Runtime Enforcement

Runtime enforcement refers to the mechanisms and controls that actively monitor and restrict AI system behavior during execution, preventing policy violations as they occur. This approach is critical for enterprise governance because it provides real-time intervention rather than relying solely on post-deployment auditing or manual oversight. Organizations use runtime enforcement to ensure language models, autonomous agents, and other AI systems comply with security boundaries, data protection requirements, and business rules without stopping model operations entirely.

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Corporate PolicyGlobal2026-08-28

Open-Source Runtime Enforcer Exposes the Gap Between Agent Policy and Practice

Conduct, an open-source AI agent governance framework published on GitHub by independent developer sseshachala, enforces compliance policy before LLM or shell tool calls execute rather than logging behavior after the fact. The project ships with more than 20 pre-mapped compliance packs covering frameworks including the EU AI Act, NIST AI RMF, HIPAA, PCI DSS 4.0, SOC 2, and ISO 42001. It uses a fail-closed default and SHA-256 hash-chained audit logs designed to produce auditor-ready evidence.

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