LLM Guardrails

LLM guardrails are technical and procedural safeguards that constrain language model outputs to prevent harmful, unintended, or non-compliant responses. These include prompt filtering, output validation, content moderation, and behavioral constraints that organizations implement to ensure models remain within acceptable operational boundaries. For enterprise governance, guardrails are essential for mitigating risks around data leakage, regulatory violations, bias, and reputational harm while maintaining productive AI system functionality.

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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.

agentic AI MCP security LLM guardrails AI governance tooling open-source AI runtime enforcement compliance packs