The question is no longer only, "Who has access to our systems?" Leadership increasingly needs to ask, "What has access, what authority have we given it, and what is it allowed to do on our behalf?"
For the last several years, most conversations about artificial intelligence in business have focused on what employees can do with AI. Generate content. Summarize documents. Analyze information. Write code. Improve productivity.
AI agents change that conversation.
Instead of simply responding to a prompt, an AI agent may be designed to take action. It can potentially access business applications, retrieve information, interact with data, initiate workflows, communicate with other systems, and perform tasks on behalf of an employee, department, or organization.
That distinction has significant implications for business leaders.
That is why identity governance is becoming one of the most important foundations for the next phase of enterprise AI.
AI Is Moving From Assistant to Actor
Traditional generative AI primarily helps users create or interpret information. AI agents are designed to go further by pursuing goals and taking actions, sometimes with limited human intervention.
An agent could eventually help a sales organization update CRM records, allow an employee to complete an internal workflow, assist IT with service requests, gather information from multiple applications, or trigger business processes based on predefined conditions.
NIST describes agentic AI architectures as systems that can dynamically acquire context, process information, and potentially take action. The organization is now actively examining how established identity concepts such as identification, authentication, and authorization should apply to these systems.
The business opportunity is significant. So is the governance challenge.
When software can act on behalf of people, the permissions granted to that software become just as important as the permissions granted to employees.
Every AI Agent Creates an Identity Question
Organizations have spent decades building identity and access management programs around human users.
An employee joins the organization. They receive an account. Access is granted based on their role. Permissions may change as responsibilities change. When the employee leaves, access is removed.
AI agents do not fit neatly into that traditional model.
An agent may act autonomously. It may act on behalf of a specific employee. It may support an entire team. It may interact with multiple applications or other agents. And its access requirements may change as its responsibilities evolve.
Major identity platforms are already beginning to address this challenge directly. Microsoft, for example, now defines dedicated agent identities within Microsoft Entra and provides governance capabilities intended to manage their access, ownership, lifecycle, and security controls.
For executives, the technology itself is less important than the underlying governance principle:
An AI agent that can interact with business systems should have a clearly defined identity, clearly defined authority, and clearly defined accountability.
Without those controls, organizations risk creating a growing population of digital actors whose access may be difficult to understand or control.
The Risk Is Not the Agent. It Is the Authority Behind It.
Consider an AI agent designed to assist a finance department.
At first, it may only need access to retrieve invoices and summarize financial information. Over time, the organization may connect it to additional systems so it can reconcile records, update financial applications, generate approvals, or trigger workflows.
Each new capability may require another permission.
Individually, those permissions may seem reasonable. Collectively, they can create an agent with access across several sensitive systems.
This is the same privilege-creep problem organizations have managed with employee accounts for years, except an AI agent may be able to execute actions at machine speed and scale.
CISA and international cybersecurity partners have specifically warned organizations about risks including broad access, privilege creep, expanded attack surfaces, behavioral misalignment, and difficulties maintaining clear event records as agentic AI systems are deployed. Their guidance recommends avoiding unnecessarily broad access and incorporating agentic AI directly into organizational security and risk models.
For leadership teams, this means AI governance cannot be separated from identity governance.
Identity Governance Has to Expand Beyond Employees
Most organizations already have some process for governing employee access.
AI agents require organizations to extend those same principles to non-human identities while accounting for the unique ways agents operate.
Who owns the agent? Every production AI agent should ultimately have an accountable business or technical owner. Someone needs responsibility for determining what the agent should be allowed to access and whether that access remains appropriate.
What can the agent access? Permissions should be intentionally scoped around the agent's business purpose rather than providing broad access simply because it is easier to configure.
What can the agent actually do? There is an important difference between allowing an agent to read information and allowing it to modify data, approve transactions, communicate externally, provision resources, or trigger other workflows.
Whose authority is the agent using? Organizations need to understand whether an agent is acting under its own identity, using delegated permissions from an employee, or relying on shared credentials or service accounts.
How is access reviewed? Agent permissions should not become permanent simply because they were appropriate when the agent was created.
What happens when the agent is no longer needed? Agents need a lifecycle. When their purpose changes or disappears, their credentials, integrations, permissions, and access should change with it.
Microsoft's emerging governance model for agent identities reflects many of these principles, including accountable human sponsors, access governance, Conditional Access, lifecycle management, activity monitoring, and the ability to disable or restrict agent identities.
AI Agents Make Least Privilege More Important, Not Less
The principle of least privilege is straightforward: an identity should have only the access necessary to perform its job.
AI agents make that principle considerably more important.
Organizations may be tempted to give an agent broad access because doing so allows the system to perform more tasks and reduces implementation friction. But every additional permission also increases the potential impact of an error, compromised integration, manipulated instruction, or unintended agent behavior.
The goal should not be to prevent agents from doing meaningful work.
The goal should be to establish boundaries that allow them to perform meaningful work without giving them unnecessary authority.
In many cases, that may mean separating agents by function rather than creating one highly privileged agent capable of performing dozens of unrelated activities.
Accountability Cannot Become Ambiguous
AI agents also introduce an executive governance question that extends beyond cybersecurity.
If an AI agent takes an action inside the business, who is accountable for that action?
If it changes a record, sends a communication, accesses confidential information, or initiates a workflow, leadership needs confidence that the organization can determine:
- Which agent performed the action
- What identity and permissions it used
- What initiated the action
- What systems and data were involved
- Who was responsible for the agent
- Whether the activity was consistent with approved business rules
This makes logging, monitoring, identity attribution, and human ownership critical components of responsible AI adoption.
An organization cannot effectively govern actions it cannot attribute.
The C-Suite Conversation Needs to Change
Executives do not need to become identity engineers to prepare their organizations for AI agents.
But they should begin asking different questions.
Rather than simply asking whether the company is adopting AI, leadership teams should understand whether the organization has a framework for governing the identities, permissions, integrations, and authority behind its AI systems.
That means bringing together functions that have historically operated separately.
IT understands the applications and infrastructure. Security understands identity and access risk. Business leaders understand the processes agents are being asked to automate. Legal and compliance teams understand the obligations associated with the information being accessed.
AI governance requires those perspectives to converge.
Build the Governance Model Before the Agent Population Explodes
AI agents are still emerging, but the direction is becoming clear. NIST launched an AI Agent Standards Initiative that includes research into agent authentication and identity infrastructure, reflecting the growing importance of establishing trusted interactions between people, software, and AI agents.
Organizations have an opportunity to establish the right governance foundations before hundreds of agents begin appearing across business applications and departments.
That foundation should include visibility into where agents exist, clear ownership, individual identities where appropriate, least-privilege access, lifecycle controls, monitoring, and policies governing what agents are permitted to do.
Waiting until agents are deeply embedded across the business will make those controls considerably more difficult to establish.
Identity May Become the Control Plane for Enterprise AI
AI governance is often discussed in terms of policies, models, and data. Those areas are important, but as AI systems become capable of taking action, identity becomes a critical control point.
Identity determines what an agent can access.
Permissions determine what it can do.
Governance determines whether that authority remains appropriate.
And visibility determines whether the organization can understand what happened after an action occurs.
For executives evaluating the next stage of AI adoption, the goal should not be to slow innovation. It should be to ensure that as AI gains greater autonomy inside the organization, its authority remains intentional, visible, and controlled.
The organizations that prepare for that shift now will be in a much stronger position to take advantage of AI agents without losing control of the systems and information those agents are being asked to use.
Preparing Your Organization for Agentic AI
AI agents introduce a new layer of identity, security, infrastructure, and governance considerations that many organizations have not yet incorporated into their existing controls.
L3 Networks helps leadership and IT teams evaluate how emerging AI capabilities intersect with their current environment, identify potential control gaps, and develop a practical approach to adopting AI securely.
Schedule a conversation with the L3 Networks team to discuss how your identity and security strategy may need to evolve as AI agents become part of your organization.
