Enterprise AI Governance Framework
A structured approach to governing AI agents across your organisation.
Governing AI agents at enterprise scale requires more than ad hoc policies. This framework provides a structured approach across five pillars — from organisational accountability through to continuous improvement — that aligns with regulatory expectations and operational reality.
Establish an AI governance board
A cross-functional committee with representation from engineering, security, legal, compliance, and business leadership sets strategy, approves high-risk use cases, and resolves escalations.
Assign agent owners for every deployed agent
Each agent has an identified owner accountable for its behavior, compliance, and operational health. Ownership is recorded in the AI inventory and reviewed when teams change.
Define a RACI matrix for AI governance activities
Clearly document who is Responsible, Accountable, Consulted, and Informed for each governance activity — from agent approval through incident response.
Maintain a comprehensive AI inventory
Catalog every AI agent, model, and tool deployed or in development. Record owner, purpose, risk classification, data sources, and governance status.
Classify agents by risk level
Assess each agent's inherent risk based on data sensitivity, autonomy level, potential impact, and affected population. Classification determines required controls.
Conduct impact assessments for high-risk agents
Before deploying agents that handle sensitive data or make consequential decisions, perform structured impact assessments covering fairness, privacy, safety, and stakeholder impact.
Define governance policies as code
Express rules in machine-readable formats that are version-controlled, tested, and enforced at runtime. Policy-as-code eliminates the gap between documented rules and actual enforcement.
Enforce policies at runtime, not just at review
Deploy a policy enforcement layer that evaluates every agent action against governance rules in real time — blocking violations, logging decisions, and escalating when needed.
Implement approval workflows for agent deployment
Require governance review and sign-off before agents reach production. Gate on risk classification, security review, evaluation results, and compliance checks.
Monitor agent behavior continuously
Collect traces, metrics, and logs from every agent. Track policy compliance, performance, cost, and safety metrics through dashboards accessible to governance teams.
Maintain immutable audit trails
Every agent action, policy decision, and governance event is captured in tamper-proof logs. Audit trails support regulatory compliance, forensic investigation, and internal review.
Generate governance reports for stakeholders
Produce regular reports on agent compliance, risk posture, incidents, and governance effectiveness for the board, regulators, and internal audit.
Review and update policies on a regular cadence
Schedule quarterly reviews of governance policies to incorporate lessons learned, regulatory changes, and evolving agent capabilities.
Conduct post-incident reviews for every governance failure
When controls fail, investigate root cause, document findings, and update policies, controls, and playbooks to prevent recurrence.
Benchmark governance maturity and set improvement targets
Use a governance maturity model to assess current state, identify gaps, and set measurable targets for advancing governance practices.
See how Prefactor operationalises governance frameworks
Prefactor gives enterprises runtime governance, observability, and control over every AI agent in production.
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