Topic starter
02/01/2026 10:47 am
TL;DR: Traditional cybersecurity is still largely reactive, while agentic AI can observe, reason, plan, and execute actions at machine speed, according to Twine Security. The shift matters because IAM, alert handling, and remediation workflows were built for human-paced decisions, not autonomous execution.
Editorial analysis by NHI Mgmt Group, based on content published by Twine Security: “From Reactive to Proactive Cybersecurity”.
By the numbers:
- In 2024 alone, U.S. organizations reported 3,158 data compromises.
- Across the UK, nearly half of medium-to-large enterprises were hit in the last 12 months.
- Breaches take an average of 258 days to identify.
Key questions
Q: What breaks when identity controls are built for human-paced workflows but AI can act autonomously?
A: Human-paced identity controls break because they assume there is time for review, ticketing and approval before action occurs.
Q: Why do AI agents change the IAM risk model?
A: AI agents change the IAM risk model because they can act as authenticated workloads rather than passive tools.
Q: How do security teams know if AI governance is working?
A: Look for evidence that access decisions are reviewable, permissions are revocable, and exceptions are not becoming permanent.
Practitioner guidance
- Differentiate advisory AI from autonomous AI Inventory AI-enabled workflows and label them by whether they only recommend actions or can execute them directly.
- Redesign access reviews for runtime access Treat periodic access reviews as insufficient for actors that can request, use and release access inside a single task.
- Bound tool execution by policy Define which tools, workflows and downstream systems an agentic system may touch, and block any action outside that boundary even if the request is contextually plausible.
Bottom line: Agentic AI turns identity governance into a runtime problem because autonomous actors can observe, decide and act inside one control window.
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This topic was modified 6 days ago by NHI Mgmt Group
06/10/2026 9:17 pm
Reactive IAM is the wrong control model for autonomous execution: Traditional identity governance assumes that access persists long enough to be reviewed, challenged and revoked by a human process. That assumption fails when the actor can observe, decide and act within the same runtime cycle. The implication is not just faster operations, but a broken governance premise that moves control from review to issuance and runtime constraint.
A few things that frame the scale:
- Gartner predicts that by 2028, 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024, and that 15% of day-to-day work decisions will be made autonomously.
A question worth separating out:
Q: How should teams separate advisory AI from agentic AI in security governance?
A: Advisory AI should be governed as decision support, while agentic AI must be governed as an actor with execution authority. The dividing line is whether the system can take actions itself, because once it can, identity, authorization and audit controls must cover the action path, not just the recommendation.
👉 Read our full editorial: Agentic AI is forcing cybersecurity to move beyond reactive defense