Agentic Governance & Model Risk Controls
A generalized control pattern for AI systems that need speed without losing review, assurance, access boundaries, or executive trust.
- Focus
- Governance evidence, approval design, model risk, and escalation paths
- Data posture
- Synthetic examples and generalized operating patterns
- System posture
- Controls before autonomy; auditability before scale
- Primary audience
- Finance, operations, risk, compliance, and executive teams
What it frames
- Approval gates for AI-assisted recommendations before financial or operating decisions move.
- Evidence capture for prompts, source context, assumptions, reviewer decisions, and exception handling.
- Model risk patterns that separate advisory workflows from systems of record and controlled execution.
- Synthetic test cases that let teams evaluate agent behavior without exposing confidential data.