Topic starter
05/11/2025 5:45 pm
TL;DR: Agentic AI combines planning, tool use, and autonomous execution, so each agent needs a verifiable identity before it can safely invoke APIs or other systems, according to Aembit. Traditional IAM assumptions break when software can act on its own, leaving traceability gaps, delegated credential risk, and weak accountability for machine actions.
Editorial analysis by NHI Mgmt Group, based on content published by Aembit: “Agentic AI”.
Key questions
Q: How should security teams govern machine identity credentials in agentic AI environments?
A: Security teams should extend secrets scanning to cover MCP configuration files, enforce short-lived credentials for all agent workloads, and assign clear ownership to every non-human identity regardless of its origin , human-created or AI-generated.
Q: Why do delegated credentials increase risk when AI agents and users are not clearly separated?
A: Delegated credentials can blur the boundary between user intent and agent autonomy.
Q: What breaks when AI agents can act without a verified human behind them?
A: Fraud and IAM controls lose attribution.
Practitioner guidance
- Define a unique identity for every agent Assign each agent a verifiable workload identity so logs, approvals, and revocation actions map to the runtime actor rather than to a human proxy.
- Eliminate borrowed human credentials from agent flows Remove delegated usernames, shared sessions, and copied tokens from agent workflows where attribution, scoping, or rollback matters.
- Scope agent permissions to specific tools and tasks Restrict what each agent can call, change, or query at runtime so privilege is tied to the current task context instead of standing access.
Bottom line: Agentic AI turns identity into an execution control problem because software can act, adapt, and chain tools without a human in the loop.
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This topic was modified 11 months ago by Abdelrahman
This topic was modified 4 days ago by NHI Mgmt Group
06/10/2026 9:29 pm
Agentic AI identity is now a governance problem, not a feature toggle. Once software can choose actions and tools at runtime, identity must be proven at execution time rather than inferred from the user who initiated the request. Traditional IAM assumes a stable human operator behind the session, but agentic systems make that assumption unreliable. Practitioners should treat agent identity as a first-class governance object with its own lifecycle and audit trail.
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 do agentic AI controls differ from traditional Zero Trust practices?
A: Traditional Zero Trust often stops at verifying identity and device posture, but agentic controls must verify what the actor does after access is granted. In practice, that means applying continuous scrutiny to tool calls, data movement, and delegated actions. The key difference is that the security boundary moves from login to runtime behaviour.
👉 Read our full editorial: Agentic AI identity risk is outpacing enterprise IAM controls