A Practical AI Governance Framework for SaaS
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
Governance assigns accountable roles and decision rights for AI systems.
Risk management identifies hazards and treatments with traceable decisions.
Controls include testing, oversight, logging, and deployment approvals.
Continual improvement requires monitoring, reviews, and CAPA.
Using recognized frameworks supports assurance and auditability.
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] ISO 42001 AI Management Systems Standard https://www.iso.org/standard/78380.html
- [2] NIST AI Risk Management Framework https://www.nist.gov/itl/ai-risk-management-framework
Machine-readable Facts
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"claim": "AI governance requires clear roles and decision rights for AI systems.",
"source": "https://www.iso.org/standard/78380.html"
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"id": "f-controls",
"claim": "Governance embeds controls such as testing, oversight, and logging.",
"source": "https://www.iso.org/standard/78380.html"
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