Topic starter
14/09/2026 10:09 am
TL;DR: Blocking AI agents before runtime visibility is established creates avoidable false positives, with Ory reporting that teams cannot yet tell normal execution from risky drift. Harness-layer telemetry has to stay on so policy can be tuned to real system calls, permissions, and touched systems.
Editorial analysis by NHI Mgmt Group, based on content published by Ory: “Turn the Lights On Before You Start Blocking”.
Key questions
Q: Why do AI agent controls need visibility before enforcement?
A: Visibility is the only way to convert unknown agent behaviour into policy that matches reality.
Q: What are the signs that AI agent policy is too aggressive?
A: Common signs include blocked business workflows, repeated false positives, and frequent exceptions that operators have to manually override.
Q: What breaks when AI agent blocking starts before visibility is established?
A: Blocking first usually creates false positives, because teams have not yet learned what normal agent behaviour looks like.
Practitioner guidance
- Implement observe-first enforcement Run agents in monitor mode before allowing deny rules to fire, and collect the actions, permissions, and target systems each agent actually uses.
- Baseline normal agent behaviour Compare real production execution against intended workflows so you can separate expected system calls from misconfigured or risky behaviour.
- Place controls at the harness layer Use the execution point as the place to inspect, approve, or stop actions because that is where agent behaviour becomes an operational change.
Bottom line: Blocking AI agents without runtime visibility creates avoidable false positives because teams cannot distinguish normal execution from risky drift.
Glossary terms: False Positive, Monitor Mode, Harness Layer, Runtime Visibility
Explore further
View Full Forum → | NHI Foundation Course → | Our Services → | Read the full analysis →
This topic was modified 6 days ago by NHI Mgmt Group
14/09/2026 10:10 am
Observe-first governance is the only defensible starting point for agent control. Blocking before visibility repeats the mistakes organisations already made with IPS and WAF rollouts, where unknown baseline behaviour created false positives and operational breakage. Agentic AI compresses that risk because adoption is moving faster than the organisational learning curve. The implication is that policy maturity now depends on runtime observation before restriction.
A few things that frame the scale:
- Only 5.7% of organisations have full visibility into their service accounts, according to the Ultimate Guide to NHIs.
A question worth separating out:
Q: How should security teams sequence AI agent monitoring and blocking?
A: Start with observation mode, learn the actual execution pattern, then turn specific denies on only where the data justifies them. That sequence preserves business workflows while still allowing the organisation to restrict unsafe actions once it understands how the agent behaves in production.
👉 Read our full editorial: AI agent blocking without visibility creates false positive risk