Pillar’s AI security pivot shows behavior now matters more than code

At a glance

What this is: This is a Pillar Security blog analysis arguing that agentic AI risk is now defined by runtime behavior, not just code, because drift and misuse emerge inside business workflows.

Why it matters: IAM and security teams need behavior-level oversight because agentic AI changes the control problem from model review to continuous governance of how autonomous systems act inside enterprise processes.

Context

Agentic AI security is the discipline of governing systems that make runtime decisions inside enterprise workflows. Pillar Security’s argument is that the main failure mode is no longer a vulnerable model alone, but behavior that changes after deployment and slips past traditional application security controls.

That shifts the identity problem from static software review to continuous oversight of how an AI system is permitted to operate. For IAM, IGA, PAM, and NHI programmes, the question becomes whether governance can keep pace with systems that act inside business logic rather than around it.

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. Assign a named owner, define least-privilege access, log every tool call, and require revocation paths for credentials and tokens. If the workflow can touch production systems or sensitive data, its permissions must be reviewed with the same discipline used for privileged machine identities.

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. In agentic systems, the danger often appears only after several valid steps combine into an unsafe outcome. That makes sequence control and identity attribution essential.

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. If monitoring only shows isolated inputs, it is not capturing agent intent, which is where misuse usually appears.

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.

Technical breakdown

Why static AppSec misses agentic AI behavior

Traditional AppSec is designed to find code defects, insecure dependencies, and predictable misconfigurations before release. Agentic AI introduces runtime variability: the system can follow an allowed path one moment and drift into unsafe behavior the next, especially when it is embedded in workflows, tool use, or decision automation. A scanner can assess code, but it cannot reliably predict how an agent will combine context, tools, and policy under live operating conditions. That is why posture baselining and ongoing observation matter more than a one-time assessment. The control problem is not only whether the model exists, but whether its behavior stays inside an intended operating envelope.

Practical implication: move AI security review from release-time scanning to continuous runtime observation of behavior and policy adherence.

What agentic drift means for governance and control

Agentic drift is the gap between how an AI system was expected to behave and how it behaves once it is embedded in real operations. In identity terms, that matters because the system is no longer just a tool, it becomes a governed executor inside business processes. When drift appears, the risk is not only incorrect output but unauthorized action, inconsistent decisions, and policy bypass through normal workflow paths. That makes runtime guardrails and policy enforcement part of governance, not just detection. Pillar’s framing also implies that AI security posture management has to capture operational context, not merely configuration state.

Practical implication: define the approved operating envelope for each AI system and monitor for deviations that change business or access outcomes.

How runtime guardrails differ from pre-deployment testing

Red teaming and pre-deployment testing still matter, but they only test known scenarios under controlled conditions. Runtime guardrails address the live environment where the system encounters new inputs, changing context, and unexpected combinations of actions. In agentic systems, the security question is whether the guardrail can detect and correct unsafe behavior before impact, not whether a test suite once exposed a weakness. That is why monitoring, correction, and governance have to work as a loop. The architecture Pillar describes treats discovery, posture management, red teaming, and runtime enforcement as connected stages rather than separate projects.

Practical implication: pair red teaming with live guardrails so that testing findings translate into enforcement during production execution.

NHI Mgmt Group analysis

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.

Agentic drift is the governance failure mode that security teams must name explicitly: The article describes a system that can move outside its intended posture while still appearing operationally normal. That is not a model bug in isolation, it is a control boundary failure in an environment where AI can influence business logic. Practitioners should treat drift as a governance event, not just a technical anomaly.

Continuous monitoring becomes the identity control plane for agentic systems: If an AI system can shape decisions, invoke tools, or drive workflows, it needs ongoing oversight comparable to how identity teams manage privileged behavior over time. This is where NHI, PAM, and agentic AI governance start to converge. The practitioner conclusion is straightforward: the control plane must watch behavior continuously, or it will miss the moment when risk becomes execution.

AI security posture management needs operational context, not only configuration state: A posture baseline that ignores where the system runs, what it can touch, and how it behaves in production is incomplete. The article’s logic points to a broader truth for the market: security outcomes depend on whether the governance layer understands runtime intent, not just declared settings. That means posture management has to inform enforcement, not just reporting.

Runtime governance gap: The durable problem is not that AI systems are hard to scan, but that they can become operationally unsafe after the scan has finished. That breaks the assumption that pre-deployment review is sufficient for systems that execute inside enterprise processes. The practitioner implication is to redesign AI governance around continuous inspection of action, not periodic review of code.

From our research library:

• 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.
• Read next: Agentic AI Identity Maturity Model

What this signals

Runtime governance gap: The next phase of AI security is less about finding flaws in models and more about controlling what systems are allowed to do once they are operating inside the business. That shift pulls AI security, NHI governance, and privileged workflow control into the same operating model.

Agentic AI programmes that still depend on one-time review will struggle to keep pace with systems that change behavior after deployment. The practical move is to baseline normal action, monitor for drift, and connect alerts to enforcement instead of leaving them as retrospective evidence.

As AI becomes embedded in execution paths, identity teams should expect governance to look more like continuous authorization than periodic assurance. That is the point where agentic AI security stops being a niche AI concern and becomes part of core identity control architecture.

For practitioners

• 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.
• Integrate AI governance with identity controls Align approval, authorization, and monitoring so that AI actions are governed with the same discipline used for privileged non-human access.

Key takeaways

• AI security is moving from code-centric review to behavior-centric governance because agentic systems can drift inside live workflows.
• Continuous monitoring and runtime guardrails matter because they detect and correct unsafe action as it emerges, not after damage is done.
• Identity and governance teams should treat agentic AI as a runtime actor that requires ongoing authorization, not just deployment approval.

Key terms

• Agentic Identity Drift: Agentic identity drift is the divergence between the access an AI agent was approved to have and the behaviour it exhibits at runtime. It appears when tool use, delegation, model updates, or new integrations expand effective access beyond the original governance boundary.
• Runtime Guardrail: A control applied while an AI agent is operating, not just during configuration or review. Guardrails can block dangerous tool calls, require approval for sensitive actions, or stop data leakage before it reaches systems or users.
• AI Security Posture Management: A governance approach for discovering and tracking AI assets such as models, agents, datasets, vector stores, and related infrastructure. It becomes useful only when inventory is connected to runtime exposure and the identity that can actually reach the data.
• Behavior Baseline: A record of normal activity for a non-human identity, including typical consumers, resources, and actions over time. Baselines help security teams detect when an identity is being used in an unusual way and provide the context needed to enforce least privilege safely in dynamic environments.

Deepen your knowledge

NHI governance, agentic AI identity, and machine identity security are core topics in our NHI Foundation Level course, the industry's only accredited NHI security programme. If you are building or maturing an IAM programme, it is worth exploring.