a guardrail acquired by a platform vendor tends to follow that platform's roadmap, which is a lock-in question rather than a security one — reported acquisitions are recorded per entry as reported, not asserted here.
unverified · verified 2026-08-18 community-maintained
selection metrics: single-pass parallel evaluation latency (<20 ms class), injection/hallucination catch rates, SecOps/SIEM routing, policy versioning surfaced into the AI-BOM
supplies: Interface Transparency & Content-Marking Layer · Output Rails / Groundedness Check
Filters to self-hostable, customer-VPC and open-source options when personal or confidential data cannot leave the EU.
| Example | Sub-category | What it does | Hosting | Claimed alignments |
|---|---|---|---|---|
| Lakera | guardrail proxy | Inline prompt-injection and content detection at request time. Typical: injection defence, content filtering. | not checked | SOC 2 (claimed)supports Art. 15 robustness measures (claimed) |
| HiddenLayer | model/agent detection & response | Model-layer detection and response with adversarial-attack telemetry. Typical: model threat detection, red-team telemetry. | not checked | SOC 2 (claimed)supports Art. 15 robustness measures (claimed) |
| Palo Alto Prisma AIRS | network-integrated AI security | AI runtime security folded into an existing enterprise network security estate. Typical: enterprise rollout, egress control. | not checked | SOC 2 (claimed)enterprise security integration (claimed) |
| Cisco AI Defense | network-integrated AI security | Discovery of AI usage plus inline enforcement across the corporate network. Typical: shadow-AI discovery, inline enforcement. | not checked | enterprise security integration (claimed) |
and 4 more in the stack advisor →
Community-maintained, disputable examples — not an endorsement and not a ranking. Alignments are as claimed by vendors or the source compilation, not verified by RAIN; a certification is shown as a certification only where a certificate or registry reference is recorded.
Disclosure: RAI·N·avigator operates in this category too, so we have a commercial interest in any comparison here. That is why this layer maps product classes to control objectives and lists named products as community-maintained examples — we publish no rankings, no quadrants and no coverage assertions about any vendor, including ourselves.
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: Interface Transparency & Content-Marking Layer
Filters to self-hostable, customer-VPC and open-source options when personal or confidential data cannot leave the EU.
| Example | Sub-category | What it does | Hosting | Claimed alignments |
|---|---|---|---|---|
| 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.
Agentic Execution Governance
The youngest tier: governance of what an agent is allowed to do at execution time — non-human identity, per-task scoping, action approval, agent inventory and agent-level red-teaming. Named products live in marketExamples; prose here describes the class. Because the category is new, capability claims outrun deployments: ask for a reference in your own regime before believing a control is covered, and treat entries with limited public verification as unconfirmed. Selection metrics: see meta.marketLandscape.selectionMetrics.agentgov.
unverified · verified 2026-08-18 community-maintained
selection metrics: non-human identity inventory completeness, credential time-to-live and revocation latency, per-action approval hooks, agent-level red-team coverage, evidence export a 2nd line can read, deployment references in your regime
supplies: Agent Discovery & Registry Endpoint
Filters to self-hostable, customer-VPC and open-source options when personal or confidential data cannot leave the EU.
| Example | Sub-category | What it does | Hosting | Claimed alignments |
|---|---|---|---|---|
| Pillar Security | agent security & inventory | Discovery, inventory and runtime policy for agents in the estate. Typical: agent registry, policy enforcement. | not checked | agent-inventory positioning |
| Lyzr | agent governance & observability | Agent platform with governance, approval and observability features. Typical: agent approval, agent analytics. | not checked | vendor-stated security posture |
| Astrix Security | non-human identity | Lifecycle governance of machine and agent identities and their grants. Typical: credential scoping, NHI inventory. | not checked | SOC 2 (claimed)NHI governance positioning |
| Britive | just-in-time access | Ephemeral, per-task privileges instead of standing credentials. Typical: JIT credentials, privilege reduction. | not checked | SOC 2 (claimed)least-privilege positioning |
Community-maintained, disputable examples — not an endorsement and not a ranking. Alignments are as claimed by vendors or the source compilation, not verified by RAIN; a certification is shown as a certification only where a certificate or registry reference is recorded.
Disclosure: RAI·N·avigator operates in this category too, so we have a commercial interest in any comparison here. That is why this layer maps product classes to control objectives and lists named products as community-maintained examples — we publish no rankings, no quadrants and no coverage assertions about any vendor, including ourselves.
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.
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: Agent Memory Record Store · Data Lineage & Versioning · Sovereign Context Layer · Local Perimeter Execution (MCP)
Filters to self-hostable, customer-VPC and open-source options when personal or confidential data cannot leave the EU.
| Example | Sub-category | What it does | Hosting | Claimed alignments |
|---|---|---|---|---|
| Azure AI Search | managed retrieval | Managed hybrid search with security trimming against tenant identities. Typical: ACL-aware RAG, enterprise search. | not checked | ISO 27001 (claimed)SOC 2 (claimed) |
| Databricks Unity Catalog | governed lakehouse | Catalog and lineage spanning tables, features and RAG chunks. Typical: lineage evidence, governed RAG. | not checked | SOC 2 (claimed)lineage/Art. 10 support (claimed) |
| Relyance AI | code-level data & AI lineage | Parses source repositories to map data and inference flows at code level, with CI checks on changes to those flows. Typical: data lineage, shift-left privacy review. Scope overlap: Its AI-governance reporting scope overlaps this platform's own; we have a commercial interest in the comparison. | SaaS (vendor cloud) | GDPR programme tooling (claimed)EU AI Act readiness positioning |
| Snowflake Cortex | governed lakehouse | Model calls inside the warehouse boundary with masking and clean rooms. Typical: in-warehouse inference, governed analytics. | not checked | SOC 2 (claimed)ISO 27001 (claimed)HIPAA-eligible (claimed) |
Community-maintained, disputable examples — not an endorsement and not a ranking. Alignments are as claimed by vendors or the source compilation, not verified by RAIN; a certification is shown as a certification only where a certificate or registry reference is recorded.
Disclosure: RAI·N·avigator operates in this category too, so we have a commercial interest in any comparison here. That is why this layer maps product classes to control objectives and lists named products as community-maintained examples — we publish no rankings, no quadrants and no coverage assertions about any vendor, including ourselves.
Grounding, Retrieval & Agent Memory
The grounding layer between raw sources and the model: document parsers, embedding models, vector databases and — new in the agentic era — persistent agent memory stores. Memory is the hard part: once a personal fact is embedded, GDPR Art. 17 erasure has to reach the vector and the memory record, not just the source row, and embeddings are partially reconstructable (see IronCore in the privacy layer). Retrieval quality is also a data-governance question under Art. 10: what got parsed, chunked and indexed is what the system 'knows'.
unverified · verified 2026-08-18 community-maintained
selection metrics: Parsing fidelity on your worst document class; retrieval precision/recall on a labelled set; tenant and ACL isolation model; per-vector encryption and erasure path; memory TTL and record semantics; self-host option.
supplies: Agent Memory Record Store · Data Lineage & Versioning · Bitemporal Memory (GDPR×Art.12) · Isolated Tenant Storage Enclave
Filters to self-hostable, customer-VPC and open-source options when personal or confidential data cannot leave the EU.
| Example | Sub-category | What it does | Hosting | Claimed alignments |
|---|---|---|---|---|
| Docling | document parser | Open-source layout-aware parsing of PDFs and office formats into structured chunks. Typical: RAG ingestion, air-gapped pipelines. | self-hostable | EU sovereignty positioning |
| LlamaParse | document parser | Managed parsing service tuned for tables and complex documents feeding RAG. Typical: RAG ingestion, table extraction. | not checked | SOC 2 (claimed) |
| Amazon Textract | document parser | OCR and form/table extraction with per-page pricing inside AWS. Typical: document intake, claims processing. | not checked | SOC 2 (claimed)HIPAA-eligible (claimed)ISO 27001 (claimed) |
| Diffbot | web/knowledge extraction | Structured extraction and knowledge-graph construction from web sources. Typical: market monitoring, entity resolution. | not checked | vendor-stated security posture |
and 12 more in the stack advisor →
Community-maintained, disputable examples — not an endorsement and not a ranking. Alignments are as claimed by vendors or the source compilation, not verified by RAIN; a certification is shown as a certification only where a certificate or registry reference is recorded.
Disclosure: RAI·N·avigator operates in this category too, so we have a commercial interest in any comparison here. That is why this layer maps product classes to control objectives and lists named products as community-maintained examples — we publish no rankings, no quadrants and no coverage assertions about any vendor, including ourselves.
Sovereign Infrastructure
Compute and storage under EU jurisdictional control. Two structurally different offers, and the difference is the decision: native EU providers give full jurisdictional isolation with narrower service catalogs and thinner managed-AI tooling; hyperscaler sovereign constructions give the broad catalog with contractual and operational isolation, where the residual question is the control plane, support access and operational metadata rather than the data plane. Named offers live in marketExamples, which is the single source of truth for this layer — prose here describes the class, not the field. Claimed alignments recorded per entry: positioning for BSI C5 / C3A and ANSSI SecNumCloud attestation, NIS2 and DORA third-party requirements. Nothing here is an endorsement, and no provider in this category is 'CLOUD-Act-proof' by label alone — ask who holds the keys and who administers the plane.
unverified · verified 2026-08-18 community-maintained
selection metrics: jurisdiction of the control plane (not only the data plane), operator nationality and support-access paths, key custody, C5 / C3A / SecNumCloud attestation scope, managed-AI service depth vs. isolation trade-off, exit and repatriation terms
supplies: Multi-Region Failover & Resilience Testing · Sovereign Context Layer
Filters to self-hostable, customer-VPC and open-source options when personal or confidential data cannot leave the EU.
| Example | Sub-category | What it does | Hosting | Claimed alignments |
|---|---|---|---|---|
| OVHcloud | native EU | French provider with EU-only jurisdiction and a narrower managed-AI catalog than the hyperscalers. Typical: EU-resident inference, regulated workload hosting. | not checked | ISO 27001 (claimed)SecNumCloud-positionedGDPR-positioned |
| Scaleway | native EU | EU-operated cloud with GPU instances and managed inference under French corporate control. Typical: EU-resident inference, fine-tuning. | not checked | ISO 27001 (claimed)GDPR-positioned |
| STACKIT | native EU | German provider (Schwarz Group) positioned for data residency in Germany. Typical: public sector, retail data platforms. | not checked | C5-positionedGDPR-positioned |
| AWS European Sovereign Cloud | sovereign hyperscaler | Separately operated EU region set with EU-resident personnel and keys; full hyperscaler catalog. Typical: large-scale enterprise AI, regulated hosting. | not checked | ISO 27001 (claimed)SOC 2 (claimed)EU data-boundary positioning |
and 11 more in the stack advisor →
Community-maintained, disputable examples — not an endorsement and not a ranking. Alignments are as claimed by vendors or the source compilation, not verified by RAIN; a certification is shown as a certification only where a certificate or registry reference is recorded.
Disclosure: RAI·N·avigator operates in this category too, so we have a commercial interest in any comparison here. That is why this layer maps product classes to control objectives and lists named products as community-maintained examples — we publish no rankings, no quadrants and no coverage assertions about any vendor, including ourselves.
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: Vendor & Model Due-Diligence Kit
Filters to self-hostable, customer-VPC and open-source options when personal or confidential data cannot leave the EU.
| Example | Sub-category | What it does | Hosting | Claimed alignments |
|---|---|---|---|---|
| Credo AI | AI governance platform | Policy packs, risk tiering and evidence workflows mapped across frameworks. Typical: AI registry, policy administration. Scope overlap: Its scope overlaps this platform's own; we have a commercial interest in the comparison. | not checked | ISO 42001 alignment (claimed)EU AI Act readiness positioning |
| Holistic AI | AI governance & audit | Risk assessment, bias auditing and regulatory reporting workflows. Typical: bias audit, regulatory reporting. Scope overlap: Its scope overlaps this platform's own; we have a commercial interest in the comparison. | not checked | NYC LL144 audit support (claimed)EU AI Act readiness positioning |
| IBM watsonx.governance | AI governance platform | Governance, factsheets and monitoring integrated with the IBM stack. Typical: factsheets, model monitoring. Scope overlap: Its scope overlaps this platform's own; we have a commercial interest in the comparison. | not checked | ISO 42001 alignment (claimed)Art. 11 documentation support (claimed) |
| ModelOp | AI/model governance | Model and agent inventory with automated lifecycle controls for large estates. Typical: model inventory, control automation. Scope overlap: Its scope overlaps this platform's own; we have a commercial interest in the comparison. | not checked | model-risk positioning (SR 11-7 style, claimed)ISO 42001 alignment (claimed) |
and 3 more in the stack advisor →
Community-maintained, disputable examples — not an endorsement and not a ranking. Alignments are as claimed by vendors or the source compilation, not verified by RAIN; a certification is shown as a certification only where a certificate or registry reference is recorded.
Disclosure: RAI·N·avigator operates in this category too, so we have a commercial interest in any comparison here. That is why this layer maps product classes to control objectives and lists named products as community-maintained examples — we publish no rankings, no quadrants and no coverage assertions about any vendor, including ourselves.
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: Explainability API (SHAP/LIME/CoT) · OpenTelemetry / FCoT Tracing · Shadow-Mode Execution
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.
Confidential Computing & Privacy Engines
Data-in-use protection and pre-model privacy interception: enclave and runtime encryption, key management, tokenisation vaults, PII detection and redaction, application-layer and vector encryption. Named products live in marketExamples; prose here describes the class. Select on: enclave attestation support, key custody model (external HSM / BYOK), detokenisation audit trail, latency added per call, and coverage of the identifier classes your regime actually names. Selection metrics: see meta.marketLandscape.selectionMetrics.privacy.
unverified · verified 2026-08-18 community-maintained
selection metrics: enclave attestation support, key custody (external HSM / BYOK), detokenisation audit trail, added latency per call, coverage of the identifier classes your regime names, in-boundary deployment option
supplies: Confidential Computing Enclaves · BYOK via External HSM
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.
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: Materiality-Threshold Escalation · Cognitive Orchestrator
Filters to self-hostable, customer-VPC and open-source options when personal or confidential data cannot leave the EU.
| Example | Sub-category | What it does | Hosting | Claimed alignments |
|---|---|---|---|---|
| LangChain / LangGraph | agent framework | Graph-structured agent runtime; interrupt/pause nodes support implementing human approval at defined steps. Typical: multi-step agents, approval workflows. | not checked | supports implementing Art. 14 oversight (claimed)supports Art. 12 step logging (claimed) |
| LlamaIndex | RAG framework | Indexing and query abstractions over documents and structured sources. Typical: enterprise RAG, document agents. | open source | retrieval-governance positioning |
| Microsoft AutoGen | multi-agent framework | Conversational multi-agent patterns with pluggable tool executors. Typical: multi-agent research, code agents. | not checked | research/OSS, no vendor certification |
| CrewAI | multi-agent framework | Role-based agent teams with task delegation and process templates. Typical: process automation, role-based agents. | not checked | vendor-stated security posture |
and 6 more in the stack advisor →
Community-maintained, disputable examples — not an endorsement and not a ranking. Alignments are as claimed by vendors or the source compilation, not verified by RAIN; a certification is shown as a certification only where a certificate or registry reference is recorded.
Disclosure: RAI·N·avigator operates in this category too, so we have a commercial interest in any comparison here. That is why this layer maps product classes to control objectives and lists named products as community-maintained examples — we publish no rankings, no quadrants and no coverage assertions about any vendor, including ourselves.
Runtime Guardrails & Enforcement
Policy enforcement in the request path: input/output validation, injection and exfiltration defence, structured-output constraints and action blocking. Distinct from observability layers because these products are in-line and can refuse. Selection questions: added latency at p95, whether enforcement is fail-open or fail-closed, whether policies are versioned artefacts, and whether the layer can be self-hosted inside your data boundary.
unverified · verified 2026-08-18 community-maintained
selection metrics: Where enforcement sits (inline proxy, sidecar, SDK) and the added latency at your token volumes; whether policy is versioned and testable as code; fail-open vs. fail-closed behaviour under guardrail outage; language and modality coverage; whether every block writes an evidence record you can cite later.
supplies: Output Rails / Groundedness Check
Filters to self-hostable, customer-VPC and open-source options when personal or confidential data cannot leave the EU.
| Example | Sub-category | What it does | Hosting | Claimed alignments |
|---|---|---|---|---|
| Guardrails AI | validation framework | Open-source validator framework for structured output and content policies in the request path. Typical: output validation, structured output. | open source | supports Art. 15 robustness measures (claimed) |
| NVIDIA NeMo Guardrails | dialogue policy rails | Programmable dialogue and topic rails placed around an LLM application. Typical: topic control, dialogue policy. | open source | supports Art. 50 interaction disclosure patterns (claimed) |
| Lakera AI | guardrail proxy | Inline prompt-injection and content detection at request time. Typical: injection defence, content filtering. | SaaS (vendor cloud) | SOC 2 (claimed)supports Art. 15 robustness measures (claimed) |
| Credal AI | enterprise access & policy layer | Permission-aware access layer with data-loss controls in front of enterprise assistants. Typical: access control, DLP. | SaaS (vendor cloud) | SOC 2 (claimed) |
Community-maintained, disputable examples — not an endorsement and not a ranking. Alignments are as claimed by vendors or the source compilation, not verified by RAIN; a certification is shown as a certification only where a certificate or registry reference is recorded.
Disclosure: RAI·N·avigator operates in this category too, so we have a commercial interest in any comparison here. That is why this layer maps product classes to control objectives and lists named products as community-maintained examples — we publish no rankings, no quadrants and no coverage assertions about any vendor, including ourselves.
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
Cascading Multi-Agent Failure
One agent's erroneous intermediate output (hallucination, goal drift from the assigned objective over multi-step plans, poisoned context) propagates unchecked through downstream agents and triggers automated cascade decisions — emergent behavior no single-agent review ever approved, with unclear liability boundaries between agent operators. Grows with orchestration depth (central orchestrator vs decentralized message bus) and autonomy tier.
mitigate with: Guardian Agents (Runtime Policy Enforcement), Watchdog Supervisor & Rate Limiting, Per-Action Autonomy Tiering, Shadow-Mode Execution, Versioned Model Registry with Rollback, Cyber Event Recovery Plan & Playbooks
LLM09 Misinformation
Hallucinated or wrong outputs create liability and decision risk.
mitigate with: Output Rails / Groundedness Check, Explainability API (SHAP/LIME/CoT)
Demographic Bias in Automated Screening
Name, language or geography features act as proxies for protected characteristics, producing systematically different outcomes across groups.
mitigate with: Bias Testing & Data Quality Pipeline, Algorithmic Bias & Fairness Audit Report
LLM06 Excessive Agency
Over-broad rights/functions of autonomous agents lead to uncontrolled actions.
mitigate with: MCP Gateway / Proxy, Agentic Zero Trust, Per-Action Autonomy Tiering, Trinity Defense (TCB + Command Gates + IFC), Deterministic Policy Engine (OPA / Cedar), Guardian Agents (Runtime Policy Enforcement), Non-Human Identity Credential Broker, Tool-Use Boundary Proxy