It is designed for product, legal, compliance, and engineering teams, providing a repeatable framework to build and manage AI in regulated environments.
The playbook operationalizes AI governance through four core pillars:
Observe: Instrumenting immutable logs for data provenance and runtime telemetry.
Document: Publishing Model Cards, maintaining a living Data Inventory, and recording Decision Logs.
Assess: Defining a composite risk score and running scenario tests for fairness, robustness, and drift.
Act: Enforcing predeploy CI/CD gates and maintaining documented remediation playbooks.
The book emphasizes producing "regulator-defensible" evidence-such as signed model bundles, immutable audit logs, and populated disclosure templates-to reduce litigation exposure and prevent regulatory escalation. It includes copyable templates for governance rubrics, remediation playbooks, and board reporting metrics.
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