Topic starter
24/10/2026 8:53 pm
TL;DR: Honeytokens and decoys become unusually effective once AI agents operate with real credentials, according to Acalvio. Runtime controls need a way to spot when an agent follows a decoy or crosses from approved work into unsafe access.
Editorial analysis by NHI Mgmt Group, based on content published by Acalvio: “Deception Is Having Its Day”.
Key questions
Q: Why can deception be effective against autonomous AI attacks?
A: Deception works because autonomous attackers consume environment signals while they decide what to do next.
Q: When should organisations prioritise deception over more guardrails for AI agents?
A: Prioritise deception when the environment already gives agents real credentials and the main problem is knowing what they do after access begins.
Q: How should security teams detect risky AI agent behaviour in production?
A: Security teams should detect risky AI agent behaviour by monitoring runtime decisions, tool selection, action sequences, and deviations from the approved use case.
Practitioner guidance
- Instrument agent sessions with deception tripwires Place honeytokens, canary files, and decoy identities in paths that legitimate users should never touch, then wire those events into containment workflows.
- Map agent access to runtime authorisation rules Review where agents receive credentials or delegated access without a live decision point, then tighten those paths so unsafe use is easier to detect.
- Extend cloud and identity monitoring to agent behaviour Correlate decoy interactions with identity, cloud, and workload telemetry so a single trap hit becomes a high-confidence investigation trigger.
Bottom line: AI agents change the security problem from pre-launch assurance to live detection inside the environment.
Glossary terms: Honeytoken, Deception Technology, Runtime Detection, Identity Blast Radius
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This topic was modified 6 days ago by NHI Mgmt Group
25/10/2026 11:13 am
Runtime deception is becoming the practical control for autonomous behaviour: once an agent has real credentials, the meaningful question is no longer whether the model was evaluated but whether unsafe action can be detected while the session is still active. That is a governance shift, not just a tooling preference. The control value comes from uncertainty, because an agent that cannot distinguish a real credential from a tripwire reveals itself by acting. Practitioners should treat that as a runtime identity signal, not as a niche red-team technique.
A question worth separating out:
Q: What is the difference between AI agent guardrails and runtime deception?
A: Guardrails constrain what should happen before or during execution, while runtime deception is designed to reveal what is actually happening by baiting unsafe actions. They are complementary, but deception gives defenders a clearer signal when the actor is autonomous enough to outpace policy review.
👉 Read our full editorial: Deception is emerging as a runtime control for AI agent risk
This post was modified 6 days ago by NHI Mgmt Group