--- title: "AI governance: actions, evidence and human control | Quantlix" description: "Define AI governance through action permissions, evaluation evidence, source access, stopping conditions and accountable operational review for each workflow." canonical: "https://quantlix.com/en/whitepapers/responsible-ai-governance-agentic-era/" last-updated: 2026-10-08 --- Source: https://quantlix.com/en/whitepapers/responsible-ai-governance-agentic-era/ Language: en Description: Define AI governance through action permissions, evaluation evidence, source access, stopping conditions and accountable operational review for each workflow. Playbook Responsible AI # AI governance: actions, evidence and human control Define AI governance through action permissions, evaluation evidence, source access, stopping conditions and accountable operational review for each workflow. Quantlix Editorial Insights team Oct 8, 2026 (2026-10-08) 2 min read ![Circuit traces forming a human head on a blue background](https://quantlix.com/_astro/artificial-intelligence.z-Hurd7C_oNgAy.webp) 03 / Technical library Engineering guides: Whitepapers **On this page** ## Govern actions as well as outputs AI governance needs an explicit account of what a system may read, recommend, prepare, or execute. These actions have different consequences. An illustrative maintenance assistant can prepare a request without authority to change equipment settings. Define approval and intervention with the people responsible. ## Keep review evidence usable Record representative evaluation results, known limits, consequential actions, and review decisions while respecting data permissions. Use the [NIST AI Risk Management Framework](https://www.nist.gov/itl/ai-risk-management-framework) as a voluntary reference for the risk discussion. It does not certify the application or replace specialist site approval. ## Revisit authority when conditions change Models, sources, tools, and permissions can change. Decide which evaluations to rerun and when authority should pause or narrow. Make stopping and fallback practical for users. The operating agreement needs an owner for monitoring and response; a written policy alone does not establish safe operation. ## Key takeaways 1. AI governance needs an explicit account of what a system may read, recommend, prepare, or execute. These actions have different consequences. An illustrative maintenance assistant can prepare a request without authority to change equipment settings. Define approval and intervention with the people responsible. - Playbook - Responsible AI Written by ## Quantlix Editorial Insights team ยท Quantlix [Talk toQuantlix](https://quantlix.com/en/contact/) [More whitepapers](https://quantlix.com/en/whitepapers/) ## Related whitepapers [More whitepapers](https://quantlix.com/en/whitepapers/) - ![Circuit illustration with the label Generative AI](https://quantlix.com/_astro/generative-ai.w0LRubVo_28mHUX.webp) Playbook Generative AI Oct 8, 2026 (2026-10-08) ### [Enterprise AI agents: scope, control and operation](https://quantlix.com/en/whitepapers/agentic-ai-enterprise-2026/) A practical guide to enterprise AI agents: define the workflow, action authority, persistent state, evaluation evidence and operating responsibility. 2 min read Read - ![Network illustration with the label RAG](https://quantlix.com/_astro/rag.CUnbr-KJ_VcR47.webp) Playbook Knowledge Systems Oct 8, 2026 (2026-10-08) ### [Enterprise RAG: sources, permissions and evaluation](https://quantlix.com/en/whitepapers/enterprise-rag-truth-playbook/) Design enterprise RAG around approved sources, permission-aware retrieval, supported answers and ongoing knowledge ownership, with practical evaluation criteria. 2 min read Read ## Need the version tailored to your platform, risk model and roadmap? Bring us the context. We will turn the patterns in this library into a pragmatic delivery path your team can use. [Plan a workshop](https://quantlix.com/en/contact/)