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02/08/2026 12:24 pm
TL;DR: Enterprises are already wrestling with AI copilots, chatbots, and multi-agent systems that legacy application security tools were never designed to govern, according to Straikerai. The practical shift is from AI enthusiasm to runtime policy enforcement, because agentic behaviour changes the access and trust model faster than most security programmes can absorb.
NHIMG editorial — based on content published by Straikerai: Why I Joined Straiker
By the numbers:
- 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems (39%), inappropriately sharing sensitive data (31%), and revealing access credentials (23%).
Questions worth separating out
Q: How should security teams govern AI agents that can access enterprise systems?
A: Security teams should govern AI agents as non-human identities with explicit ownership, scoped privileges, and continuous monitoring.
Q: Why do AI agents create more risk than traditional automation?
A: AI agents create more risk because they can interpret context, choose actions, and invoke tools autonomously.
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
- Classify every production agent as a governed non-human identity Assign an owner, a purpose, a task scope, and a revocation path to each agent before it reaches production.
- Enforce task-scoped access for every tool and data connector Limit each agent to the smallest workable set of APIs, datasets, and actions.
- Separate red teaming from runtime enforcement Use adversarial testing to find unsafe prompts, tool misuse paths, and delegation chains, then place policy controls at execution time so those paths are blocked in live workflows.
What's in the full article
Straikerai's full blog post covers the product framing and market context this post intentionally leaves for the source:
- How Straiker defines its dual offense and defense model for agentic AI security
- The product positioning behind autonomous red teaming and runtime guardrails
- The customer and market context used to justify the company's AI security focus
- The role of Straiker's leadership perspective in shaping the article's argument
👉 Read Straikerai's blog post on securing agentic AI applications →
Agentic AI security gaps: are runtime controls keeping up?
Explore further
02/08/2026 1:24 pm
Agentic AI security is becoming an identity governance problem, not just an AI safety problem. Once a system can choose tools and act on its own behalf, it begins to resemble a non-human identity that needs scoped access, auditability, and revocation. Legacy AppSec does not manage delegated runtime authority well, which is why IAM and NHI controls need to move into the AI operating model. Practitioners should treat this as a governance redesign, not a point product decision.
A question worth separating out:
Q: Should IAM teams be involved in AI agent governance from the start?
A: Yes, because AI agents inherit access, secrets, and revocation problems that are already IAM concerns. IAM teams should define entitlement boundaries, session scope, audit expectations, and offboarding rules before the first production rollout. If those controls are deferred, the programme will scale faster than it can be governed.
👉 Read our full editorial: Agentic AI security is moving from theory to runtime control
