Topic starter
02/08/2026 12:03 pm
TL;DR: Agentic AI governance is shifting from policy documents to runtime monitoring, guardrails, human oversight, and continuous testing because autonomous systems can call tools, take actions, and amplify risk in ways traditional IT governance was never designed to handle, according to Akto. That makes accountability, tool authorization, and traceable decision paths the new operational baseline.
Editorial analysis by NHI Mgmt Group, based on content published by Akto: “AI Governance: Frameworks, Best Practices & Enterprise Guide (2026)”.
Key questions
Q: How should security teams govern AI agents that can change actions at runtime?
A: Security teams should govern runtime AI by correlating identity, data, and intent before trusting an action path.
Q: Why do agentic AI workloads increase governance risk compared with normal model calls?
A: Agentic workloads can chain decisions, retry automatically, and expand context across multiple calls.
Q: What breaks when agent actions are only monitored after execution?
A: The control breaks because monitoring can explain misuse, but it cannot prevent tool abuse, data exposure, or chained execution once the agent has already acted.
Practitioner guidance
- Define task-scoped agent authorisation Bind each agent to the smallest set of tools, APIs, and data sources needed for a specific workflow, then re-evaluate permissions as the workflow changes.
- Enforce human approval for high-risk actions Require explicit approval before an agent can send messages, move funds, change records, or trigger other irreversible business actions.
- Log delegation scope and action lineage Record which human or system delegated the task, which tools were used, what outputs were produced, and which approvals were granted.
Bottom line: Agentic AI governance now depends on controls that operate while the system is acting, not just on policies written before deployment.
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02/08/2026 1:09 pm
Runtime control is now the core governance boundary for agentic AI: policy documents describe intent, but they do not constrain an agent that can select tools and act inside a workflow. The practical control surface has moved to authorization at execution time, approval checkpoints, and live monitoring. For identity teams, that means agent governance increasingly resembles non-human identity governance with stronger runtime enforcement.
A few things that frame the scale:
- 54% of organisations are actively deploying AI agents across workflows, yet only 21% report a mature governance model for agentic AI.
A question worth separating out:
Q: How do organisations keep humans in the loop for AI agent decisions?
A: They define which agent actions require approval, what evidence must be reviewed, and how decisions are logged for audit and oversight. Human-in-the-loop only works when review is operationally realistic. If the approval path is too slow or too broad, teams either bypass it or stop using the control.
👉 Read our full editorial: AI governance for agentic systems now depends on runtime controls