Agentic security for AI agents highlights the NHI risk gap

At a glance

What this is: C1.ai's post argues that AI agents and other non-human identities create identity risk faster than manual review can keep up, and that detection plus routed remediation is now the governing control plane.

Why it matters: IAM, IGA, and PAM teams need to treat AI agents as governed identities, because ownership, classification, and remediation now happen at machine speed rather than review cadence.

By the numbers:

• Palo Alto Networks' 2026 Identity Security Landscape found organizations now manage 109 machine identities, including AI agents, for every human identity.
• The ratio of machine identities, including AI agents, to human identities rose from 82:1 to 109:1 in one year, according to Palo Alto Networks research cited by C1.ai.

👉 Read C1.ai's post on agentic security and identity risk detection

Context

AI agent identity risk is what happens when machine identities are created, granted access, and left to operate faster than governance processes can classify or review them. In this post, C1.ai frames the problem as an identity operations gap rather than a tooling gap.

For identity teams, the issue is not just inventory. Ownership, classification, anomaly detection, and remediation all have to work continuously across service accounts, workloads, integrations, and AI agents if the programme is going to stay current.

The article's starting position is typical for organisations scaling AI adoption. The same governance assumptions that worked when identities were reviewed manually now break down when identities appear and act at machine speed.

Key questions

Q: What should teams do when an AI agent has no clear owner or business justification?

A: They should suspend or remove the agent until ownership, purpose, and access scope are documented. Orphaned agents are a governance failure, not a tolerated edge case, because they preserve access without accountability and often retain credentials long after the original experiment or deployment has ended.

Q: Why do AI agents create risk when they fall outside normal identity review cycles?

A: AI agents create risk because they can be provisioned quickly, used widely, and forgotten just as quickly. When they sit outside standard IAM review cycles, no one consistently validates ownership, permissions, or business need. That leads to persistent broad access, weak accountability, and hidden trust relationships that security teams may not discover until an incident or audit exposes them.

Q: What are the signs that machine identity management is failing in an organisation?

A: Common signs include incomplete inventory, spreadsheet based tracking, manual renewal processes, unclear ownership, and repeated certificate expiry events. Another signal is when teams struggle to audit where machine identities exist or cannot automate lifecycle actions at scale. If operational teams keep reacting to expiring certificates instead of governing identity lifecycles proactively, the control environment is already under strain.

Q: What is the difference between identity inventory and identity context?

A: Inventory tells you what identities exist. Context shows what they can access, how they connect across systems, and which entitlements expand their reach. For AI agents and service accounts, context is what turns a list of accounts into a governance decision because it reveals which identities are actually risky.

How it works in practice

Why manual identity review fails for AI agents

Manual review assumes identities can be inspected on a human timetable. AI agents and other machine identities do not behave that way: they can be created quickly, granted access immediately, and begin acting before the next recertification cycle. That makes ownership gaps, misclassification, and shadow accounts less like exceptions and more like a structural outcome of scale. In NHI governance terms, the problem is not lack of review intent. It is that the review model is built around a slower identity lifecycle than the one machine identities now follow.

Practical implication: Treat AI agent identity review as a continuous detection problem, not a periodic certification exercise.

Why identity context matters more than inventory

Inventory tells you what exists. Identity context tells you what the identity can reach, who owns it, and how it connects across apps, accounts, and entitlements. The post emphasizes an intelligence graph because the same identity can look harmless in a list while being materially risky once its access reach is visible. This is especially important for service accounts used by agents, where the operational identity and the business workflow are tightly coupled. Without context, teams can count identities but still miss the ones that matter most.

Practical implication: Prioritise access graphs and ownership context over flat identity lists when triaging machine identity risk.

How routed remediation changes the control model

Detection alone does not close identity risk. The article describes a workflow where findings are assigned to owners, pushed through approval paths, or routed into ITSM and incident response tools. That matters because the control point shifts from after-the-fact review to governed remediation with audit evidence. In practice, this is closer to identity workflow orchestration than static reporting. For AI agents, the value is not only that a risky condition is found, but that the organisation can prove who acted, what changed, and when.

Practical implication: Build remediation routing and audit trails into the identity workflow, not outside it.

NHI Mgmt Group analysis

Machine-speed identity growth has broken the assumptions behind manual review. Access review programmes were designed for identities that persist long enough to be observed, certified, and offboarded on a human cadence. AI agents and adjacent non-human identities can be created, assigned, and operational before that cadence ever catches up. The implication is that identity governance must move closer to issuance and runtime state, not depend on retrospective review alone.

Ownership gaps are now a primary governance failure mode for AI agents. An agent without an owner is not just an administrative oversight. It is an accountability defect that prevents remediation, approval routing, and audit closure. When the identity subject is a machine actor, ownership becomes part of the control itself, not a reporting field.

Identity context is the new control surface for machine identities. A list of accounts cannot tell you which service account or agent can reach sensitive systems, which entitlements are inherited, or where anomaly detection is absent. That is why governance for NHI now depends on reach, relationship, and entitlement context, not inventory alone. Practitioners should expect triage quality to improve only when the control plane understands identity relationships.

Continuous remediation is becoming the practical meaning of governed AI adoption. If teams cannot route findings into the systems they already use for approvals, tickets, and incident response, then detection becomes noise. The field is moving toward governed workflows that connect discovery, decision, and evidence in one chain. Practitioners should measure identity maturity by how quickly a risky condition can be turned into a tracked and auditable resolution.

Identity review cannot remain a human-centric process when the subject is an AI agent. AI agent identity governance was designed for conditions where a person, a service account, and a workflow could be separated cleanly. That assumption fails when the actor is a machine that launches, authenticates, and acts on its own schedule across connected systems. The implication is that governance needs to be redesigned around machine lifecycle state, not adapted from human recertification models.

From our research library:

• Only 5.7% of organisations have full visibility into their service accounts, according to the Ultimate Guide to NHIs.
• 62% of organisations are at least experimenting with AI agents, but only 23% have scaled one into a single business function, according to McKinsey's 2025 State of AI survey.
• Read next: Agentic AI Identity Guide

What this signals

Identity review is moving from periodic certification to continuous governance. When machine identities appear and disappear faster than a review window, the operational question becomes whether the programme can detect and route risk before the identity has already acted. That shifts priority from access recertification alone to ownership enforcement, classification hygiene, and governed remediation.

AI agent identity risk should be treated as a lifecycle problem, not a discovery problem. Discovery answers what exists, but governance depends on who owns it, how it is classified, and whether the finding can be resolved through existing approval and audit paths. Programmes that stop at inventory will keep finding risk without reliably closing it.

For practitioners

• Map ownership for every AI agent and service account Create a required owner field for each non-human identity and block remediation closure until the ownership gap is resolved.
• Prioritise identities with expanded reach Use access graphs to rank machine identities by the apps, accounts, and entitlements they can reach, then triage the highest-impact ones first.
• Route findings into existing governance workflows Send identity findings through the same approval, ticketing, and audit paths used for access changes so remediation is accountable and visible.
• Suppress blind spots in anomaly detection Flag connectors and identity sources where anomaly detection is disabled so the governance model reflects where monitoring coverage is incomplete.
• Separate human and non-human classification logic Review identities that are misclassified as human and correct the classification rules so machine identities do not inherit the wrong governance path.

Key takeaways

• AI agents and other machine identities now create governance pressure faster than manual identity review cycles can absorb.
• The strongest control signal in this post is not inventory volume but whether ownership, classification, and anomaly detection are enforced continuously.
• Identity programmes need routed remediation and audit evidence, because finding risk without closing it still leaves the governance gap open.

Key terms

• Machine Identity: The digital identity of a machine, device, or workload, such as a server, container, or VM, used to authenticate it within a network. Sometimes used interchangeably with NHI, though NHI is the broader category.
• Identity context: The entitlement, ownership, and purpose information that explains why an action occurred and whether it was expected. For security operations, identity context turns raw alerts into decisions by showing which human or non-human identity acted and what it was allowed to do.
• Ownership Gap: An ownership gap exists when an identity has no accountable person or team responsible for remediation and approval. For AI agents and service accounts, that gap blocks closure, weakens auditability, and turns detection into unresolved backlog instead of risk reduction.
• Ownership-Routed Remediation: Ownership-routed remediation is the practice of assigning a prioritised vulnerability or exposure to the team that can fix it, along with enough context to act quickly. It reduces queue confusion, avoids orphaned findings, and connects detection work to the systems and people responsible for resolution.

What's in the full announcement

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

• How agentic security findings are routed across connected systems and queued for action
• The access-graph context behind each flagged identity, including apps, accounts, and entitlements
• The remediation workflow for assigning owners, revoking access, and recording audit evidence
• How imported findings from other tools inherit the same tagging, routing, and audit trail

👉 C1.ai's full post covers the remediation workflow, access graphs, and audit trail details behind the findings.

Deepen your knowledge

NHI governance, agentic AI identity, and machine identity security 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.