AI Agent Identity Tools: What Each Category Secures and Where It Stops

Non-human identities outnumber human users in a typical enterprise by roughly 45 to 1, a ratio Token Security puts at the center of its pitch, and in cloud-native estates it climbs higher. Every service account, API key, OAuth token, and now every AI agent carries one. When an agent authenticates to an LLM and starts moving data, that identity is the thin line between a permitted action and an exposure. I want to walk through the tools that secure these identities, sorted by the job each one does, because the category has collapsed into a single marketing message while the jobs underneath it stay distinct.

Buyers get burned when they assume one tool covers all of it. A platform that finds every agent in your environment answers a different question from one that issues an agent a short-lived credential, and both answer a different question from one that rules on a specific prompt as it leaves for a model. This is a buyer guide for AI agent identity management, organized by function, with named vendors and honest notes on where each stops.

The four jobs behind AI agent identity

Sort the market by what a tool actually does at runtime and four jobs separate out.

Discovery and posture comes first: find every non-human identity, classify it, flag the over-permissioned and the orphaned. Issuing and brokering is the second job: hand a workload or agent a scoped, short-lived credential instead of a static key that lives forever in a config file. Runtime authorization is third: at the moment a request leaves for a model, decide whether this caller, in this role, against this data, is allowed to make it. The fourth job is the record: a per-decision audit entry an auditor can read months later, written independently of the application that made the call. Most tools cluster around the first two. The third and fourth are where coverage thins out, and they are the ones a regulator asks about.

Non-human identity security platforms find and govern the inventory

Astrix Security, Oasis Security, and Token Security build in this lane. They discover non-human identities across SaaS, cloud, and on-prem, classify them, map their permissions and connections, and surface the risky ones for remediation.

Astrix extended its platform to AI agents through an Agent Control Plane that inventories managed and shadow agents and applies policy on what those agents can reach. Cisco announced its intent to acquire Astrix, and Astrix stopped selling new standalone licenses as of June 30, 2026, with its capabilities moving into Cisco Identity Intelligence. I covered that category shift in the Cisco-Astrix identity-aware AI move. Oasis Security raised $120 million in March 2026 and shipped a Zscaler integration in June 2026 to extend zero trust to agentic identities; its platform discovers, classifies, and governs machine identities across hybrid cloud. Token Security frames the same job around ephemeral, context-aware tokens for agents and service accounts.

These platforms give you the map and the governance. The pass-or-block ruling on a single live AI call, and the independent record of that ruling, sit further down the request path. That is the boundary of the discovery-and-posture category, and it is worth knowing before you buy expecting inline enforcement. For the wider category framing, see non-human identity security.

Workload identity brokers and cloud-native IAM issue the credential

The second job is handing an agent an identity it can use safely. Aembit sits here, positioning agents as workloads rather than users and brokering secretless, short-lived access to the resources a workload needs. Aembit IAM for Agentic AI became generally available in April 2026 and added support for Microsoft Copilot Studio, applying policy at the moment a workload requests access and issuing a credential scoped to that request.

The cloud providers ship native versions of this. AWS IAM Roles Anywhere, Microsoft Entra Workload ID with managed identities, and Google Cloud Workload Identity Federation all let a service or agent exchange a trusted attestation for a short-lived, scoped credential instead of a static API key baked into code. For workloads that live inside one cloud, the native option is often enough, and it carries no extra vendor.

Brokering decides whether a workload may obtain a credential to a resource. Once the agent holds a valid token and opens an HTTPS call to an LLM, the content of that prompt becomes a separate decision that the broker does not evaluate. A correctly issued, correctly scoped credential can still carry a prompt that leaks source code or customer PII to the model. That is the post-authentication gap: authentication and access brokering answer who is calling, not what this caller is permitted to send right now.

Runtime enforcement and per-decision audit rule on the live AI call

The last two jobs happen on the wire, at the AI request boundary, and they are the ones the tools above hand off. Authentication confirms the agent, brokering scopes its credential, and discovery already knows it exists. The open question is the one that reaches the model: is this authenticated caller, in this role, against this data classification, allowed to send this specific prompt to this specific endpoint at this moment, and where is the signed record that says the ruling was made.

That question has teeth because attacks now move at machine speed. Google Mandiant's M-Trends 2026 report put the median handoff from initial access to a secondary threat group at 22 seconds in 2025, down from over 8 hours in 2022. On March 18, an internal Meta AI agent exposed sensitive data to engineers who should not have seen it; the exposure ran two hours and Meta classified it Sev-1. The agent was fully authenticated the whole time. If I had to name the weakest link in most AI identity programs, it is the audit record, because teams build discovery, brokering, and content scanning, then discover they have no independent per-decision log when a regulator asks for one. EU AI Act Article 12 requires automatic event logging over a high-risk system's lifetime, with timestamps, inputs, and the identities involved, and those obligations land August 2, 2026.

DeepInspect

This is the gap DeepInspect closes. DeepInspect is a stateless proxy that sits inline between your authenticated users and agents and the LLM endpoints they call. For every request it evaluates who is asking, what role they hold, what data classification is in play, and organizational policy, then makes a pass or block decision before the traffic reaches the model. Enforcement runs under 50 milliseconds in internal testing, invisible against the 500 milliseconds to several seconds a model takes to respond, so there is no throughput argument against making it inline.

DeepInspect does not discover your identities, vault your secrets, or run your directory. Astrix, Oasis, Token Security, and Aembit do that work, and the cloud providers issue the underlying workload credentials. DeepInspect assumes those identities exist and enforces policy on the AI call each one makes, then commits a per-decision audit record before the response returns, written by something other than the application that made the call. That write-path independence turns a log into evidence. Run a non-human identity platform for discovery and governance, run a workload broker for credentials, and put identity-bound enforcement and an independent audit trail underneath them on the live traffic. With the EU AI Act high-risk obligations landing August 2, 2026, that record is the part most teams have not built. If you are facing the August deadline, let's talk.