Gartner estimates that 40% of enterprise applications will include task-specific AI agents by the end of 2026, up from less than 5% today. Google has recently launched a dedicated Gemini Enterprise app for business users, connecting enterprise data across productivity platforms like Google Workspace, Microsoft 365, Salesforce and other platforms.
This will elevate productivity in ways we’ve never seen before. But it also introduces risk at a scale we’ve never dealt with. These agents require broad access to systems and data and they can act hundreds or thousands of times faster than a human.
Meanwhile, governance frameworks are lagging behind adoption. If we don’t rethink governance for this new class of machine identities now, we risk turning enterprise systems into chaos comparable to the Wild West.
How AI Agents Differ from Human Users
AI agents are provisioned as accounts and granted access rights in much the same way human users are. They process invoices, approve workflows, reconcile transactions, generate customer communications, and analyze contracts. In that respect, they resemble any other user in the system.
The differences are operational. Unlike human users, agents can run continuously without interruption, interact directly with application APIs rather than through interfaces, and execute transactions at a volume and speed no individual could match. They also make contextual decisions adapting to inputs rather than following fixed scripts which distinguishes them from the robotic process automation (RPA) tools enterprises are already familiar with.
Those differences have practical consequences for governance. A misconfigured permission for a human user typically produces a localized problem. The same misconfiguration applied to an agent operating at scale can propagate across many transactions before it is detected. The blast radius is structurally larger.
The Auditability and Explainability Gap
One of the more pressing operational questions is what happens after an AI agent makes a consequential decision that turns out to be wrong. If an agent processes a high-value transaction incorrectly, or generates a customer-facing communication that creates a compliance issue, the organization needs to be able to reconstruct what happened: what inputs the agent received, what logic it applied, and what caused it to behave unexpectedly.
AI systems do not follow deterministic rule sets in the way traditional software does. Their outputs can be difficult to explain after the fact, particularly when behavior is unexpected. Without behavioral monitoring and structured audit trails, organizations lack the ability to investigate incidents effectively, satisfy regulatory inquiries, or make targeted corrections.
This is not a hypothetical concern. AI systems have already produced hallucinated outputs, fabricated references, and inconsistent decisions under certain conditions. When those behaviors occur within governed workflows connected to real business transactions, the consequences are material, not abstract. This is why explainability is emerging as a key requirement in several proposed AI regulations.
Unsanctioned AI Use Extends the Exposure
The governance challenge extends beyond formally deployed agents. Employees are increasingly using external AI services for drafting, summarization, analysis, and other tasks without organizational oversight. When sensitive data enters an unmanaged third-party tool, existing data controls and compliance workflows no longer apply.
Enterprises focused solely on governing the agents they have officially deployed are addressing only part of the risk. A comprehensive approach needs to account for how AI is being used across the organization, not just where it has been formally authorized.
Applying Governance to Non-Hum an Identities
Governance for AI agents requires the same rigor applied to human users, adapted to account for how agents actually behave. Four areas are especially critical –
Scoped access permissions
Least-privilege access, granting users only the permissions they need for their specific role is a well-established security principle that has often been inconsistently applied in practice. For AI agents, consistent application matters more. Because agents operate continuously and at volume, every unnecessary permission represents a persistent and scalable exposure. Access should be defined at the task level and reviewed regularly.
Behavioral monitoring
Governing what an agent is permitted to do is necessary but not sufficient. Organizations also need visibility into what agents are actually doing and whether their behavior falls within expected parameters. Monitoring designed for human activity is not well-suited to this purpose; machine-generated activity has different patterns, higher volume, and faster deviation from baseline. Specific monitoring for non-human identities is a distinct operational requirement.
Audit trails and decision logging
Actions taken by AI agents should be logged at a level of detail that supports meaningful review: not just what was done, but the inputs received and the context in which decisions were made. This is what makes post-incident investigation possible, and what allows organizations to demonstrate compliance when required. It’s an additional requirement to consider that AI agents will be necessary to monitor and review these actions and generate the appropriate audit reports.
Consistent enforcement across layers
AI agents interact with data, applications, and APIs. Governance policies applied at the application layer do not automatically extend to direct API interactions. Controls need to be enforced consistently across all layers where agents operate, rather than assumed to cascade from one.
Conclusion
The expansion of AI agents inside enterprise systems is a significant shift, and the governance implications are real but also manageable. The core requirements such as appropriate access scoping, behavioral monitoring, structured audit trails, and consistent policy enforcement are extensions of disciplines that enterprises already understand, applied to a new class of identity.
Organizations that build governance into their AI deployments from the start will be better positioned to realize the operational benefits while managing the risks. Those that treat governance as an afterthought will find it harder to retrofit later, and more exposed in the interim.
About the Author
Chris Radkowski is an SAP GRC expert at Pathlock, an identity security and governance platform. A recognized leader in access governance with over 20 years of experience driving innovation in enterprise security and compliance solutions, he brings deep expertise in application access governance, risk management and regulatory compliance.
Chris can be reached online at https://www.linkedin.com/in/chris-radkowski-aa9161/ and at our company website https://pathlock.com/
