Start with an auditable inventory.

Catalog every AI-enabled workflow across your organization. Capture who owns it, which data sources fuel it, and what decisions hinge on its recommendations.

An accurate inventory becomes the backbone of your risk assessments and informs how you instrument telemetry and reviews.

Your AI inventory should be treated like a living CMDB for automation.
  • Classify workloads by business criticality and regulatory scope.
  • Document human override paths and escalation owners early.
  • Link each workload to the datasets and prompt libraries it uses.

Instrument the AI lifecycle with purposeful telemetry.

Move beyond accuracy metrics. Capture drift, prompt health, cost, and fairness signals within the same observability plane.

Telemetry must feed directly into review cadences so compliance, security, and engineering speak from the same source of truth.

  • Collect prompt inputs/outputs with automated redaction for sensitive data.
  • Measure decision latency and fallback frequency for critical automations.
  • Track human feedback to drive reinforcement learning or rule updates.

Run AI risk reviews as executive business rituals.

Quarterly reviews anchor your controls. Bring product, legal, compliance, and operations leaders together to assess performance, incidents, and pending launches.

Use dashboards that tie AI performance and governance posture to business KPIs so leaders see the same narrative.

Controls stick when leadership treats AI risk reviews like financial close meetings.
  • Highlight incidents, mitigations, and policy updates since the prior review.
  • Align on upcoming AI changes, required sign-offs, and stakeholder training.
  • Store review minutes and approvals in a shared governance workspace.