Topic starter
07/03/2026 9:38 pm
TL;DR: Akeyless argues that agentic AI is exposing a widening gap between rapid deployment and identity controls built for people or static workloads, as shared secrets, long-lived credentials, and weak visibility amplify risk across human IAM and NHI governance. The assumption that identity can be fixed at provisioning time is breaking down.
Editorial analysis by NHI Mgmt Group, based on content published by Akeyless: “From Anonymous to Accountable: Giving AI Agents a Digital Identity”.
Key questions
Q: How should security teams respond when agentic AI still depends on static credentials?
A: They should treat static credentials as a deployment blocker, not a convenience layer.
Q: Why do agentic systems make standing privileges riskier than in traditional IAM?
A: Agentic systems make standing privileges riskier because the agent’s intent is only known during execution, not at provisioning time.
Q: What do organisations get wrong when they onboard AI agents into IAM workflows?
A: A common mistake is treating AI agents like ordinary users or simple scripts.
Practitioner guidance
- Inventory hard-coded and long-lived credentials Scan code repositories, service configurations, and orchestration layers for static secrets, reusable tokens, and certificate material that would let an agent act beyond its intended scope.
- Move agent access to short-lived issuance Replace reusable shared secrets with ephemeral, task-scoped credentials and enforce automatic rotation or expiry tied to task completion.
- Add attestation to initial credential binding Use attested identity proofing for workloads and agents so that credential creation is tied to a verified runtime identity instead of an ad hoc bootstrap string.
Bottom line: Agentic AI does not just create a new workload class. It exposes the limits of IAM and NHI models that still depend on stable identities, static credentials, and human-paced review cycles.
What's in the full article
Akeyless's full article covers the operational detail this post intentionally leaves for the source:
- Code-level guidance for finding hard-coded secrets and reusable credentials in repositories and services
- The article's stepwise migration path from shared secrets to cryptographic challenge-response and secretless identity
- Implementation detail on SPIFFE-based attestation and automated credential issuance for agents and workloads
- Practical sequencing for shifting from static permissions to just-in-time access and continuous verification
👉 Read Akeyless's analysis of agentic AI identity risk and IAM control gaps →
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This topic was modified 45 minutes ago by NHI Mgmt Group
08/10/2026 2:56 pm
Agentic AI exposes a control gap, but more importantly it exposes an assumption gap: enterprise IAM still behaves as if identity state is stable long enough to be provisioned and reviewed in cycles. Agentic systems can request, use, and shed access on the pace of execution rather than the pace of governance. That means the control failure is not just poor implementation, but a mismatch between review-based IAM and runtime identity behaviour. Practitioners need to recognise that the old access lifecycle is no longer the primary control surface.
A few things that frame the scale:
- 69% of security leaders agree identity management must fundamentally shift to address agentic AI systems, according to the 2026 Infrastructure Identity Survey.
- 67% of organisations still rely heavily on static credentials despite the risks they pose to agentic AI deployments, according to the 2026 Infrastructure Identity Survey.
A question worth separating out:
Q: What is the difference between secretless agent identity and a normal service account?
A: A normal service account often starts with a reusable credential and depends on static trust, while secretless identity uses attested issuance and short-lived credentials to reduce reuse and improve traceability. For agents, that difference matters because the credential must follow task scope rather than sit on the account for indefinite use.
👉 Read our full editorial: Agentic AI identity risk is outpacing enterprise IAM controls