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08/06/2026 4:14 pm
TL;DR: Truly agentic AI depends on four foundations—autonomy, persistence, reactivity, and proactivity—according to Twine Security, and that framing applies to identity operations where systems provision access, maintain lifecycle continuity, detect anomalies, and surface risky entitlements. The practical question is not whether AI can assist IAM, but whether its behaviour changes the governance model around access, accountability, and lifecycle control.
NHIMG editorial — based on content published by Twine Security: 4 Components That Make AI Truly Agentic
By the numbers:
- 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems, inappropriately sharing sensitive data, and revealing access credentials.
Questions worth separating out
Q: How should security teams govern AI systems that act like autonomous identities?
A: Security teams should classify the system by what it can decide at runtime, not by the label the vendor uses.
Q: Why do persistent AI agents create new lifecycle risk for IAM programmes?
A: Persistent agents carry memory, state, and prior context forward, so access risk is no longer limited to a single transaction.
Q: What breaks when reactive AI systems can take identity actions without approval?
A: What breaks is the assumption that human-paced review will catch the action before it matters.
Practitioner guidance
- Classify agentic systems by decision authority Document whether the system can choose actions, select tools, and decide execution timing without human approval.
- Map lifecycle controls to retained state Review how memory, goals, and contextual state persist across sessions, then define when that state is reset, revoked, or reauthorised.
- Separate proactive actions from approved workflows Inventory every identity-related action the system can take on its own, including anomaly response, entitlement changes, and escalation.
What's in the full article
Twine Security's full blog covers the operational detail this post intentionally leaves for the source:
- Twine's own framing of the AI digital employee model and how it is intended to fit into identity operations
- The vendor's examples of how autonomy, persistence, reactivity, and proactivity are applied inside its IAM workflow design
- The product positioning behind delegated identity work, onboarding, and ongoing access management tasks
- The source article's explicit claims about what makes its digital employee different from passive automation
👉 Read Twine Security's analysis of the four foundations of agentic AI →
Agentic AI foundations: what IAM teams need to verify?
Explore further
08/06/2026 5:45 pm
Agentic AI is not just another non-human identity class. It changes the governance assumption that access can be fully described at provisioning time, because the actor can choose actions, context, and timing at runtime. That means identity policy no longer governs a fixed workflow alone. Practitioners should stop treating agentic systems as enhanced automation and start treating them as actors whose behaviour can outgrow the permission model.
A few things that frame the scale:
- 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems, inappropriately sharing sensitive data, and revealing access credentials, according to AI Agents: The New Attack Surface report.
- Only 52% of companies can track and audit the data their AI agents access, leaving 48% with a complete blind spot for compliance and breach investigation.
A question worth separating out:
Q: How do autonomous AI identities change accountability in access governance?
A: Accountability becomes harder when the actor makes decisions independently and leaves a trail that looks like delegated behaviour rather than a human request. Teams need named owners, durable logs, and rollback authority so that responsibility does not disappear into the system's runtime autonomy.
👉 Read our full editorial: Agentic AI needs autonomy, persistence, reactivity, and proactivity
