Topic starter
12/06/2026 12:11 am
TL;DR: AI security fails when teams focus on code rather than emergent behaviour, because agentic drift can occur inside core business workflows and escape traditional AppSec scanning, according to Pillar Security. The editorial case is that continuous monitoring, posture baselining, and runtime guardrails now matter because AI has become an operational identity and governance problem, not just a model problem.
Editorial analysis by NHI Mgmt Group, based on content published by Pillar Security: “Why I’m Joining Pillar Security by Jenna Raby”.
Key questions
Q: How should security teams govern AI-enabled workflows that can act on their own?
A: Treat them as identity-governed execution paths, not just software features.
Q: Why do traditional AppSec tools fall short for agentic AI?
A: Traditional AppSec tools fall short because they are designed to inspect code, requests, or dependencies, not the meaning of an evolving tool chain.
Q: How do you know if AI agent monitoring is actually working?
A: It is working when you can explain why a sequence of actions was allowed, blocked, or escalated, using evidence from the full chain rather than a single request.
Practitioner guidance
- Inventory where AI is embedded in business workflows Map every system where AI influences decisions, automates actions, or touches operational processes so the governance boundary is explicit.
- Establish a runtime behavior baseline Define normal agentic behavior for each use case, then alert on drift, policy violations, or unexpected tool and workflow patterns.
- Treat red teaming as an input to enforcement Use test findings to tighten live guardrails, not just to document weaknesses after the assessment is over.
Bottom line: AI security is moving from code-centric review to behavior-centric governance because agentic systems can drift inside live workflows.
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This topic was modified 1 day ago by NHI Mgmt Group
12/06/2026 9:16 am
Behavior, not code, is now the primary AI security boundary: Once AI is embedded in business workflows, the meaningful attack surface becomes how the system acts under live conditions. Static review can show that code is clean and still miss a system that drifts into unsafe operational behavior after deployment. The implication is that AI governance must be built around runtime conduct, not artifact inspection.
A few things that frame the scale:
- 59% of compromised machines in a major 2025 supply chain attack were CI/CD runners rather than personal workstations, according to the State of Secrets Sprawl 2026.
- 96% of organisations store secrets outside of secrets managers in vulnerable locations including code, config files, and CI/CD tools, according to the Ultimate Guide to NHIs.
A question worth separating out:
Q: When should organisations use runtime guardrails instead of relying on pre-deployment testing?
A: Use runtime guardrails whenever the system can change behavior after launch, reach tools, or affect live business operations. Pre-deployment testing remains necessary, but it cannot replace live enforcement when the risk is produced by emergent behavior. The two controls solve different parts of the problem and should be linked.
👉 Read our full editorial: Pillar’s AI security pivot shows behavior now matters more than code
This post was modified 1 day ago by NHI Mgmt Group