AI Agents Are Redefining Credential Management

It should come as no surprise at this point that AI agents are fundamentally changing the role of credential management  in respect to continuous identity and governance. The common thought process is that enterprises should inventory every agent, replace standing secrets with scoped and short-lived access and make every action attributable to an owner. But how do they get there? 

Being able to exercise control of your credentials is essential. But traditional methods of credential rotation were made to  deal with credentials for humans. Once you switch into governing credentials for AI agents, the playing field changes drastically. You never want to give a static credential to an agent that will live on in perpetuity. Once that credential is in the inference window, you’ve lost control of it. You want to be able to give agents just the right amount of access for the right amount of time, so they can do their one job and be done with it.

Traditionally, credential management relied on managing the interaction between two forces: 1) the credential itself and 2) the identity using that credential. But over the past few decades, that equation has changed as passwords and credentials have evolved. Now we find ourselves in a real interesting quagmire where we’re dealing with human credentials, non-human identities (NHI), service accounts, secrets, machine account access keys and so on and so forth. How do we manage all of these and balance their various security risks and access needs?

Most recently, AI agents have fundamentally changed and expanded how we define and manage the “identities” that interact with those credentials. The natural question on every leader’s mind this cybersecurity month should be: how can credential governance change to manage this new form of identity?

Agents and NHIs challenge traditional identity models

In days of yore, identity and access management (IAM) could operate under certain assumptions about how a given identity will access systems. For instance, developers need access to the codebase, but not to company financial records, while finance teams have opposite needs. Beyond a few highly privileged users, it’s rare that anyone genuinely needs long-standing access to tools or systems that aren’t directly relevant to their role.

AI tools and agents are a different matter entirely. For one task, an agent might need access to a database; for the next, it might need access to the company’s visual design assets. NHIs and AI agents require access to a much wider variety of systems and data than human users with static roles do, but providing them with long-lived access to those systems could be a recipe for disaster.

Even when operating correctly, agents can interpret commands in extreme ways, such as in the PocketOS incident, in which an agent used a long-lived API token to delete an entire production database; teams need to ensure that they don’t have the access needed to make destructive decisions without oversight. 

1Password’s research found that 47% of developers have had an AI agent follow instructions from a prompt injection attack and 74% of developers witnessed unintended consequences from using AI agents.

Unfortunately, 1Password’s research also discovered that oversight into agent identities is severely limited; 40% of developers grant agents persistent access to systems or credentials, but 71% of technical workers report visibility gaps in knowing which agents are using credentials. In fact, 33% of IT and security respondents say they don’t even know how many NHIs or agents their company uses.

This means that even though agents and NHIs often have highly privileged levels of credential access, IT and security teams struggle to maintain the full visibility and governance that these privileged identities require.

Companies need a new credential access model

AI agents represent a new spin on an old problem with their utilization of identity and they require that companies redefine how they manage credentials and access for NHIs.

Visibility and discoverability are critical

It’s projected that the global population of active AI agents will grow from roughly 28.6 million in 2025 to 2.2 billion by 2030. As Forbes puts it, this figure represents “…a 139% compound annual growth rate.” That puts agent deployment on one of the steepest curves in software history.

IT and security teams and traditional credential management systems, have never had to manage a class of identity that proliferates so rapidly. They need systems that let them discover and monitor agents to surface the access that exists outside of traditional credential governance.

Least privilege must become adaptive

Traditional credential management often emphasizes storage and rotation over months or years. Those controls remain important, but the high-volume and high-velocity nature of agentic workflows requires another security layer to manage the pace at which agents interact with systems. 

Least privilege requires providing the minimum permissions needed to perform a role. This principle is much harder when an agent’s work changes from task to task. Instead, organizations need to implement automated systems that define credential boundaries at the task level. Give agents and NHIs the minimum credential access required for a specific task, for the shortest practical duration and revoke them when the task ends. This limits the blast radius if an agent is compromised.

Agent adoption starts with agent security

For forward-thinking enterprises that want to encourage their teams to adopt agentic AI, Cybersecurity Awareness Month is the perfect time to ask two critical questions: 1) how are we managing credentials right now and 2) how would that need to change to keep agentic access secure?

The future of credential management lies in building a trustworthy identity foundation for systems that can act at machine speed. Organizations that build that foundation now will be better positioned to use AI agents confidently and securely.