Curity

Access isn't a decision you make once at login. Especially not for agents. The industry spent a long time treating identity and access as roughly the same problem. That works until the thing holding the token is an agent that acts unpredictably across many services. At Curity we've focused on access from the start, not identity. The distinction matters because the token itself needs to be alive. It needs to reflect what the caller is allowed to do now, in this context, not what was true at the moment of authentication. Token intelligence is how we implement that. Tokens that can be designed, scoped, and reshaped over their lifetime. Access can narrow when a task is done. It can widen again when the agent wakes up and refreshes the next morning. That's yours to decide. Which means access becomes something you architect, not something you provision once and hope for.

We are not a SaaS. That's a deliberate decision. Building the most advanced identity server on the market means letting our customers actually operate it. Deploy it where their infrastructure lives, run it on their own uptime, keep the data where it belongs. None of that is possible if the server sits behind our cloud console. We built Curity as a cloud-native component instead. Easy to deploy, easy to run, and fully operable by the teams using it. Which gives our customers a modern deployment model without handing us control of their identity and access layer. That's what self-hosted actually means when it's done properly. Not shipping a legacy on-prem product, and not pretending SaaS is the same thing.

How many of the permissions your agents hold does anyone still remember issuing? The default when granting access to a new service or agent is to hand it a set of permissions upfront. It works while someone remembers what got issued and why. That memory fades fast. Six months in, the estate is full of agents running with permissions nobody can account for. And every one of those waits for something to go wrong before anyone notices. Zero standing privilege is the model that fits agents. Nothing gets issued upfront. Access gets granted on demand, scoped to the request, and released when the task is done. Which means the exposure doesn't accumulate over time, and the audit trail actually reflects what's happening now.

Your AI strategy is only as good as the APIs it will actually run on. But can your API estate support AI securely enough to scale? Most AI strategy decks are ambitious. The security and access infrastructure underneath them hasn't always had the same investment. That becomes a problem when a promising pilot needs to move into production. AI at scale means more API calls, more machine identities, more autonomous agents and more access to valuable data and services. AI that can't scale securely can't scale commercially so your system needs to be able to: ✔ Govern access at the point of request ✔ Secure human, machine and agent access ✔ Enforce the right permissions as AI reaches sensitive resources ✔ Scale without authentication and authorization becoming bottlenecks ✔ Adapt to new access patterns without creating security gaps The question is worth asking is not whether your APIs are ready for AI but - are they secure enough to support where your business wants to take it?

AI agents are applications. Highly skilled ones, autonomous, and dangerous in the wrong hands. The instinct to give them human-shaped identities makes the whole problem harder to reason about. It borrows the wrong mental model from the start. The real question should be what the API on the other end needs to know before it grants access. That's application-centric identity. Getting to that frame changes how the whole model gets designed. Access decisions stop being about matching an identity to a set of permissions. They start being about what the target service needs to verify at the moment a request arrives. The identity model that works for AI is one that answers that question well, not one that pretends the agent is another user in the system.

Treating an AI agent as another user in your identity system is a major business risk companies don’t seem to realize soon enough. The problem is that the existing model was built for people, and the exposure it creates for AI is a very different thing to defend. Agents don't behave anything like users, and they don't fail like users either. When something goes wrong, you don’t get a compromised account, but a service making thousands of unauthorised calls before anyone notices. And the impact lands in places the incident playbook wasn't written for. That's a different kind of exposure to sign off on. It sits with whoever owns the platform, not the individual team that put the agent live. The exposure compounds every time another agent gets provisioned into the same model. And it doesn't get easier to unwind later.