Topic starter
25/10/2026 11:09 am
TL;DR: Agentic AI risk is already inside workflows, where small changes can expand hidden blast radius, data exposure, or manipulated execution, according to JetStream Security. Runtime governance has to track what agents can reach and do before authority changes go unseen.
Editorial analysis by NHI Mgmt Group, based on content published by JetStream Security: “The Agent Is Already Running. Now What? JetStream AI Blueprints”.
Key questions
Q: Why do agentic AI systems need runtime governance instead of only pre-deployment review?
A: Because their capability set changes after deployment.
Q: How do security teams decide when AI access has too much blast radius?
A: Measure the sensitivity of the data involved, the number of systems the AI can reach, and whether it can take actions without a human checkpoint.
Q: What breaks when AI agents are not governed at runtime?
A: Without runtime governance, an agent can shift behaviour after provisioning and still execute actions that were never reviewed in context.
Practitioner guidance
- Map every agent workflow before production use Document each agent, model, tool, data source, and identity involved in the workflow so governance starts with a real inventory rather than an assumption.
- Define permission state at each workflow step Record which permissions are active before, during, and after each action so reviewers can see where authority expands or contracts.
- Inspect reachable resources after each change Treat every new tool, data source, or permission addition as a blast-radius event and verify what becomes reachable before rollout.
Bottom line: Agentic AI governance fails when teams focus on model correctness and ignore what the workflow can do with tools, data, and delegated authority.
Glossary terms: Agentic AI Runtime, Workflow-Level Control, Blast Radius, Tool Provenance
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This topic was modified 7 days ago by NHI Mgmt Group
27/10/2026 7:16 pm
Runtime governance is now the defining control plane for agentic AI. The central issue is no longer whether a model can answer correctly, but whether an agent can be observed, attributed, and constrained while it acts across tools and data. That changes governance from a review function into an execution-time control problem. For practitioners, the relevant question is whether authority is visible at the moment of action.
A question worth separating out:
Q: What should organisations do first when they cannot fully describe an AI workflow?
A: Stop treating the workflow as governed and build a complete description of the agent, model, tools, data sources, and identities involved. If the system cannot be described, it cannot be responsibly approved, monitored, or attributed. Description is the prerequisite for any credible control model.
👉 Read our full editorial: Runtime control is the missing safeguard for agentic AI
This post was modified 7 days ago by NHI Mgmt Group