The Rise of AI Agents in Enterprise Applications: Managing Governance and Risks

In a recent report, Gartner predicts that by the end of 2026, 40% of enterprise applications will incorporate task-specific AI agents, a significant increase from the current less than 5%. This trend is exemplified by Google’s launch of Gemini Enterprise, a dedicated app catering to business users. This integration of AI into enterprise workflows holds the promise of unprecedented productivity gains, but it also introduces new levels of risk that organizations need to address.

AI agents, unlike human users, operate continuously, interact directly with application APIs, and can execute tasks at a speed and volume that surpass human capabilities. These agents make contextual decisions and adapt to inputs, distinguishing them from traditional robotic process automation tools. As organizations embrace AI agents, governance frameworks must evolve to ensure proper oversight and control.

Distinguishing AI Agents from Human Users

AI agents are provisioned with accounts and access rights similar to human users, carrying out tasks such as processing invoices, approving workflows, and analyzing contracts. However, their operational differences, including continuous operation and direct API interaction, pose unique challenges for governance. Misconfigurations in agent permissions can have far-reaching consequences, highlighting the need for robust governance practices.

The Challenge of Auditability and Explainability

One of the key operational concerns with AI agents is the ability to audit and explain their decisions. Unlike traditional software, AI systems do not follow deterministic rules, making it challenging to reconstruct their decision-making process after the fact. Organizations must implement behavioral monitoring and structured audit trails to investigate incidents effectively and ensure compliance with regulatory requirements.

Managing Unsanctioned AI Use

In addition to formally deployed AI agents, organizations must address the use of external AI services by employees without proper oversight. When sensitive data enters unmanaged third-party tools, existing data controls and compliance workflows may no longer apply. A comprehensive governance approach should encompass all AI usage within the organization, not just authorized deployments.

Implementing Governance for AI Agents

Governing AI agents requires a rigorous approach similar to that applied to human users, with adaptations to account for their unique behavior. Key areas of focus include scoped access permissions, behavioral monitoring, audit trails, and consistent policy enforcement across all layers where agents operate. By building governance into AI deployments from the outset, organizations can harness the benefits of AI while mitigating risks effectively.

Chris Radkowski, an SAP GRC expert at Pathlock, emphasizes the importance of incorporating governance into AI deployments to navigate the complexities of identity security and compliance. With over 20 years of experience in driving innovation in enterprise security, Chris brings deep expertise in access governance, risk management, and regulatory compliance.

For more insights from Chris Radkowski, visit his LinkedIn profile and explore the offerings of Pathlock at pathlock.com.