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30/07/2026 10:48 pm
TL;DR: Agentic AI removes the human from identity flows and turns every agent credential into a continuously active access point, while NHI sprawl, stale credentials, and prompt-driven tool use expand the attack surface, according to Pomerium and Rubrik Zero Labs. Static scopes and session-based controls no longer match autonomous behaviour, so governance must shift to per-action enforcement and live attribution.
NHIMG editorial — based on content published by Pomerium: Identity was built for humans. Logging in. Agentic AI ripped up that assumption
By the numbers:
- Non-human identities outnumber human users by roughly 45 to 1 in the modern enterprise, and as much as 100 to 1 in some organizations, according to Rubrik Zero Labs.
- 47% of non-human identities are more than a year old with no credential rotation, according to Rubrik Zero Labs.
Questions worth separating out
Q: How should security teams govern agentic AI that can execute IAM tasks?
A: Start by treating the agent as an NHI with bounded authority, explicit ownership, and revocation procedures.
Q: Why do traditional IAM controls struggle with autonomous AI agents?
A: Traditional IAM assumes predictable users or static machine accounts, but AI agents can act independently, interact with multiple systems, and generate new access needs over time.
Q: What do security teams get wrong about prompt injection defence?
A: They often assume better blocklists will solve the problem, but obfuscation simply changes the shape of the payload.
Practitioner guidance
- Inventory every non-human credential in the agentic path Build a complete list of tokens, service accounts, API keys, certificates, and agent credentials, including age, owner, scope, and last rotation date.
- Remove durable bearer tokens from agent workflows Broker credentials through short-lived scoped assertions rather than giving agents long-lived secrets they can reuse or leak.
- Enforce policy at request time in the data path Put a decision point between the agent and the target system so each tool call can be allowed or denied against live context.
What's in the full article
Pomerium's full blog post covers the operational detail this post intentionally leaves for the source:
- How its identity-aware proxy enforces per-request policy for agents, engineers, and CI pipelines.
- How it brokers external identity tokens into short-lived scoped JWTs without token passthrough.
- How MCP tool restrictions can be applied at the action layer instead of in each server.
- How deployment works across Kubernetes, cloud, and on-prem environments with gRPC and WebSocket traffic.
👉 Read Pomerium's analysis of how agentic AI changes identity trust →
Agentic AI and identity trust: what changes for IAM teams?
Explore further
30/07/2026 10:55 pm
Agentic AI does not just increase NHI volume, it breaks the assumption that access can be defined before execution begins. Traditional IAM and NHI models assume a stable actor, a known purpose, and a bounded session. Agentic systems can re-plan, re-select tools, and extend their own execution path mid-session, so provisioning-time least privilege is no longer sufficient as a governance premise.
A few things that frame the scale:
- Only 5.7% of organisations have full visibility into their service accounts, according to Ultimate Guide to NHIs.
- 79% of organisations have experienced secrets leaks, and 77% of those incidents resulted in tangible damage, according to NHI Mgmt Group research.
A question worth separating out:
Q: Who is accountable when an AI agent takes an unsafe action?
A: Accountability should sit with the business owner of the agent, the team that provisioned the access, and the control owners responsible for monitoring and revocation. If no one can answer who approved the identity, the scope, and the oversight model, the governance framework is not complete enough for production.
👉 Read our full editorial: Identity was built for humans: agentic AI changes the trust model
