Housing & real estate
AI Real Estate Transaction Closing & Settlement
Minimal RiskUnverifiedDiscuss / dispute
AI systems that perform title search/examination, generate closing and conveyancing documents (deeds, closing disclosures, settlement statements), and direct escrow/settlement fund disbursement for residential real-estate purchase transactions.
Consensus classification rationale: AI-generated conveyancing documents risk crossing into the unauthorized practice of law in roughly half of US states that reserve deed drafting and closing conduct to licensed attorneys or attorney-supervised staff. Entities performing settlement services for a federally related mortgage loan remain subject to RESPA/Regulation X's Section 8 anti-kickback and unearned-fee prohibitions regardless of AI use in the workflow. Non-financed transfers of residential property to legal entities or trusts implicate FinCEN's Residential Real Estate reporting regime. In the EU, real-estate agents intermediating a sale (or a letting above EUR 10,000/month) are obliged entities under the AML Regulation and must complete customer due diligence before any funds or property transfer. An AI system that autonomously finalizes closing instruments or disburses escrow funds without licensed-professional review carries both professional-licensing exposure and AML compliance exposure distinct from generic document intelligence or KYC use cases.
This profile is incomplete:no standards mapped. That is a gap in the graph, not a statement that nothing applies — propose the missing links →
Decision attributes in force
Autonomyhuman-in-the-loop — Drives the human-oversight duties (Art. 14, Art. 26(2)) and Art. 50 disclosure.
Profiling of natural personsno — Feeds the Art. 6(3) second-subparagraph override directly — profiling makes the derogation categorically unavailable.
Affected subjectsnatural-person — Instruments scoped to natural persons drop out of scope when only legal entities are assessed.
Deployer typeprivate-enterprise — Selects between the recorded alternate classification readings.
Role in the value chainboth — Splits provider duties, deployer duties and upstream GPAI duties.
Consequential scoringno — Consequential scoring of natural persons requires intrinsic interpretability, not post-hoc explanation only.
Indicative decision support, not legal advice. Risk classification depends on your concrete deployment context and can change with scope drift — validate the result with qualified counsel.
Target market(s)European UnionUnited States (federal)change
Changes which instruments below count as in scope for this profile.
Target markets: European Union, United States (federal)
Regulatory footprint
4 instruments across 2 of 7 regulatory domains- AI lawnone triggered
- Data protectionnone triggered
- Cyber & resiliencenone triggered
- Online safety & platformsnone triggered
- Product safetynone triggered
- Financial services2 instruments
- Sector & employment2 instruments
- Standardsnone triggered
By jurisdiction
- EU1European UnionAML Package (AMLR/AMLA)
- US3United States (federal)Bank Secrecy Act / FinCEN Program Rules, RESPA / Regulation X (Settlement Services), State Unauthorized Practice of Law Restrictions on Real-Estate Closings (US)
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: Robust
Every hop of this derivation rests on a primary source with a recently verified status. Read it as a defensible starting position, still not legal advice.
Computed weakest-link over 7 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 band5 factors lowered the band — each links to the claim behind it
- Source tier: State Unauthorized Practice of Law Restrictions on Real-Estate Closings (US) carries no resolvable citation — the claim is uncited. open node →
- Source tier: AML Package (AMLR/AMLA) carries no resolvable citation — the claim is uncited. open node →
- Verification age: Bank Secrecy Act / FinCEN Program Rules has no recorded verification date. open node → primary source →
- Verification age: Art. 4 — AI Literacy has no recorded verification date. open node → primary source →
- Verification age: Art. 25 — Value Chain / Role Flip has no recorded verification date. open node → primary source →
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.
- 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
- RESPA / Regulation X (Settlement Services): Criminal: fine up to $10,000 and/or up to 1 year imprisonment per Section 8 violation (12 U.S.C. 2607(d)(1)); civil: private treble-damages liability plus attorney's fees; CFPB may also pursue civil money penalties.
- State Unauthorized Practice of Law Restrictions on Real-Estate Closings (US): Class 1 misdemeanor under N.C. Gen. Stat. 84-8 (representative state penalty); civil injunctions, fee disgorgement, and potential invalidity of closing documents; a supervising attorney who improperly delegates closing responsibility risks professional discipline.
- Bank Secrecy Act / FinCEN Program Rules: Civil money penalties per violation plus criminal exposure for willful program failures.
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, Art. 25 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 (Zero-Data-Retention Vendor Binding, Agent Discovery & Registry Endpoint, Identity & Sanctions Screening Engine) 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 standards are mapped yet.
Classification precedent
Consensus reading: Minimal Risk open in the graph →
AI-generated conveyancing documents risk crossing into the unauthorized practice of law in roughly half of US states that reserve deed drafting and closing conduct to licensed attorneys or attorney-supervised staff. Entities performing settlement services for a federally related mortgage loan remain subject to RESPA/Regulation X's Section 8 anti-kickback and unearned-fee prohibitions regardless of AI use in the workflow. Non-financed transfers of residential property to legal entities or trusts implicate FinCEN's Residential Real Estate reporting regime. In the EU, real-estate agents intermediating a sale (or a letting above EUR 10,000/month) are obliged entities under the AML Regulation and must complete customer due diligence before any funds or property transfer. An AI system that autonomously finalizes closing instruments or disburses escrow funds without licensed-professional review carries both professional-licensing exposure and AML compliance exposure distinct from generic document intelligence or KYC use cases.
What the reading rests on — the provisions this classification actually pulls in:
- Art. 4 — AI Literacy
- RESPA / Regulation X (Settlement Services) (Real Estate Settlement Procedures Act, 12 U.S.C. 2601-2617, as implemented by Regulation X, 12 CFR Part 1024)
- State Unauthorized Practice of Law Restrictions on Real-Estate Closings (US) (State unauthorized-practice-of-law (UPL) statutes and bar authorized-practice opinions restricting preparation of conveyancing instruments and conduct of real-estate closings to licensed attorneys or attorney-supervised staff (e.g. N.C. Gen. Stat. 84-4 to 84-8; NC State Bar Authorized Practice Advisory Opinion 2002-1))
- Bank Secrecy Act / FinCEN Program Rules (31 U.S.C. §5311 et seq. / 31 CFR Chapter X)
- AML Package (AMLR/AMLA)
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 (4)
RESPA / Regulation X (Settlement Services) (Real Estate Settlement Procedures Act, 12 U.S.C. 2601-2617, as implemented by Regulation X, 12 CFR Part 1024)
Federal law governing 'settlement services' (title search/examination, abstract preparation, document preparation, escrow, attorney services) for federally related mortgage loans; prohibits kickbacks/unearned referral fees (Section 8) and mandates timely disclosure of settlement costs.
Sanctions: Criminal: fine up to $10,000 and/or up to 1 year imprisonment per Section 8 violation (12 U.S.C. 2607(d)(1)); civil: private treble-damages liability plus attorney's fees; CFPB may also pursue civil money penalties.
State Unauthorized Practice of Law Restrictions on Real-Estate Closings (US) (State unauthorized-practice-of-law (UPL) statutes and bar authorized-practice opinions restricting preparation of conveyancing instruments and conduct of real-estate closings to licensed attorneys or attorney-supervised staff (e.g. N.C. Gen. Stat. 84-4 to 84-8; NC State Bar Authorized Practice Advisory Opinion 2002-1))
in-force · verified 2026-09-18 in force US
In roughly half of US states, drafting deeds and other conveyancing instruments, or conducting a residential real-estate closing, is reserved to licensed attorneys and constitutes the unauthorized practice of law when performed by a non-lawyer (or an unsupervised AI system) without attorney review; other states permit escrow/title-agent closings.
Sanctions: Class 1 misdemeanor under N.C. Gen. Stat. 84-8 (representative state penalty); civil injunctions, fee disgorgement, and potential invalidity of closing documents; a supervising attorney who improperly delegates closing responsibility risks professional discipline.
Bank Secrecy Act / FinCEN Program Rules (31 U.S.C. §5311 et seq. / 31 CFR Chapter X)
US AML regime: risk-based CDD/beneficial-ownership identification, sanctions and PEP screening, SAR filing within 30 days, and five-year record retention for automated screening decisions.
Sanctions: Civil money penalties per violation plus criminal exposure for willful program failures.
AML Package (AMLR/AMLA)
in-force · verified 2026-09-05 in force EU
Customer due diligence, perpetual monitoring and AMLA supervisory expectations for AI-driven KYC/AML systems.
Legal Obligations (2)
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.
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 (0)
obligation (article) → operationalized_by → control objective → satisfied_by → component/pattern; control objective → evidenced_by → evidence artifact
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
No harmonised standard is mapped to this use case yet.
Evidence you will need (4)
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 (1)
A structured judgement about risk, rights or a management system.
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
Test reports (1)
Measured results from testing, evaluation or red-teaming.
Accuracy, Robustness & Red-Teaming Reportspractice-derived — dispute welcomeserves 2 obligations
Art. 15 evidence: declared accuracy metrics, adversarial and corruption robustness results (DIN SPEC 92001-2, ISO 24029), penetration and jailbreak-resistance testing, groundedness evaluation scores.
verifiability: independently-attested
chain: Art. 15 — Accuracy, Robustness, Cybersecurity · Art. 15 — Accuracy, Robustness, Cybersecurity → CO: Adversarial Robustness Verified · Enterprise SDLC Code Automation & QA · LLM01 Prompt Injection
Log records (1)
Machine-generated records produced while the system runs.
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
Process records (1)
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
Architecture Blueprint
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.
Guarded RAG Pattern
For limited-risk conversational/generative systems: deterministic RAG with input rails (DLP/NER, injection blocking), retrieval rails (relevance, freshness, ACL) and output rails (groundedness check with deterministic fallback), plus synthetic-content labelling.
Required Technical Components (20)
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
Identity & Sanctions Screening Engine
Beneficial-ownership resolution, sanctions/PEP list matching and alert scoring with tunable thresholds and full match-evidence capture.
from: Bank Secrecy Act / FinCEN Program Rules
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: Bank Secrecy Act / FinCEN Program Rules · Human-in-the-Loop Core Pattern · Deterministic Document-Validation Pipeline
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: Human-in-the-Loop Core 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: 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: Human-in-the-Loop Core Pattern
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
Input Rails / Prompt Shields
Pre-model validation of user input: injection detection, topic blocking, encoding checks.
from: Guarded RAG Pattern
Retrieval Rails (ACL-aware RAG)
Relevance, freshness and per-user permission checks on every retrieved chunk; curated, versioned index.
from: Guarded RAG Pattern
Output Rails / Groundedness Check
Faithfulness scoring of answers against retrieved sources; deterministic fallback instead of hallucination; schema-validated structured output.
from: Guarded RAG Pattern
PII Scrubbing / DLP-NER Layer
Automated detection, pseudonymisation and blocking of personal data in inputs, retrievals and outputs.
from: Guarded RAG Pattern
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: Guarded RAG Pattern
Dual-Gate Validation Pipeline
Input and output validation as two independent gates (MLCommons-hazard-class semantic filters, groundedness checks, structural validators: LLM Guard sub-ms–10 ms, Llama Guard <90 ms, NeMo <50 ms, Guardrails AI 50–200 ms). Latency economics decide the architecture: sequential gate chains add 300–800 ms per agent action; parallel evaluation collapses total added latency to the slowest single check — run independent checks concurrently, reserve sequential ordering for true dependencies.
from: Guarded RAG Pattern
Build or Buy — Vendor Layer (12)
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.
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.
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: Explainability API (SHAP/LIME/CoT) · Bias Testing & Data Quality Pipeline · Confidence Scoring & Threshold Gate · AI Register & Model Registry / Factsheets · Dual-Gate Validation 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 |
|---|---|---|---|---|
| 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.
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: Document Intelligence Engine · Retrieval Rails (ACL-aware RAG)
Filters to self-hostable, customer-VPC and open-source options when personal or confidential data cannot leave the EU.
| Example | Sub-category | What it does | Hosting | Claimed alignments |
|---|---|---|---|---|
| Docling | document parser | Open-source layout-aware parsing of PDFs and office formats into structured chunks. Typical: RAG ingestion, air-gapped pipelines. | self-hostable | EU sovereignty positioning |
| LlamaParse | document parser | Managed parsing service tuned for tables and complex documents feeding RAG. Typical: RAG ingestion, table extraction. | not checked | SOC 2 (claimed) |
| Amazon Textract | document parser | OCR and form/table extraction with per-page pricing inside AWS. Typical: document intake, claims processing. | not checked | SOC 2 (claimed)HIPAA-eligible (claimed)ISO 27001 (claimed) |
| Diffbot | web/knowledge extraction | Structured extraction and knowledge-graph construction from web sources. Typical: market monitoring, entity resolution. | not checked | vendor-stated security posture |
and 12 more in the stack advisor →
Community-maintained, disputable examples — not an endorsement and not a ranking. Alignments are as claimed by vendors or the source compilation, not verified by RAIN; a certification is shown as a certification only where a certificate or registry reference is recorded.
Disclosure: RAI·N·avigator operates in this category too, so we have a commercial interest in any comparison here. That is why this layer maps product classes to control objectives and lists named products as community-maintained examples — we publish no rankings, no quadrants and no coverage assertions about any vendor, including ourselves.
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.
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: 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.
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.
Runtime Security & Guardrail Vendors
First-line inline enforcement: single-pass parallel input/output evaluation proxies, injection and exfiltration defense, PII masking, grounding checks, SecOps routing. Named products live in marketExamples; prose here describes the class. What the class buys you: a policy decision point in the request path that fails closed and emits telemetry an auditor can read. Selection metrics: single-pass latency (<20 ms class), catch rates, policy-version telemetry into the AI-BOM. Consolidation matters commercially: a guardrail acquired by a platform vendor tends to follow that platform's roadmap, which is a lock-in question rather than a security one — reported acquisitions are recorded per entry as reported, not asserted here.
unverified · verified 2026-08-18 community-maintained
selection metrics: single-pass parallel evaluation latency (<20 ms class), injection/hallucination catch rates, SecOps/SIEM routing, policy versioning surfaced into the AI-BOM
supplies: Input Rails / Prompt Shields · Output Rails / Groundedness Check · Dual-Gate Validation 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 |
|---|---|---|---|---|
| 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.
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: Input Rails / Prompt Shields · 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.
Secure Data Infrastructure & Vector Storage
Governed retrieval substrate: vector databases, lakehouses and catalogs with tenant/namespace isolation, RBAC and client-managed keys (CMEK), lineage into RAG chunks, air-gap options, and code-level data and AI lineage. Named products live in marketExamples; prose here describes the class. What the class buys you: retrieval that can be scoped per requester and traced back to a source record. The Art. 10 runtime data-governance duties land here. Selection metrics: see meta.marketLandscape.selectionMetrics.data.
unverified · verified 2026-08-18 community-maintained
selection metrics: namespace/tenant isolation, RBAC + CMEK, lineage into RAG chunks, SOC 2 / ISO 27001 attestations, air-gap capability
supplies: Retrieval Rails (ACL-aware RAG)
Filters to self-hostable, customer-VPC and open-source options when personal or confidential data cannot leave the EU.
| Example | Sub-category | What it does | Hosting | Claimed alignments |
|---|---|---|---|---|
| Azure AI Search | managed retrieval | Managed hybrid search with security trimming against tenant identities. Typical: ACL-aware RAG, enterprise search. | not checked | ISO 27001 (claimed)SOC 2 (claimed) |
| Databricks Unity Catalog | governed lakehouse | Catalog and lineage spanning tables, features and RAG chunks. Typical: lineage evidence, governed RAG. | not checked | SOC 2 (claimed)lineage/Art. 10 support (claimed) |
| Relyance AI | code-level data & AI lineage | Parses source repositories to map data and inference flows at code level, with CI checks on changes to those flows. Typical: data lineage, shift-left privacy review. Scope overlap: Its AI-governance reporting scope overlaps this platform's own; we have a commercial interest in the comparison. | SaaS (vendor cloud) | GDPR programme tooling (claimed)EU AI Act readiness positioning |
| Snowflake Cortex | governed lakehouse | Model calls inside the warehouse boundary with masking and clean rooms. Typical: in-warehouse inference, governed analytics. | not checked | SOC 2 (claimed)ISO 27001 (claimed)HIPAA-eligible (claimed) |
Community-maintained, disputable examples — not an endorsement and not a ranking. Alignments are as claimed by vendors or the source compilation, not verified by RAIN; a certification is shown as a certification only where a certificate or registry reference is recorded.
Disclosure: RAI·N·avigator operates in this category too, so we have a commercial interest in any comparison here. That is why this layer maps product classes to control objectives and lists named products as community-maintained examples — we publish no rankings, no quadrants and no coverage assertions about any vendor, including ourselves.
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.
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
LLM01 Prompt Injection
Direct or indirect (RAG/files/web) instructions override system prompts — the primary attack vector on the perception layer.
mitigate with: Input Rails / Prompt Shields, Guardrail Sidecar / Interception, Trinity Defense (TCB + Command Gates + IFC), Divided-Focus Memory Tiering, Dual-Gate Validation Pipeline
LLM02 Sensitive Info Disclosure
Leakage of PII, trade secrets or system prompts in outputs.
mitigate with: PII Scrubbing / DLP-NER Layer, Output Rails / Groundedness Check, PII/PHI Redaction & Tokenisation Engine
LLM09 Misinformation
Hallucinated or wrong outputs create liability and decision risk.
mitigate with: Output Rails / Groundedness Check, Explainability API (SHAP/LIME/CoT)
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