AI agents in the enterprise: Balancing speed and security

The adoption of AI agents within enterprise environments has surged in recent months. Whether developed in-house or licensed from third-party vendors, these agents are now widely embedded into enterprise workflows. While these agents offer tremendous potential to automate tasks, accelerate workflows, and enhance employee productivity, they also introduce new categories of risk.

As with most major technology shifts, the opportunities come intertwined with evolving vulnerabilities. The challenge for today’s enterprises isn’t simply adopting AI agents — it’s governing them. A bold yet balanced approach to innovation and governance is no longer optional; it’s critical.

From discovery to ownership: The lifecycle problem

One of the greatest challenges in managing AI agents is discovery and lifecycle management. In complex environments, often sprawling with AI agents, manual oversight isn’t sustainable. Any newly created, licensed, or deployed agent must be automatically discoverable. Otherwise, organizations risk losing control before they even realize it.

Another major hurdle is the question of ownership. Too often, agents are introduced without clear accountability. When the creator leaves, the enterprise is left with orphaned agents creating gaps in accountability and security. Formal lifecycle management, including ownership transfer protocols, is essential if AI agents are to remain an asset instead of a liability.

Guardrails, not guesswork: Defining security

Security parameters must also be carefully defined. Organizations need to determine the boundaries of operations: Where an agent is authorized to function? How are its permissions configured? Which data sources, applications, and tools are accessible? Without answers, risk multiples.

The solution lies in centralized governance. A cross-functional collaboration across identity, security, cloud operations, and AI development must establish and enforce clear boundaries. Unified rules, permissions, and governance frameworks ensure that AI agents operate with both agility and accountability.

Identity as a strategic connector

The rise of AI agents creates new opportunities for the identity function to play a strategic role. Establishing regular alignment with security and cloud teams ensures provisioning, oversight, and enforcement remain consistent. Security efforts should focus on ensuring that each agent is appropriately secured, with identity providing visibility through inventories, certifications and audit trails. This cross-functional approach strengthens both security posture and operational efficiency.

Emerging trends indicate that as language models become more sophisticated, resilient, and accurate, AI agents will increasingly collaborate to achieve shared objectives. In time, hierarchical structure may emerge, where agents orchestrate tasks collectively towards business goals. Organizations that foster innovation while embedding strong governance will be best positioned to thrive.

The bottom line: Innovation with accountability

AI agents are no longer an experiment at the edges of the enterprise — they are embedded into core workflows. Their ability to accelerate work and amplify productivity is undeniable, but so are the risks that surface when they are left ungoverned. Success is this new era will not come from adoption alone; it will come from disciplined governance.

Without unified visibility and control, the gap between identity context and security context widens, leaving enterprises exposed at the very moment they are accelerating into the AI era. Those that close this gap will be best positioned to capture the benefits of AI while maintaining the trust and security their business demands.