Turn an AI use case into its full regulatory footprint — every domain it touches, from AI law and data protection to cyber, product safety and sector rules — with the obligations, the architecture and the evidence you owe, in about two minutes.
Community-curated knowledge graph — every claim carries its citation across law, engineering and governance. Every change traceable →
Computer-vision defect detection on production lines.
Consensus classification rationale: Minimal risk unless it becomes a safety function under the Machinery Regulation (then Annex I high-risk); DIN SPEC 92001-2 robustness evidence either way.
This profile is incomplete:no threats modelled. That is a gap in the graph, not a statement that nothing applies — propose the missing links →
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
3 instruments across 2 of 7 regulatory domains, plus 3 standards references
AI lawnone triggered
Data protectionnone triggered
Cyber & resilience1 instrument
Online safety & platformsnone triggered
Product safety2 instruments
Financial servicesnone triggered
Sector & employmentnone triggered
Standards3 references
By jurisdiction
EU3European UnionCyber Resilience Act, General Product Safety Regulation, Machinery Regulation
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 8 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 band4 factors lowered the band — each links to the claim behind it
Source tier: ENISA Multilayer Framework & AI Threat Landscape carries no resolvable citation — the claim is uncited. open node →
Source tier: NIST SP 800-218 (SSDF) carries no resolvable citation — the claim is uncited. open node →
Verification age: ENISA Multilayer Framework & AI Threat Landscape has no recorded verification date. open node →
Verification age: NIST SP 800-218 (SSDF) has no recorded verification date. open node →
Compliance brief
This use case is minimal-risk under the EU AI Act (Minimal Risk); no product-specific obligations beyond general AI literacy apply.
What is owed
Art. 4.Providers and deployers must ensure sufficient AI literacy of staff dealing with AI systems.
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
Cyber Resilience Act:Up to €15m or 2.5% of worldwide annual turnover
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 obligations named above as active workstreams with an accountable owner.
Stand up the named oversight design — Mode 3 — with a documented human-review procedure.
Produce the technical documentation and evidence artefacts already mapped to this use case (SBOM & Dependency Management, Secure Boot & Hardened Runtime, Kill Switch / Graceful Degradation) 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.
Minimal risk unless it becomes a safety function under the Machinery Regulation (then Annex I high-risk); DIN SPEC 92001-2 robustness evidence either way.
What the reading rests on — the provisions this classification actually pulls in:
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 →
Security-by-design for products with digital elements over the full lifecycle: vulnerability management, patching, SBOM. Complements AI Act Art. 15 at product level.
Sanctions: Up to €15m or 2.5% of worldwide annual turnover
General Product Safety Regulation (Regulation (EU) 2023/988)
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
ENISA Multilayer Framework & AI Threat Landscape
Three-layer good-practice model (cyber foundations → AI-specific → sectoral) and lifecycle threat landscape — the operational base for Art. 15 and CRA.
unverified · no verification date
evidence for: Cyber Resilience Act
NIST SP 800-218 (SSDF)
Secure Software Development Framework: practices for provenance, review and vulnerability handling of generated and third-party code; SSDF-AI companion covers AI-assisted development.
unverified · no verification date
evidence for: Cyber Resilience Act
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.
The concrete deliverables this use case's obligations ask for — grouped by what kind of artifact they are. Documentation is the largest single conformity cost block, so the list is a work plan, not a reading list. Full evidence matrix →
Documents & files (1)
Written deliverables an authority or auditor can request as a file.
CRA evidence: software bill of materials incl. model weights and datasets, vulnerability handling and patch history — reused for NIS2 supply-chain security and DORA third-party registers.
verifiability: documented artefact — verifiable on inspection
Traces that a process actually happened, and who did it.
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
Operator stop controls and degraded-mode fallbacks; real-time override (veto) channels for HOTL operation.
from: Hardened Edge / IoT Pattern
Unified Incident-Response Runbook
One procedure reconciling AI Act Art. 73, GDPR Art. 33 (72h), DORA and NIS2 (24h/72h) timelines and recipients.
from: Hardened Edge / IoT Pattern
Build or Buy — Vendor Layer (1)
The graph models vendor CATEGORIES as first-class nodes and keeps named vendors as community-maintained, disputable desc content with lastVerified dates. A category is stable; a vendor list is a currency-layer object like any standard node.
AI Supply-Chain Security & AIBOM
Bills of materials for AI: base architecture, training-data dependencies, fine-tuning history and licence lineage of a model, plus scanning of third-party pretrained weights and model artifacts for backdoors, poisoning and tampering before ingestion. Adjacent to, but not the same as, software SBOM tooling — the unit of analysis is a weights artifact and its provenance. Selection metrics: see meta.marketLandscape.selectionMetrics.supplychain.
selection metrics: Whether the AIBOM records training-data and fine-tuning lineage or only package dependencies; artifact formats scanned (safetensors, pickle, GGUF, container images); detection basis for tampering and poisoning (signature, behavioural, provenance attestation) and its false-positive rate; support for signing and verifying weights in your own pipeline; whether ingestion can be blocked, not just reported.
supplies: SBOM & Dependency Management
Filters to self-hostable, customer-VPC and open-source options when personal or confidential data cannot leave the EU.
AI bill-of-materials generation, model-provenance capture and third-party model risk scanning. Typical: AIBOM, third-party model ingestion. Scope overlap: Its AI-governance reporting scope overlaps this platform's own; we have a commercial interest in the comparison.
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.
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.