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01/08/2026 10:44 pm
TL;DR: Enterprises now manage more than 20,000 non-human identities per 1,000 human identities, while Onyx says 80% to 90% of AI agents were invisible before discovery and the median enterprise now runs over 1,000 agents. The control problem is not detection quality but identity attribution, runtime enforcement, and accountability that survive autonomous action.
NHIMG editorial — based on content published by Onyx: The 5 Questions I would ask any AI Security Vendor
By the numbers:
- As of 2024, for every 1,000 human identities in most enterprise environments, there were over 20,000 non-human identities: service accounts, API tokens, OAuth grants, webhooks, machine credentials of every description.
- Onyx data puts the median number of AI agents per enterprise at over 1,000, and between 80 and 90 percent of those agents were invisible to security teams before they were discovered.
- 68% of organisations do not know how to fully address NHI risks.
Questions worth separating out
Q: How should security teams govern AI agents that can access enterprise systems?
A: Security teams should govern AI agents as non-human identities with explicit ownership, scoped privileges, and continuous monitoring.
Q: Why do AI agents create new risk in non-human identity management?
A: AI agents create risk because they operate as software identities with delegated authority, but many organisations do not track them with the same discipline applied to users or service accounts.
Q: What do security teams get wrong about AI agent and NHI monitoring?
A: They often treat monitoring as a logging problem instead of an identity governance problem.
Practitioner guidance
- Map every agent to an owning identity and session model Require each AI agent to be bound to an initiating user, workload, or workflow identity, with session-level records for all tool calls and downstream actions.
- Test for enforcement at the action boundary Run a live policy-violation scenario and verify that the system can block or mask the action before data leaves the approved boundary.
- Define pre-execution review gates for high-risk actions List the action classes that require human approval, then confirm the agent session pauses until the reviewer responds.
What's in the full article
Onyx's full article covers the operational detail this post intentionally leaves for the source:
- How Onyx frames runtime attribution across human, workload, and agent-initiated actions.
- The vendor's five evaluation questions in their original wording, including demonstration prompts.
- Operational examples for action-boundary enforcement and human-review escalation paths.
- Onyx's discussion of cross-platform coverage across SaaS, cloud, coding, and MCP-related surfaces.
👉 Read Onyx's analysis of the five evaluation questions for AI security vendors →
AI agent identity governance: are your controls keeping up?
Explore further
01/08/2026 10:53 pm
AI agent security is an identity governance problem before it is a threat detection problem. The article correctly shifts the evaluation lens away from alert quality and toward attribution, control, and accountability. That is the right move because agents are being provisioned into production like ordinary software, while their behaviour has identity consequences that conventional app security does not model well. Practitioners should treat agent evaluation as a governance exercise, not a feature comparison.
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, with 77% of these incidents resulting in tangible damage.
A question worth separating out:
Q: How do security teams decide whether an AI workload is ready for production?
A: Use a governance test, not a marketing test. The workload is ready only if its models, dependencies, data sources, runtime controls, and resource limits are known, approved, and continuously monitored. If any of those elements are opaque, the deployment is still experimental from a security perspective.
👉 Read our full editorial: AI agent identity governance is lagging behind enterprise control
