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Governance & ComplianceView domain

APRF-29

Organizational Governance

AI policy, ownership, risk acceptance, and continual improvement—ISO 42001-style management system.

Purpose

Establish organizational AI policy, clear ownership/RACI, risk-acceptance authority, and continual improvement so technical pillars have accountable stewards.

Mandatory checks

Gate controls. Each check is pass/fail via artifact + pass condition. Expected from the annotated capability level when the system meets the minimum criticality tier.

  • ORG-M1L3 · DefinedTier 2 · ProductionhybridE3

    An approved AI policy shall exist with version, named owner, and review date ≤12 months, and shall include both acceptable-use and prohibited-application sections.

    Artifact
    Approved AI acceptable-use / prohibited-applications policy with version, owner, and review date + Confirmation that both acceptable-use and prohibited-application sections are present
    Pass condition
    Policy has version, owner, and review date ≤12 months; includes both acceptable-use and prohibited-application sections (policy evidence measuredAt ≤90 days). If AI is not used at all, score NOT_APPLICABLE.

    Why this control exists

    Threat map

    Establish the organization's binding boundary for acceptable and prohibited AI use.

    Threats mitigated

    Insider MisuseHarmful Content GenerationShadow Agents

    Protects

    UsersAudit TrailSafety

    MITRE: no technique mapped — this control addresses governance or assurance rather than a specific adversary technique.

    Without a stated policy there is no basis for declaring a use unacceptable or for acting when someone crosses the line. This is foundational governance with no adversary technique mapping.

    Informative threat context — mappings reduce exposure and do not guarantee mitigation; not certification.

Evidence required

  • AI policy document
  • Ownership/RACI for production AI systems
  • Risk-acceptance register samples
More detailPhilosophy, failures, practices, validations, examples, crosswalks, and evolution

Engineering philosophy

Technical controls without organizational governance drift. A production-ready AI program names owners, accepts residual risk explicitly, and improves from incidents and audits.

Why it matters

Enterprises fail AI readiness when no one owns a pillar, exceptions are informal, and leadership cannot see residual risk.

Common failures

  • AI features ship with no named owner for safety or eval gates
  • Exceptions granted in chat with no expiry
  • No AI policy covering acceptable use and prohibited applications
  • Compliance theater without leadership review of risk

Severity & risk

Severity
high
Impact if violated
Risk level
medium
Typical residual risk (impact × likelihood)

Engineering best practices

  • Map APRF domains to teams; avoid orphan pillars
  • Time-box exceptions; auto-escalate expired waivers
  • Tie promotions and funding to measurable maturity for high-criticality systems

Automatic validations

  • Inventory systems missing owners
  • Alerts on expired risk acceptances

Manual validations

  • Annual policy review
  • Leadership tabletop on AI risk acceptance

Examples

  • A risk register entry accepts a missing multi-provider fallback until Q3 with CTO sign-off
  • Each agent product lists owners for Security, Safety, Evaluation, and Reliability

References

Crosswalks

  • GOVERN Govern

    NIST AI Risk Management Framework · supports

  • Accountable Accountable and Transparent

    NIST AI Risk Management Framework · supports

  • §4 Context of the organization

    ISO/IEC 42001 · aligns-with

  • §5 Leadership

    ISO/IEC 42001 · supports

  • §6 Planning

    ISO/IEC 42001 · partial

  • §10 Improvement

    ISO/IEC 42001 · aligns-with

  • L6 Security & Compliance

    CSA MAESTRO (Multi-Agentic Threat Model) · supports

  • CC1 Control Environment

    SOC 2 Trust Services Criteria · evidence-for

  • CC3 Risk Assessment

    SOC 2 Trust Services Criteria · evidence-for

  • Operational Excellence Operational Excellence

    AWS Well-Architected Framework · aligns-with

Future evolution

Machine-readable organizational control catalogs mapped to APRF domains and ISO 42001 clauses.