Controls, regulatory requirements, and implementation
Find a control, see the requirements it supports, and follow practical steps to implement and test it. Start with a control or a regulation.
Controls and the requirements they support
More filters: collection and mapping basis
Selecting an instrument does not determine whether it applies to you. Check entity, role, intended use, exclusions, and timing against each source. EU AI Act is one instrument; the EU jurisdiction also includes GDPR and DORA.
169 matching items; 12 shown
H-GOV-01 · Handbook practice
Named mandate
Approve purpose, excluded uses, action classes, and accountable process owner.
Start here: Document the agent's purpose, operating boundary, permitted action classes, excluded uses, and process owner.
Evidence: Signed mandate and system boundary.
11 other instruments in this scope
H-GOV-02 · Handbook practice
Impact and legal scope
Assess affected people, consequences, jurisdictions, entity roles, and data before choosing autonomy.
Start here: Identify the intended use, affected people, consequential decisions, data, jurisdictions, and organizational roles.
Evidence: Impact register and scoped obligation decisions.
9 other instruments in this scope
H-GOV-03 · Handbook practice
Decision rights
Assign release, exception, halt, restart, correction, and retirement authority.
Start here: Document permitted decisions and name who can approve release, accept an exception, stop operation, and authorize restart.
Evidence: RACI, delegated limits, and decision records.
10 other instruments in this scope
H-GOV-04 · Handbook practice
Risk and exceptions
Record specific failure mechanisms, residual gaps, compensating controls, owners, and expiry.
Start here: Identify the intended use, affected people, consequential decisions, data, jurisdictions, and organizational roles.
Evidence: Risk register and expiring exception log.
7 other instruments in this scope
H-GOV-05 · Handbook practice
Training and adoption
Demonstrate operator and reviewer competence, workload capacity, and understanding of role limits.
Start here: Define competence requirements for operators and reviewers, including limitations and escalation rules.
Evidence: Training rubric, planted cases, staffing and burden evidence.
7 other instruments in this scope
H-DES-01 · Handbook practice
Configuration identity
Version model, harness, instructions, tools, permissions, memory, monitors, and fallback.
Start here: Register a stable system identity, owner, purpose, deployment environment, model version, tools, permissions, and lifecycle dates.
Evidence: Deployment manifest and production digest.
6 other instruments in this scope
H-DES-02 · Handbook practice
Independent authority
Enforce least privilege and resource authorization outside agent-editable policy.
Start here: List allowed actions and resources for each identity and context; issue the minimum permissions needed.
Evidence: Effect-path inventory, grants, and negative outcomes.
6 other instruments in this scope
H-DES-03 · Handbook practice
Data and source purpose
Define permitted sources, classifications, rights, freshness, tenant scope, and egress.
Start here: Register data sources, provenance, permitted purpose, access conditions, and an accountable steward.
Evidence: Source register, access decisions, and data-flow records.
5 other instruments in this scope
H-DES-04 · Handbook practice
Memory stewardship
Gate admission and reuse; retain provenance, derivative lineage, revocation, and deletion policy.
Start here: Record memory provenance, state versions, dependencies, and who may change or restore them.
Evidence: Admission records, lineage, quarantine and repair evidence.
3 other instruments in this scope
H-DES-05 · Handbook practice
Failure and recovery design
Specify behavior on monitor, policy, tool, audit, provider, and identity failures.
Start here: Identify dependency failures, partial actions, and the fallback or recovery behavior required for each.
Evidence: Dependency failure matrix and reconciliation design.
6 other instruments in this scope
H-EVL-01 · Handbook practice
Decision and oracle
Define authorized utility, prohibited effects, adjudication, and external success criteria.
Start here: Define the intended-use evaluation, representative cases, known failure modes, and decision criteria before running tests.
Evidence: Evaluation protocol and oracle validation.
5 other instruments in this scope
H-EVL-02 · Handbook practice
Representative tasks
Cover normal work, edge cases, missing data, accessibility, and relevant customer stages.
Start here: Define the intended-use evaluation, representative cases, known failure modes, and decision criteria before running tests.
Evidence: Case register, sampling rationale, labels, and uncertainty.
7 other instruments in this scope
Establish scope before relying on a mapping
Before relying on a mapping, establish the jurisdiction, entity, intended use, and role in scope. The filters narrow the handbook; they do not perform an applicability assessment. The 45 handbook practices, 12 specifications and 112 reference controls retain separate identities. Each relationship is an editorial interpretation of how a proposed safeguard can contribute; it is not a compliance verdict.
Support if applicable · scoped law, rules or supervisory expectationsGuidance alignment · published voluntary or regulator guidanceUpstream provider duty · model provider obligation; request its evidenceIndustry reference · industry white paperScope-level alignment · public ISO scope; clauses and conformity not inspectedDomain analogy · transferable discipline outside agentic scopeDraft alignment · consultation text
These shared topic explanations provide the background for the mappings. They are editorial interpretations of a contribution to a broader objective, not complete control-by-clause compliance assessments. Original explanation identifiers and earlier links remain available.
Accountability and decision rights · X01
Connect decisions about use, residual risk and intervention to named people with authority. A charter alone does not demonstrate operation.
Evidence to collect: Approved mandate; accountable owner; release and exception decisions; escalation records.
EU AI Act — Regulation (EU) 2024/1689: Art 17 · Support if applicable · High-risk AI system duties for the provider or other role named in the cited article. Establish classification, role, article-specific application date and transitional rules first.
ISO/IEC 42001: AI management-system scope · Scope-level alignment · Organisational AI management or risk-management scope only. ISO public catalogue inspected; numbered clauses and conformity were not inspected.
Identify the deployed system, its dependencies and accountable owner so evidence can be joined to the configuration actually used.
Evidence to collect: System inventory; versioned manifest; dependency and model records; approval linkage.
EU AI Act — Regulation (EU) 2024/1689: Arts 11, 17 · Support if applicable · High-risk AI system duties for the provider or other role named in the cited article. Establish classification, role, article-specific application date and transitional rules first.
US interagency model risk guidance — SR 26-2: VI — model inventory · Domain analogy · Domain analogy only: SR 26-2 explicitly excludes generative and agentic AI models. Assess conventional model components separately; this agent control is not claimed to be required by SR 26-2.
Impact, legal scope and risk decisions · X03
Assess intended use, affected people, applicable roles and materiality before selecting controls. Record the reason a law applies or does not apply.
Evidence to collect: Impact assessment; jurisdiction and role decision; risk register; residual-risk owner.
EU AI Act — Regulation (EU) 2024/1689: Arts 9, 27 · Support if applicable · Article 9: relevant high-risk provider duties. Article 27: specified deployers, including covered public-service and certain credit/insurance uses; an impact assessment is not required of every deployer.
ISO/IEC 23894: AI risk-management scope · Scope-level alignment · Organisational AI management or risk-management scope only. ISO public catalogue inspected; numbered clauses and conformity were not inspected.
Reviewers need competence, time, evidence and the authority to change or stop a consequential action. Measure correction rather than counting approvals.
Evidence to collect: Reviewer test results; workload and response times; held actions; corrections and halt exercises.
EU AI Act — Regulation (EU) 2024/1689: Arts 14, 26(2) · Support if applicable · Article 14: high-risk provider oversight design. Article 26(2): high-risk deployer assignment of competent, trained and authorised oversight. Chapter III application dates and transitional rules matter.
Demonstrate that operators, reviewers and affected users understand limitations and their responsibilities. Training attendance is incomplete evidence.
Evidence to collect: Competence rubric; planted cases; accessible instructions; staffing and adoption evidence.
EU AI Act — Regulation (EU) 2024/1689: Art 4 · Support if applicable · Article 4: providers and deployers in scope of the AI Act; AI literacy for staff and others operating on their behalf.
Keep source provenance, permitted use and data quality traceable; additional lawful-basis and rights review is needed when personal data or protected material is involved.
EU AI Act — Regulation (EU) 2024/1689: Art 10 · Support if applicable · High-risk AI system duties for the provider or other role named in the cited article. Establish classification, role, article-specific application date and transitional rules first.
Limit access and reuse to the authorised purpose and retain personal data only as justified. Test deletion and correction through caches, memory and derived records.
Evidence to collect: Purpose/retention schedule; deletion lineage; access and correction tests; minimisation decisions.
Authenticate the principal and check the actual action and resource before an effect. An authenticated tool call does not establish business authority.
Evidence to collect: Permission matrix; grant/revocation history; denied cross-tenant and expired-authority outcomes.
Prevent untrusted inputs, integrations and changed artifacts from bypassing security boundaries. The specified technical mechanism remains an implementation choice.
Evidence to collect: Threat model; integrity checks; approved artifacts; injection and exfiltration tests.
EU AI Act — Regulation (EU) 2024/1689: Art 15 · Support if applicable · High-risk AI system duties for the provider or other role named in the cited article. Establish classification, role, article-specific application date and transitional rules first.
Trace persistent and derived state, prevent contamination from acquiring authority, and verify that repair reaches descendants. Privacy duties are conditional on personal data.
Evidence to collect: Memory provenance; state lineage; quarantine; corrected descendants; restore tests.
Evaluate utility, relevant failure modes and the deployment decision under declared conditions. A test score does not establish fitness outside those conditions.
Evidence to collect: Evaluation plan; representative cases; configuration; pass/fail decisions; uncertainty and limitations.
EU AI Act — Regulation (EU) 2024/1689: Arts 9(6)–(9), 15 · Support if applicable · High-risk AI system duties for the provider or other role named in the cited article. Establish classification, role, article-specific application date and transitional rules first.
Challenge the system and its integrations under credible threats. Specialist attacks are proposed ways to test broader robustness expectations, not named legal requirements.
EU AI Act — Regulation (EU) 2024/1689: Art 15(5) · Support if applicable · High-risk AI system duties for the provider or other role named in the cited article. Establish classification, role, article-specific application date and transitional rules first.
ISO/IEC 42001: AI management-system scope · Scope-level alignment · Organisational AI management or risk-management scope only. ISO public catalogue inspected; numbered clauses and conformity were not inspected.
Material change and renewed evidence · X14
Detect changes to components, permissions, tasks and suppliers; re-evaluate the affected claims before relying on old approval evidence.
Evidence to collect: Baseline/candidate comparisons; materiality decisions; notices; regression results; new approvals.
EU AI Act — Regulation (EU) 2024/1689: Arts 9(2), 17 · Support if applicable · High-risk AI system duties for the provider or other role named in the cited article. Establish classification, role, article-specific application date and transitional rules first.
US interagency model risk guidance — SR 26-2: V — ongoing monitoring · Domain analogy · Domain analogy only: SR 26-2 explicitly excludes generative and agentic AI models. Assess conventional model components separately; this agent control is not claimed to be required by SR 26-2.
Protected action and assurance evidence · X15
Join identity, policy, decisions and actual effects in protected records. Logging is subject to privacy, confidentiality and retention limits; raw reasoning disclosure is not a universal duty.
Evidence to collect: Attributable action log; configuration and approval IDs; integrity/access checks; retention schedule.
EU AI Act — Regulation (EU) 2024/1689: Arts 12, 19 · Support if applicable · High-risk AI system duties for the provider or other role named in the cited article. Establish classification, role, article-specific application date and transitional rules first.
Monitor behaviour and outcomes against the tested envelope, including latency, capacity and blind spots. Reopen claims when the environment changes.
Evidence to collect: Signal definitions; thresholds; measured delay/error; drift cases; reassessment decisions.
EU AI Act — Regulation (EU) 2024/1689: Arts 26(5), 72 · Support if applicable · Article 26(5): high-risk deployer monitoring. Article 72: high-risk provider post-market monitoring. Establish the applicable role and article-specific timing.
EU AI Act — Regulation (EU) 2024/1689: Arts 20, 73 · Support if applicable · High-risk AI system duties for the provider or other role named in the cited article. Establish classification, role, article-specific application date and transitional rules first.
Inventory suppliers and processing relationships; secure usable documentation, audit rights, change/incident notice and data handling terms.
Evidence to collect: Due diligence; contracts; processor terms; provider evidence; dependency register.
EU AI Act — Regulation (EU) 2024/1689: Art 25(4) · Support if applicable · High-risk AI system providers and third parties supplying components or services; written arrangements depend on the specified value-chain role.
EU AI Act — Regulation (EU) 2024/1689: Art 53(1)(b) · Upstream provider duty · Covered GPAI model providers owe documentation to downstream system providers. An enterprise can request and retain it; model use alone does not make it the obligated model provider.
Concentration, exit and retirement · X20
Identify critical dependencies and demonstrate an exit that closes grants, preserves needed evidence, and restores the business process.
Evidence to collect: Concentration assessment; tested exit; data return/deletion; credential withdrawal; custody decisions.
Measure relevant cohort outcomes, burdens and exclusions across routing and intermediate actions as well as the final decision. Statistical disparity alone is not a complete legal conclusion.
Evidence to collect: Covered-decision inventory; cohort/trajectory results; uncertainty; alternative-design analysis.
EU AI Act — Regulation (EU) 2024/1689: Arts 10(2)(f)–(g), 27 · Support if applicable · Article 10(2)(f)–(g): relevant high-risk provider duties. Article 27: specified deployers, including covered public-service and certain credit/insurance uses; an impact assessment is not required of every deployer.
Explain the actual decision basis and the agent’s role in terms affected people can use. Generated rationalisations are insufficient evidence of the reasons actually applied.
Evidence to collect: Decision factors; validated notice; AI disclosure; intelligibility tests; delivered notice record.
EU AI Act — Regulation (EU) 2024/1689: Arts 13, 50, 86 · Support if applicable · Article 13: high-risk provider information to deployers. Article 50: transparency for specified systems, content and roles. Article 86: deployers making specified covered decisions. Check each article separately.
Provide an accessible route to correction or review and investigate recurring harm beyond the person who complained. Rights, exceptions and remedy duties depend on the use and jurisdiction.
Evidence to collect: Access/correction requests; human reconsideration; complaint results; affected-cohort lookback.
EU AI Act — Regulation (EU) 2024/1689: Arts 85, 86 · Support if applicable · Article 85: complaints about infringements. Article 86: explanation for specified individual decisions by deployers. These do not create a universal right to every form of remedy.
Preserve identity, narrower authority, state lineage and containment across agents. Review the whole system rather than assuming individually acceptable components compose safely.
Evidence to collect: Topology; parent/child authority; joint-outcome tests; propagated revocation and containment.
Keep action, exposure and time limits outside agent-editable state, including retries and descendants. Proposed budget mechanisms support risk bounding; the sources do not prescribe universal numeric limits.
Evidence to collect: External limits; consumption ledger; expiry and restart tests; bounded mandate.
Separate organisational certification, system conformity assessment and evidence of actual control effectiveness. This map cannot establish any of those verdicts.
EU AI Act — Regulation (EU) 2024/1689: Arts 17, 43 · Support if applicable · High-risk AI system duties for the provider or other role named in the cited article. Establish classification, role, article-specific application date and transitional rules first.
ISO/IEC 42001: AI management-system scope · Scope-level alignment · Organisational AI management or risk-management scope only. ISO public catalogue inspected; numbered clauses and conformity were not inspected.
ISO/IEC 23894: AI risk-management scope · Scope-level alignment · Organisational AI management or risk-management scope only. ISO public catalogue inspected; numbered clauses and conformity were not inspected.
Check external business state before asserting completion and reconcile uncertain effects. This is a proposed implementation of accuracy and evidence disciplines, not a universally mandated oracle design.
Evidence to collect: External postconditions; idempotency; state reconciliation; false-completion cases.
EU AI Act — Regulation (EU) 2024/1689: Art 15(1) · Support if applicable · High-risk AI system duties for the provider or other role named in the cited article. Establish classification, role, article-specific application date and transitional rules first.
Trace training and retrieved sources and assess protected data or source rights. GPAI model-provider documentation duties do not automatically pass to every agent deployer.
Evidence to collect: Training/source lineage; rights assessment; extraction tests; supplier documentation.
EU AI Act — Regulation (EU) 2024/1689: Art 53(1)(a)–(d) · Upstream provider duty · Article 53 binds covered GPAI model providers. An enterprise using their models can request and retain the documentation; use alone does not make it the obligated model provider.
Check measurement grain, labels, oracle quality, independence and uncertainty before relying on a safety or oversight claim. Specialist test methods remain proposed implementations.
Evidence to collect: Protocol; denominator; independent labels; uncertainty; scope and reproducibility notes.
US interagency model risk guidance — SR 26-2: V — validation · Domain analogy · Domain analogy only: SR 26-2 explicitly excludes generative and agentic AI models. Assess conventional model components separately; this agent control is not claimed to be required by SR 26-2.
Keep each trigger, recipient, clock and reporting template distinct. Do not apply a single deadline to every AI or ICT incident.
Evidence to collect: Reportability assessment; jurisdiction/authority; clock start; reports and notification receipts.
EU AI Act — Regulation (EU) 2024/1689: Art 73 · Support if applicable · High-risk AI system duties for the provider or other role named in the cited article. Establish classification, role, article-specific application date and transitional rules first.
The dated edition files retain the original topic-based alignments. Each control inherits the references associated with its explanation topics; these files do not certify item-level compliance. For the current selection and suggested procedures, use the filtered implementation export above.
Reviewed 2 October 2026. Next monthly review due 1 November 2026. Monthly reference updates supplement this dated edition; they do not silently revise its legal map.
Commission consolidated-text explorer, 27 July 2026. Status source.
Applies to
EU-market providers, deployers and other covered operators; classify intended use and role first.
Timing
Literacy/prohibitions: February 2025 (some new prohibitions December 2026); GPAI: August 2025; Article 50: August 2026; Annex III high-risk Chapter III duties: 2 December 2027; Annex I: 2 August 2028.
Interpretation limit
Article 27 covers specified deployers; Articles 53/55 cover model providers, not every user of a model. Other articles and transitional arrangements have their own dates. The explorer identifies July 2026 amendments; the linked Official Journal is controlling.
Official consolidated text: CELEX 02016R0679-20160504; reviewed 2 October 2026. Status source.
Applies to
Personal-data processing within Articles 2/3; controller and processor duties differ.
Timing
Applicable since 25 May 2018. Article 22 concerns solely automated decisions with legal or similarly significant effects, subject to exceptions and safeguards.
Core regulation and official rulebook. Status source.
Applies to
Financial entities listed in Article 2; check exclusions, proportionality and the simplified framework.
Timing
Applicable since 17 January 2025; reporting classifications, clocks and templates also depend on delegated/implementing rules.
Interpretation limit
ICT resilience duties do not by themselves define fairness or AI conformity requirements. This map selects core articles, not every technical standard.
UK-incorporated banks, building societies and PRA-designated investment firms with internal-model capital approval; model definition and materiality still matter.
Timing
Policy began 17 May 2024; April 2026 revision inspected. Annual self-assessment expectations remain.
Interpretation limit
Branches, firms without internal-model approval, credit unions, insurers and reinsurers are outside the stated scope. For others these disciplines are an analogy, not a new agent-specific mandate.
Covered duties start 1 January 2027. Attorney General implementing rules were proposed in August 2026; proposals are not enacted requirements.
Interpretation limit
This entry maps the General Assembly’s official enacted summary by named duty, not numbered statutory clauses. It does not reuse the superseded SB24-205 impact-assessment regime.
17 April 2026; supersedes SR 11-7 and SR 21-8. Status source, rechecked 2026-10-03.
Applies to
Banking organizations supervised by the Federal Reserve, OCC or FDIC. The Federal Reserve cover letter says it is expected to be most relevant to banking organizations with over $30 billion in total assets regulated by the Federal Reserve. Scope: traditional statistical/quantitative and non-generative, non-agentic AI models.
Timing
Current guidance. Attachment footnote 3 explicitly excludes generative and agentic AI models.
Interpretation limit
Every agent mapping here is marked analogy. Relevant traditional model components can separately be in scope. Guidance does not establish enforceable standards.
Data users processing personal data with agents; underlying PDPO duties remain binding.
Timing
Current agent-specific supplement to the 2024 Model Framework.
Interpretation limit
Mappings cite the nine recommendations and checklist. DPP references explain the law discussed by the guidance; this column does not convert every recommendation into a statutory requirement.
Organisational AI management systems; contract or policy may make adoption an organisational obligation.
Timing
Published management-system standard.
Interpretation limit
Only ISO’s public catalogue scope was inspected. All mappings are scope-level alignments; no paid clause text, Annex A conformity or certification verdict is claimed.