Managing non-human identity risk as agentic AI expands

At a glance

What this is: This blog argues that NHI governance has become a 2025 priority because machine identities are multiplying faster than teams can safely govern, especially as agentic AI expands their use and reach.

Why it matters: It matters because IAM, IGA, PAM, and cloud teams cannot rely on human-centric controls for service accounts, tokens, secrets, APIs, and AI-driven access paths.

By the numbers:

• 93% of respondents said the risks associated with NHIs are urgent, with 24% calling them extremely urgent.
• 42% of respondents say NHI security is now a higher priority than securing human users.
• 78% of respondents said they have high or full visibility into NHIs across their environment.

👉 Read C1.ai's analysis of managing non-human identity risk as agentic AI expands

Context

Non-human identity risk is now a governance problem, not a niche technical issue. As systems, services, software, and AI agents receive more access, the identity model that was built around people stops describing how privilege is actually used.

This article is about the gap between claimed visibility and real control. The central issue is not whether NHIs exist, but whether organisations can govern scope, review, rotation, and offboarding when the identity subject is a machine or agent rather than a person.

Agentic AI raises the stakes because access can now be requested, combined, and used by software acting on behalf of humans. That shifts identity governance from periodic review toward continuous control of issuance, privilege, and lifecycle.

Key questions

Q: How should security teams reduce risk from overprivileged non-human identities?

A: Start by identifying every non-human identity and the exact permissions it uses in production. Then remove broad access, separate duties across workloads, and require periodic entitlement review. The goal is not just fewer identities, but smaller blast radius when one credential is exposed or misused.

Q: Why does high visibility not mean NHI governance is under control?

A: Because inventory and control are different outcomes. A team can know an NHI exists and still leave it over-privileged, unrotated, or unmanaged after deployment. Real governance requires lifecycle control, not just discovery, especially when machine identities outnumber human accounts and do not generate the same natural offboarding events.

Q: What breaks when AI agents are reviewed like human users?

A: Human review assumes access is stable long enough to be observed, approved, and recertified. Agentic workflows often complete within one session and can change scope mid-execution, so the review cycle arrives too late to matter. The result is a governance gap where the action has already happened before anyone can certify it.

Q: What is the difference between human identity controls and NHI controls?

A: Human identity controls rely on interactive authentication, user challenge, and behavioural oversight. NHI controls must manage secrets, certificates, tokens, service accounts, and workload permissions at machine speed. The difference is operational: machine identities need lifecycle governance and runtime visibility, not just login protection.

Technical breakdown

Why NHI visibility does not equal control

Visibility tells you an identity exists. It does not tell you whether the identity is over-provisioned, whether its secrets are rotated, or whether its access matches the task it performs. In NHI estates, the hard problem is governance over tokens, service accounts, certificates, and API keys that rarely follow human lifecycle patterns. A dashboard can show inventory while still missing privilege creep, orphaned credentials, or undocumented third-party dependencies. The result is a false sense of control that becomes more dangerous as automation scales. Practical implication: treat discovery as the starting point, not the control objective.

Practical implication: move from inventory to lifecycle and privilege governance.

How agentic AI changes the NHI risk model

Agentic AI turns some non-human identities into runtime decision-makers that can request access, choose actions, and trigger downstream workflows. That does not mean every AI system is autonomous, but it does mean the access pattern is more dynamic than a static service account. Traditional provisioning assumes intent is known in advance and access can be reviewed later. When an agent acts at runtime, the review window can shrink dramatically, and the old assumption that access will persist long enough to be audited becomes weaker. Practical implication: design governance around issuance and session boundaries, not just periodic recertification.

Practical implication: govern agent access at issuance time and during execution, not only after the fact.

Why privilege and rotation remain the real failure points

The article points to over-provisioning and credential rotation as the core operational barriers. Those are not separate annoyances; they are the two places where machine identity programmes usually fail. Excessive privilege widens blast radius, while weak rotation keeps old access alive long after systems change. Because NHIs do not resign, move teams, or file tickets, lifecycle drift is structural rather than exceptional. That makes manual review cycles unreliable at scale. Practical implication: reduce standing privilege first, then make rotation and offboarding continuous rather than event-driven.

Practical implication: prioritise privilege scope reduction and continuous credential lifecycle control.

Threat narrative

Attacker objective: The likely objective is to exploit excess machine access for unauthorised system reach, data access, or workflow manipulation.

1. Entry occurs when systems, services, software, and AI agents are granted machine access that is broader than the task requires.
2. Escalation happens when over-provisioned NHIs accumulate privileges that are rarely reviewed after deployment.
3. Impact follows when long-lived credentials, dormant access, or third-party machine identities create an unmonitored path into sensitive systems.

Breaches seen in the wild

• AI agent retail card theft campaign 2026: AI agents breached 27+ retailers for about $25 each, used cloud keys and a Secrets Manager dump, and stole 600,000+ payment cards.
• CoPhish OAuth phishing via Copilot Studio: Datadog showed Copilot Studio agents on a Microsoft domain can front OAuth consent phishing and forward stolen tokens; no victims reported.

Read and download The State of NHI & AI Agent Breach Report 2026, covering 150+ breaches impacting Non-Human Identities including AI Agents.

NHI Mgmt Group analysis

NHI governance has become a machine-scale access problem, not a visibility problem. The article shows that many organisations can see their NHIs but still cannot govern them effectively. That distinction matters because discovery does not reduce privilege, rotate secrets, or revoke stale access. The discipline now sits at the intersection of IGA, PAM, and cloud identity governance, where lifecycle control matters more than inventory counts.

Visibility does not survive over-provisioning. When machine identities are granted broad access by default, claimed visibility can coexist with unsafe privilege. That is why the article’s contradiction is so important: organisations may know what exists and still fail to know what it can do. Practitioners should read that as a warning that visibility metrics are lagging indicators, not proof of control.

Agentic AI introduces a runtime access problem that human-centric recertification was not built to solve. Access review processes assume privilege lasts long enough to be examined later. That assumption breaks when an agent requests and uses access within the same task or workflow. The implication is not simply more review, but a rethink of where governance starts and ends for machine-initiated action.

Over-provisioning and rotation are the two failure points that make NHI risk persistent. Broad default access expands blast radius, while weak rotation keeps old credentials alive after business change. Together they explain why machine identity risk remains stubborn even when teams believe they have visibility. Practitioners should treat these as lifecycle defects, not isolated hygiene issues.

Ephemeral credential trust debt is now a governing concept for agentic environments. The article makes clear that the more work is delegated to machines, the more organisations accumulate unresolved assumptions about access scope, credential duration, and offboarding. That debt shows up later as unreviewed privilege, stale tokens, and unmanaged third-party access. Security teams need to treat that debt as a first-class governance risk.

From our research library:

• 53% of security leaders expect AI to run major portions of their infrastructure autonomously within the next three years, according to the 2026 Infrastructure Identity Survey.
• Read next: Agentic AI Identity Guide

What this signals

Agentic AI makes NHI governance a live operating issue, not a future planning exercise. Once systems can request access and execute work on behalf of humans, the programme has to govern issuance, duration, and revocation in near real time. That is a different operating model from periodic review, and it exposes any gap between visibility and actual control.

Access review cadences assume privilege lasts long enough to be certified. That assumption weakens when agents can acquire and release credentials inside a single workflow, so security teams need stronger controls at issuance and session boundaries. According to the 2026 Infrastructure Identity Survey, systems with least-privileged AI access had a 17% incident rate vs 76% for over-privileged systems, and organisations failing to scope AI access properly are 4.5x more likely to experience a security incident.

For practitioners

• Map your NHI estate to actual business function Classify service accounts, tokens, certificates, APIs, and AI agents by owner, purpose, and system dependency before trying to rationalise access.
• Replace broad default grants with scoped entitlements Rework machine access so each identity has the minimum permissions required for its task, environment, and runtime boundary.
• Automate rotation and revocation for machine credentials Build lifecycle controls for secrets and tokens so access expires, rotates, or is revoked without waiting for a manual review cycle.
• Add third-party NHI oversight to vendor governance Track external service accounts and cloud-linked machine identities with the same ownership and offboarding discipline as internal identities.
• Separate human trust controls from machine governance Do not assume MFA, SSO, or user lifecycle management will constrain NHIs; use controls designed for non-human credentials and delegated access.

Key takeaways

• The article’s central warning is that NHI sprawl is now amplified by agentic AI, which increases the number and dynamism of non-human access paths.
• Its evidence points to a governance gap between claimed visibility and actual privilege control, especially where access is broad by default and rarely revisited.
• The practical answer is to treat machine identity lifecycle, entitlement scope, and offboarding as core controls, not supporting hygiene.

Key terms

• Non-Human Identity (NHI): A digital identity assigned to a non-human entity such as a software application, service account, API key, bot, machine, or AI agent that enables it to authenticate and interact with systems without direct human involvement. NHIs now outnumber human identities in most enterprises by 25 to 50 times.
• Over-provisioned NHI: An over-provisioned NHI has broader access than its task, system, or environment requires. In practice, this is one of the most common ways machine identities become dangerous, because excess entitlement expands blast radius and makes later review or cleanup much harder.
• Credential Rotation: The practice of regularly replacing secrets and credentials with new values to limit the window of exposure if a credential is compromised. Automated rotation, enforced by policy, is the security-optimal approach.
• Agentic AI Identity: The complete set of credentials, permissions, and governance controls applied to an autonomous AI agent, covering authentication, authorisation, action logging, and access revocation. Distinct from traditional NHI because agent identities are often ephemeral, delegated, and multi-hop.

What's in the full article

C1.ai's full blog covers the operational detail this post intentionally leaves for the source:

• The survey framing behind the 93% urgency finding and how respondents split on NHI priority versus human identity security
• The specific governance challenges named in the report, including over-provisioning, credential rotation, third-party risk, and sheer volume
• The article’s discussion of how agentic AI changes the access model for tokens, service accounts, secrets, and APIs
• The full set of recommended controls for automated access reviews, scoped permissions, audit trails, and usage monitoring

👉 C1.ai's full blog covers the survey findings, governance challenges, and machine identity controls in more detail.

Deepen your knowledge

NHI governance, agentic AI identity, and machine identity lifecycle are core topics in our NHI Foundation Level course, the industry's only accredited NHI security programme. If you are building or maturing an IAM programme, it is worth exploring.