Identity and Access Management Meets HR: Governing AI Agents

Governing AI agents

Identity and access management is the foundation of cybersecurity. Before organizations can protect data, applications, and critical systems, they must be able to verify who or what is requesting access and determine what that identity is authorized to do. Zero-trust principles often provide a framework for identity and access management practices. Identity governance builds on this foundation by defining accountability throughout the identity life cycle. Effective identity governance confirms that identities are created, managed, monitored, and retired appropriately while maintaining clear ownership, least-privilege access, and regulatory compliance. For decades, HR teams have set workforce policy, defined roles and organizational structure, established manager accountability, supported leadership development, and led workforce change. HR information system (HRIS) platforms have served as the system of record for many of those decisions, while identity governance has translated them into digital controls that govern access, permissions, and accountability across the enterprise.

The rapid adoption of AI agents makes identity and access management, identity governance, and AI governance more complex. AI agents perform tasks that require access to enterprise applications, data, and business processes. They summarize information, generate content, execute workflows, support decisions, or interact autonomously with other systems. While AI agents are not human, they represent identities that must be authenticated, authorized, governed, and continually monitored.

Identity and access management and governance amid the rise of AI is both a cybersecurity and operational challenge. Organizations cannot simply provision AI agents with broad access and hope existing controls will suffice. Every AI agent requires a clearly defined identity, an assigned owner, appropriate permissions, life cycle governance, and continual oversight to reduce cybersecurity, operational, and compliance risk. The good news is that the principles organizations already rely on to govern human identities provide the foundation for governing AI agents. By extending identity and access management and governance to AI agents, organizations can establish the same accountability, visibility, and control that underpin cyber risk management and enable AI adoption without compromising security or trust.

5 pillars for governing AI agents

Preparing for AI agents doesn’t require new governance models. Most of the foundational concepts already exist across HR, HRIS, workforce management, identity governance, and risk management. The technology is new, but the questions are familiar: Who owns the work, who is accountable, how is performance measured, when should management intervene, and how does the life cycle end? For decades, the answers were straightforward, but integrating AI agents into the equation will require that HR, HRIS, identity and access management, and governance, cybersecurity, and risk management work together.

As organizations assign AI agents more work, they should apply the following five pillars to guide AI agent governance:

• Pillar 1: Ownership, accountability, and life cycle governance
• Pillar 2: Performance management for AI agents
• Pillar 3: Workforce and capacity planning
• Pillar 4: Organizational design and governance
• Pillar 5: Compliance, risk, and ethical oversight

Together, these five pillars support accountability and oversight of both employees and AI agents.

Pillar 1: Ownership, accountability, and life cycle governance

Accountability is foundational to workforce governance. Every employee has a manager; every role has an owner. Organizations assign responsibility for outcomes, provide oversight, and define escalation paths when issues arise. 

AI agents require similar governance. Every agent should have a clearly identified human owner who understands its purpose, expected outcomes, risks, and limits. That owner remains accountable for business outcomes regardless of how autonomously the agent operates. Ownership goes beyond technical administration; it covers how the agent performs, how it affects business processes, and how it changes over time.

Organizations should establish governance processes that define:

• Human ownership and accountability
• Business purpose and expected outcomes
• Escalation procedures
• Risk tolerance thresholds
• Life cycle management requirements

The concept likely seems familiar to both HR and identity governance professionals. HR teams assign managerial accountability for workers. Identity governance programs require accountable owners for applications, privileged accounts, and nonhuman identities. AI agents increasingly sit between those two worlds: They are not employees, but they are more than software.

Ownership isn’t symbolic. It takes time, attention, and training. As AI agents multiply, leaders should ask whether owners have the authority, expertise, and capacity to govern them. If no one can say who owns an agent, who understands its purpose, and who answers for its outcomes, that agent is a governance risk, however sophisticated it might be.

Implementing this pillar begins with implementing the joiner-mover-leaver life cycle. HR and identity governance teams use these processes to manage access and accountability when an employee joins the organization, changes roles, or leaves. The same basic discipline should apply to AI agents throughout their life cycle.

Pillar 2: Performance management for AI agents

Organizations know how to evaluate employee performance. They set expectations, measure outcomes, spot areas to improve, and step in when results slip. Few organizations have equivalent processes for AI agents, but just as organizations manage underperforming employees, business processes, and systems, they should establish governance mechanisms for managing underperforming AI agents.

As AI agents become more deeply integrated into business operations, organizations should establish clear expectations regarding performance, reliability, and acceptable risk. Organizations should define acceptable performance thresholds for accuracy, consistency, hallucination rates, drift, policy compliance, and error rates while also establishing expectations for operational resilience. When AI agents consistently operate outside established risk tolerances, governance processes should require intervention, retraining, additional supervision, or decommissioning from production use.

Oversight should match the stakes of the work. For example, an internal knowledge AI agent might need little supervision, but an agent involved in hiring, employee relations, financial reporting, regulatory work, or cybersecurity needs far more. Formal testing can track accuracy and reliability over time and guide how much supervision and which use cases are appropriate.

Pillar 3: Workforce and capacity planning

Organizations have always gauged workforce capacity through headcount, staffing models, and productivity metrics, all of which assume people do the work. AI agents upend that. As they take on real tasks, planning has to move past human capacity alone and account for how work will be performed by people as well as AI agents.

Capacity is only part of the workforce question. Organizations also should evaluate how AI agents reshape roles, workflows, required skills, supervisory responsibilities, and career paths. HR can help redesign work around human-agent collaboration, identify reskilling needs, and prepare employees and managers for new ways of working.

Above all, an AI agent’s output should not be taken at face value. Developers and sponsors might expect productivity gains, but real outcomes need independent validation, judged not just on output volume but on quality, accuracy, consistency, and reliability. Human bias colors these assessments, too. Employees might over- or understate an AI agent’s impact based on incentives, culture, or anxiety about change, especially when staffing decisions hinge on perceived gains.

Organizations should plan around demonstrated performance, not projections. HR can use data from HRIS platforms and AI agent inventories to understand the combined capacity of people and agents and to inform staffing, job design, reskilling, and workforce development decisions.

Pillar 4: Organizational design and governance

The introduction of AI agents raises questions that extend beyond technology governance. Historically, organizational structures focused on human relationships: reporting lines, spans of control, and workforce models that assumed employees performed work while technology supported them.

Future operating models will look different. Organizations will increasingly run teams of people supported by multiple AI agents. Those agents won’t appear on org charts, but they still need defined ownership, accountability, and oversight.

No single function can manage AI agent governance alone. An efficient, holistic approach includes several functions that might overlap. HR understands workforce design and change; identity and access management and governance manage accountability; cybersecurity handles monitoring and protection; AI governance addresses transparency and responsible use; risk management weighs operational and compliance exposure.

Managers and AI agent owners require enough AI literacy to set expectations, interpret outputs, recognize unreliable or risky results, and know when to intervene. HR should treat those capabilities as part of leadership development and change management, not solely as technical training.

Earlier waves of change – such as automation, technology modernization, and global expansion – offer lessons. People feel uncertain when operating models shift, so clear communication, change management, and engagement are essential to adoption. Governing AI agents will demand closer collaboration among HR, cybersecurity, identity governance, AI governance, and enterprise risk than most organizations have in place today.

Pillar 5: Compliance, risk, and ethical oversight

As AI agents interact with employee information, business processes, and enterprise systems, organizations will encounter new compliance, privacy, and governance obligations. Considerations that once applied primarily to employees increasingly apply to AI agents as well.

Such questions include:

• Who owns the activity being performed?
• Who approved the access?
• How are actions monitored?
• What controls govern acceptable behavior?
• How can the organization demonstrate ownership and accountability?
• How are decisions reviewed and audited? What happens when performance falls below acceptable levels? 

Many of these questions map directly onto identity governance: ownership, access approval, certification, segregation of duties, and life cycle management all offer practical controls for AI agents. However, HR governance matters just as much. Accountability, workforce oversight, and policy management remain the foundation.

Boards and executives should start asking similar questions they already ask about employees: How many AI agents do we have? Who owns them? What can they access? How is performance measured? What happens when something goes wrong? Human-in-the-loop accountability is central. Organizations can delegate tasks to AI agents, but they cannot delegate responsibility for outcomes.

The convergence of HRIS and identity and access management and governance

For years, HR and identity governance teams have managed different but connected sides of workforce governance. HR sets workforce policy, defines organizational relationships, establishes manager accountability, develops leaders, and leads workforce change. HRIS platforms provide the system of record that supports those decisions. Identity and access management and governance translate them into digital identities, access, ownership, approvals, and life cycle controls. When AI agents become more numerous and embedded in business processes, it is imperative to note how – and why – these two functions are converging.

As AI use expands, AI agents will continue to be assigned identities, receive access, perform work, influence outcomes, and require oversight. Organizations that treat agents only as technology assets will struggle to govern them sustainably. Organizations that integrate HR, identity governance, cybersecurity, and AI governance will be better positioned to deploy AI responsibly while maintaining accountability, trust, and workforce confidence.