For years, the most visible use of enterprise generative AI was relatively simple. An employee would open an application, type in a prompt and get an answer. Humans were still the ones deciding what to do next, and access was governed largely through the employee’s identity.
That model is changing fast. Today, AI agents can carry out workflows. They can query databases, raise tickets, prepare payments, write code and call other applications, with varying degrees of autonomy. For example, an agent in a finance team could review overdue invoices, flag discrepancies, email vendors and prepare payments for release, subject to defined approvals. To do this, they need access through credentials, API keys, tokens and permissions. In short, AI agents are becoming participants in enterprise systems. Like any participant, their access needs to be identifiable and accountable.
A New Class of Identity
Most organizations manage human and non-human identities, including service accounts. AI agents introduce additional complexity because their activities may not follow the predictable patterns of conventional automation. Service accounts typically support applications or automated processes with defined permissions. An AI agent, on the other hand, may interpret instructions, choose tools and take actions based on changing context. An agent may still use a service account, but its decisions and activities require closer scrutiny.
Unlike humans, AI agents can operate continuously and perform tasks at machine speed. They can be duplicated or deployed in seconds. That makes them powerful but potentially hard to track.
This creates a visibility problem. Agents are often set up by business teams or developers who are testing capabilities. Sometimes they use borrowed credentials. They may also be granted broader permissions than their task requires. Without a central view, organizations can end up with unmanaged agent access and no clear ownership. Each identity can provide access to data and systems. Consider a developer who plugs their own admin API key into a pilot support agent: the key may remain active long after the pilot is forgotten.
Where the Risk Concentrates
For CIOs and CISOs, several governance issues stand out.
First, privilege levels are often too high. It’s easier to grant access than to precisely define what each agent needs. An agent with excessive permissions can expose or change much more than necessary. This risk grows if someone manipulates the agent through prompt injection or steals its credentials.
Second, there is a risk of data exposure. An AI agent might summarize information that includes confidential, personal or regulated data. It could show that data to users who were never meant to see it. This can happen when the agent has excessive permissions or when existing access controls are not consistently enforced across connected applications.
Third, accountability can be unclear. If an agent performs an action, who’s responsible? If logs only show a shared service account, it becomes very hard to trace the action to a person, a purpose or an approval. A large refund recorded only under “svc-automation” is a good illustration, because the record alone may not reveal who approved it or why.
Fourth, monitoring becomes more complex. Traditional monitoring may not provide enough context to distinguish legitimate agent activity from unintended or malicious actions. With agents operating across several systems at speed, unusual activity may go unnoticed until it causes damage.
Building the Framework
We don’t need to abandon the security principles we already have. We need to apply them to a new type of digital actor.
Treat every AI agent as having an identifiable, accountable presence within enterprise systems. Each deployed agent should have a traceable identity, with actions linked to a named owner and a defined purpose. Its lifecycle should cover onboarding, periodic access reviews and decommissioning.
Apply the principle of least privilege. Give access only to what a specific task requires. Use scoped permissions, time-limited credentials and, where appropriate, just-in-time access instead of permanent privileges. A procurement agent, for instance, could receive read-only access to the vendor list through a token that expires after 30 minutes, rather than a permanent login. High-impact actions, such as releasing payments or deleting sensitive data, should be subject to defined approval controls, including human review where warranted.
Extend Zero Trust principles to agents. Verify identities, enforce context-aware access policies and manage secrets securely, just as organizations would for other identities operating in critical systems.
Maintain audit records of what agents access, the actions they take and, where applicable, the user request or approval behind them. Set behavioral baselines, investigate anomalies promptly and ensure access can be revoked when needed.
Make accountability clear. IT, security, legal, risk and business teams should agree on which agents can be deployed, the data they can access and who is responsible for their actions.
For enterprises, this also means assessing whether their existing identity and access management, privileged access, API security and monitoring capabilities can be extended to AI agents. These controls need to work across applications, cloud platforms and third-party integrations, rather than being implemented separately for each new AI project.
Balancing Speed with Control
There is pressure to adopt AI. Teams want to move. Strict controls could push them toward unapproved tools and workarounds. A better approach is to make the approved path the easiest one. A team stuck in a slow approval process might simply start feeding company data into an unapproved public AI tool.
Give teams access to approved platforms, clear usage guidelines and security controls built into the deployment process. If employees can work within those boundaries without needless delays, they are less likely to bypass them.
Existing data protection and sector-specific regulations, along with evolving AI governance requirements, are increasing the need for organizations to demonstrate accountability over how automated systems access and process sensitive information. When organizations have clear access controls and reliable audit trails, they are better placed to support their compliance obligations.
The Road Ahead
The number of AI agents will likely keep rising. Their level of autonomy is also set to grow. The organizations that succeed will be those that treat them as accountable participants in enterprise systems, not simply as tools with unrestricted access.
They will know which agents have access, why it was granted, how long it remains valid and who is accountable for their actions.
For CIOs and CISOs, giving an AI agent an identity is only the beginning. The real test is whether their organizations can define its authority, trace its actions and intervene when necessary.
Authored by Suchit Karnik, Chief Operating Officer, RAH Infotech
