YAML Policies and SQLite Audit Trails for AI Governance
This article discusses how the author's company implemented a YAML-based policy engine and a SQLite-powered audit trail to govern their 13 production AI agents with different permissions and risk levels.
Why it matters
Effective AI governance is critical as companies deploy more complex, high-stakes AI systems. This article showcases a pragmatic solution that can scale to hundreds of AI agents.
Key Points
- 1Hardcoded governance rules break at scale, requiring a more flexible, configuration-based approach
- 2YAML policy files define allowed/denied tools, escalation rules, and runtime limits for each agent and role
- 3A SQLite database records all governance decisions, allowing querying, analysis, and export of audit data
Details
The article describes the challenges of managing governance for multiple AI agents with varying permissions and risk levels. To address this, the company implemented a YAML-based policy engine that allows defining global defaults, role-specific rules, and agent-level overrides. This enables flexible, scalable governance without hardcoding rules in the codebase. Additionally, they built a SQLite-powered audit trail that records all governance decisions, including allow/deny actions, escalations, and forced terminations. This provides a queryable, exportable audit log for observability and compliance. The combination of declarative YAML policies and a persistent audit database represents a practical approach to AI governance at scale.
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