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Answer CardVersion 2025-09-22

A Practical AI Governance Framework for SaaS

AI governanceSaaSFrameworkRisk managementControls

TL;DR

AI governance aligns roles, risk, controls, and assurance for systems using ML/LLMs. A practical framework uses one policy backbone, clear accountability, risk taxonomy, change gates, human oversight, logging, incident handling, and continual improvement. It should map to ISO 42001 and be informed by NIST AI RMF.

Key Facts

Implementation Steps

Policy & roles → AI policy, RACI.

Risk process → risk register, decisions, exceptions.

Change & testing → gated releases, test logs.

Oversight & logging → oversight records, audit logs.

Assurance loop → reviews, metrics, CAPA.

Glossary

Governance
System of policies, processes, and controls that direct and oversee AI activities
Decision rights
Authority to make choices about AI system design, deployment, and operation
Human oversight
Human involvement in AI system operations to ensure appropriate outcomes
Exception
Approved deviation from standard governance processes or controls
Assurance
Confidence that AI systems operate within defined parameters and controls
Auditability
Ability to examine and verify AI governance processes and decisions

References

  1. [1] ISO 42001 AI Management Systems Standard https://www.iso.org/standard/78380.html
  2. [2] NIST AI Risk Management Framework https://www.nist.gov/itl/ai-risk-management-framework

Machine-readable Facts

[
  {
    "id": "f-roles",
    "claim": "AI governance requires clear roles and decision rights for AI systems.",
    "source": "https://www.iso.org/standard/78380.html"
  },
  {
    "id": "f-controls",
    "claim": "Governance embeds controls such as testing, oversight, and logging.",
    "source": "https://www.iso.org/standard/78380.html"
  },
  {
    "id": "f-improve",
    "claim": "Continual improvement is a core governance requirement with reviews and CAPA.",
    "source": "https://www.iso.org/standard/78380.html"
  }
]

About the Author

Spencer Brawner