What is a non-human identity?

A non-human identity is any credentialed actor in a system that is not a person: a service account, an API key, an OAuth client, a workload or container identity, a signing certificate, or an AI agent holding a token. Each authenticates and acts against other systems the way a human account does. The difference is scale and behavior. Palo Alto Networks counted 109 machine identities per human in the average 2026 enterprise, and most of them carry static long-lived credentials with no interactive login and no second factor, which is why they need their own governance model rather than the one built for employees.

Why are non-human identities harder to secure than human accounts?

Human accounts get MFA, SSO sessions that expire, and a directory entry that ties activity to a name. Machine identities carry long-lived keys that rarely rotate, have no human present to satisfy MFA, and often share one credential across many services. GitGuardian counted 28.65 million secrets hardcoded into public GitHub in 2025 alone. The sharing is what breaks attribution: when several callers use one key, the audit record captures the key and loses the caller, so during an incident the investigation reaches a credential and cannot go further.

How do AI agents change non-human identity security?

AI agents reason and act on their own, and teams commonly start them on a single shared API key to reach an LLM. Every agent's prompts and tool calls then land in the record under the same credential. When one agent sends data it should not, the forensic trail goes cold at the key, because nothing distinguished the agents from each other at request time. Securing agent NHIs means supplying per-agent identity on each call and authorizing that specific actor, so the record names the agent instead of the shared secret.

Is DeepInspect a secrets manager or a CIEM?

DeepInspect is neither of those. It governs HTTP AI traffic between authenticated users or agents and LLM endpoints. It authorizes each AI call against the identity and policy in play and writes an independent per-decision audit record. Secret storage and rotation belong to a vault, and cloud infrastructure entitlement belongs to a CIEM tool. DeepInspect runs alongside those, adding authorization and attribution at the moment an AI request leaves your environment, which is the point where a shared key otherwise erases the actor.

What does binding an AI call to an identity actually produce?

It produces a record that names the actor rather than the credential. When your application passes per-agent or per-user identity context, DeepInspect evaluates that actor's role, the data classification in the prompt, and policy, then commits an audit entry before the response returns. Instead of "an API key called the model," the log reads as a specific agent or user, under a specific delegated authority, sending a specific request, with a pass or block outcome. That record is what an auditor or an incident responder can actually use months later.

Non-Human Identity Security When Machines Outnumber People 109 to 1

Palo Alto Networks' 2026 identity security report counted 109 machine identities for every human identity inside the average enterprise, up from 82 to 1 a year earlier. CyberArk's figure lands in the same band, north of 80 to 1. These non-human identities, service accounts, API keys, workloads, CI/CD runners, and now AI agents, already carry most of the traffic in a modern environment, and almost none of them authenticate the way a person does. Non human identity security is the practice of governing those identities across their whole life. The part that breaks first is attribution, knowing which non-human actor did a specific thing, at a specific time, against a specific system.

I want to walk through what an NHI actually is, why the controls built for human accounts fail on machine accounts, and the specific twist AI agents add when several of them share one credential. This connects up to the broader work on AI agent identity management, and it narrows in on the security and attribution side of the category.

What counts as a non-human identity

A non-human identity is any credentialed actor in your systems that is not a person. The common types: a service account that runs a nightly ETL job, an API key that lets one microservice call another, an OAuth client that a SaaS integration uses, a Kubernetes service account bound to a workload, a signing certificate, and increasingly an AI agent holding a token so it can call an LLM or a downstream tool. Each one authenticates, gets authorized, and acts, the same three steps a human account goes through.

The volume is the first problem. Entro Security put the ratio at 144 to 1 in cloud-native environments in 2025, up from 92 to 1 eighteen months earlier. When identities grow that fast, the ones nobody remembers pile up. I have watched a security team find a service account named svc-etl-prod, created in 2021, whose key had been pasted into a Slack channel two reorganizations ago, still valid, still reaching production. Nobody could say who owned it.

Why controls built for people fail on machine identities

Human identity security rests on a stack of assumptions that machine identities violate one by one. A person logs in through SSO, proves a second factor, and gets a session that expires. Their access maps to a name in the directory, so a log entry points at a human you can call.

Machine identities carry static, long-lived credentials instead. An API key issued in 2022 often still works in 2026 because rotating it means finding every workload that reads it and coordinating a cutover, and that work keeps sliding. There is no MFA step for a service account, because there is no human present to approve one. Keys get shared across services and copied into config files, environment variables, and repositories. GitGuardian's State of Secrets Sprawl 2026 found 28.65 million hardcoded secrets pushed to public GitHub in 2025 alone, a 34% jump year over year, and the private-repo number is worse.

The attribution failure is the one that costs you during an incident. When ten services and forty jobs share one key, the log records only that key. It cannot capture which caller behind the key made the request, because the request never carried that distinction in the first place.

AI agents make the forensic trail go cold at the credential level

An AI agent is a non-human identity that reasons and acts on its own. Give a fleet of agents one shared API key to reach an LLM, which is the default many teams start with, and the attribution problem sharpens to a point. Every prompt, every tool call, every piece of data an agent sends to a model shows up in the record under the same credential. When one of those agents exfiltrates a customer record or sends a poisoned instruction downstream, the investigation reaches the key and stops. Which agent, running whose task, under which delegated permission, sent that request? The credential holds no answer, because nothing at request time separated one agent from the next.

This sits on top of the post-authentication gap. Authentication confirms the token is valid. It does not confirm that this particular agent, doing this particular task, should be sending this particular prompt to this model right now. VentureBeat reported in 2026 on exactly this pattern, agents on shared API keys with no per-agent identity attribution, and it turns a compliance log into a document that names a secret rather than an actor. An auditor asking "who did this" gets an API key ID and a shrug.

What securing a non-human identity actually requires

Governing NHIs well runs across four workstreams, and they are owned by different tools. Discovery and inventory, so you know every key, account, and agent that exists. Secret rotation and vaulting, so credentials are short-lived and stored outside application code. Least-privilege entitlement, so a workload can reach only what its job needs, which is the domain of cloud infrastructure entitlement management. And authorization plus attribution at the point of use, so each individual action is checked against policy and recorded against a named actor.

The first three are adjacent to what DeepInspect does. A vault, a secrets rotator, and a CIEM tool handle inventory, storage, rotation, and cloud IAM entitlement. DeepInspect is none of those, and I would not pitch it as one. My opinion, and a committee would cut it, is that the industry oversold vaulting as the finish line for machine identity. A rotated, well-stored key still authorizes anyone holding it to do anything the key permits. That is the workstream the identity-aware category Cisco and Astrix are helping define is built around, and it is where AI traffic specifically needs a decision on every call.

DeepInspect

This is the gap DeepInspect closes for the AI traffic slice of the problem. 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 the identity presented, the role that identity holds, the data classification in the prompt, and organizational policy, then passes or blocks 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 answer.

The attribution part is what changes for non-human identity security. When your application supplies per-agent identity context rather than a bare shared key, DeepInspect authorizes each AI call against that specific actor and writes a per-decision audit record naming it, independent of the application that made the call. The log stops saying "key svc-agent-07" and starts saying which agent, under which delegated authority, sent which prompt, and what the ruling was. DeepInspect does not rotate your secrets, run your vault, or manage cloud IAM. Run those tools alongside it, and put identity-bound authorization and an independent audit trail underneath your AI traffic. Book a demo today.