AI Governance
Definition
The framework of policies, processes, and technical controls that ensure AI systems operate safely, ethically, and in compliance with regulations. AI governance spans multiple levels: organizational (policies, committees, oversight), technical (safety mechanisms, monitoring, enforcement), and regulatory (compliance with AI Act, GDPR, SOC 2). Technical AI governance specifically refers to the runtime enforcement mechanisms that control AI behavior.
Why It Matters
As AI systems move from advisory to executive roles, governance must evolve from documentation to enforcement. Policy documents don't prevent AI agents from deleting databases. Runtime enforcement does. The gap between organizational governance (what you write) and technical governance (what you enforce) is where incidents occur.
How Exogram Addresses This
Exogram bridges the gap between policy and enforcement. Policy rules are executable code — not documents, not prompts, not suggestions. The deterministic policy engine enforces governance at the execution boundary, producing an immutable audit trail that satisfies compliance requirements.
Related Terms
Key Takeaways
- → This concept is part of the broader AI governance landscape
- → Production AI requires multiple layers of protection
- → Deterministic enforcement provides zero-error-rate guarantees