Your AI agents are identities now — and most of them are ungoverned

A person signs in. An AI agent does not — it carries a credential and acts. Every copilot, workflow agent, RAG worker and automation you deploy is a new identity on your network: one that can read data, call tools and spend money without anyone ever logging in. The security story dominating mid-2026 is that these non-human identities are multiplying faster than anyone is counting them.

The identity nobody is counting

Non-human identities — service accounts, bots, tokens, and now AI agents — already outnumber human ones many times over. Estimates vary widely, from roughly ten-to-one in some environments to as high as eighty-to-one in others, but every credible source agrees on the direction. In its 2026 cybersecurity outlook, KPMG named non-human identity a top priority for security leaders and called its uncontrolled growth a “ticking time bomb.” The Cloud Security Alliance put numbers on the gap: in a survey published in January 2026, fewer than a quarter of organisations had a documented, adopted policy for even creating or removing AI identities, more than one in six did not track when new AI-related identities were created at all, and just 12% were highly confident they could stop an attack that arrived through a non-human identity.

Why your existing IAM cannot see them

Identity and access management was built for people: a human, a login, a session, a manager who offboards them on their last day. AI agents break every one of those assumptions. They are created in code, often outside any central directory; they authenticate with long-lived API keys rather than interactive logins; and when a project ends, nobody walks them to the door — the key just keeps working. The result is over-provisioning at scale. In OWASP’s 2026 review of agentic-AI security, around three-quarters of respondents said their agents routinely hold more access than they need, and 79% said agents introduce new access pathways that are hard to monitor; only about a third said they apply the same controls to this digital workforce that they already apply to human staff. Meanwhile the credentials themselves leak: shared “god keys” copied into config files, environment variables and notebooks are the agent-era version of a password on a sticky note.

A non-human identity gets governed at the gateway

Here is the structural point. Every one of these agents has to call a model, and if it calls that model through a gateway, the gateway is the one place its identity can actually be governed. That is what Qevron is. Point an agent at a single OpenAI-compatible endpoint and, instead of handing it a shared key to the whole world, you issue it its own — scoped to the models and routes its job needs, capped with a budget and a rate limit, logged on every request so you can see what each agent actually did, and revocable the instant it is compromised or retired. The agent never gets an open door; it gets a named credential the gateway checks on every call. The discipline IAM applies to people — least privilege, attribution, fast revocation — is exactly what a gateway can apply to non-human identities, because it sits on the one path they all share.

Run through a gateway, basic identity hygiene for a fleet of agents stops being aspirational and becomes the default:

• A distinct identity per agent — never one shared key nobody can attribute later
• Least privilege — each agent scoped to the models, routes and tools its role actually needs
• A budget and rate limit on every identity, so a runaway loop hits a ceiling instead of a bill
• Full attribution — every model and tool call traced back to the agent that made it
• Instant revocation in one place the moment an agent is compromised, replaced or retired

A gateway is not the whole answer, and we won’t pretend it is. It governs the traffic that passes through it; an agent that reaches a database or a SaaS tool by some other path still needs its own controls, and the unglamorous work — rotating keys, retiring stale ones, owning each identity — is work no proxy does for you. What the gateway gives you is the one choke point where the model side of every agent can be named, scoped, watched and switched off.

The half a hosted tool can’t give you: identities that never leave your perimeter

There is a catch in most of the tools shipping to solve this: the control plane itself usually runs on someone else’s cloud. The one component that sees every agent’s identity, every credential and every action then lives outside your control — and under a foreign-owned provider, it can fall under a foreign jurisdiction. Arpanet is built the other way. The models and the gateway are ours, so the whole stack runs on-prem or in an isolated deployment, behind your own SSO, with the audit trail inside a perimeter you control. For a data controller under Türkiye’s KVKK this is not a nicety: Article 12 puts the duty to secure personal data squarely on you — and you cannot secure access you cannot see. Keeping the choke point on your side of the line is the difference between a control you can prove and a promise you have to trust.

An agent is only as governed as the credential it carries — and the safest place to issue, watch and revoke that credential is infrastructure you own.

AI agents are the fastest-growing population on your network, and they all act with credentials rather than logins. The teams that stay in control through 2026 are the ones treating every agent as an identity to be issued, scoped and revoked — at a choke point they run themselves. That is the seam Qevron already sits on, engineered for the KVKK from the first line of code. Pricing depends on your deployment and scale; contact us and we will scope it with you.