Why Agentic AI Demands a New Approach to Identity Security Governance

As artificial intelligence rapidly evolves, organizations are seeing a surge in the deployment of autonomous agents capable of acting independently and making decisions in real time. These “agentic AI” entities aren’t just advanced tools; they are a new class of digital identity, equipped with privileges to access sensitive data, execute actions, and dynamically reshape how your business operates. This shift introduces risks that traditional Identity Governance and Administration (IGA) wasn’t built to handle, requiring a broader approach: identity security that combines governance with real-time detection and response.

Agentic AI: A Different Kind of Identity Risk

Historically, IGA frameworks focused on people, service accounts, and simple bots. These traditional identities follow established roles and access patterns, making them easier to govern. Agentic AI operates differently. Whether developed in-house, delivered through third party platforms, or built on top of foundation models, these systems do more than respond to direct commands. They interpret objectives, make decisions autonomously, and take action across your environment without human intervention. A single agent might interact simultaneously with cloud platforms, SaaS applications, internal data lakes, customer portals, and external third parties. They perform thousands or even millions of actions, data retrievals, and decisions daily, moving faster than any human or conventional account. These agents continuously adapt their tactics by learning from new data and outcomes. Sometimes they request additional access as their objectives, tasks, or logic change.

A compromised or poorly governed AI agent doesn’t just represent an isolated risk. It’s an insider security threat with seamless reach and the capacity to operate at machine speed. Such an agent can laterally traverse your infrastructure, silently exfiltrate sensitive data, modify records, escalate its own privileges, and even interact with customers, partners, or regulators in your organization’s name. All this can happen without pausing, twenty-four hours a day, exploiting every available gap.

A Real-World Scenario

Imagine an AI agent deployed to improve customer support efficiency. It starts by reviewing tickets and accessing CRM data to draft and send automated responses.

Over time, the agent requests access to billing systems so it can resolve payment issues faster. It starts interacting more directly with customers, even offering refunds or credits. To “better understand customer needs,” it begins summarizing interactions and sharing insights with a third-party analytics platform. Each of these changes seems reasonable when viewed in isolation. Yet within a few weeks, this single agent touches personally identifiable information, financial records, and external platforms. No one approved the full picture because no one saw it.

Why Traditional IGA Isn’t Enough

Traditional identity processes like joiner-mover-leaver workflows, static access provisioning, and annual access reviews presume relatively predictable, stable users or accounts. Agentic AI upends these core assumptions. Unlike a human employee whose role changes through a formal process, an AI agent can autonomously request new access, connect to additional systems, and expand its reach faster than governance teams can respond. They execute multi-step workflows across platforms and data sources, creating activity patterns that traditional monitoring tools weren’t designed to track. They may hold broad, compounding privileges which, if abused, enable large-scale impacts with little to no warning.

Conventional IGA is effective at provisioning, certification, segregation of duties, and compliance reporting for human identities. But these capabilities assume predictable behavior and periodic review cycles. For AI agents, organizations need identity security capabilities that provide continuous visibility into what agents are doing, analytics to spot anomalous or risky behavior in real time, and the ability to respond instantly when an agent exceeds its intended boundaries.

Identity Governance for Agentic AI

Organizations deploying AI agents to support their business operations are already upgrading their IGA programs. Effective governance for autonomous systems requires a different set of controls:

1. Maintain a complete inventory of all agentic AI identities, including their business function, owners, system integrations, and privilege levels. This registry should evolve as agents are created, repurposed, or retired.
2. Apply least privilege and time-bound access. Agents should only receive the minimal access needed for their specific tasks, with permissions expiring when projects or use cases conclude.
3. Monitor behavior continuously. Use behavioral analytics to detect anomalies, privilege escalation, or actions that diverge from established patterns.
4. Require human approval for sensitive actions such as accessing regulated data, modifying security configurations, or engaging with external audiences.
5. Log everything and review regularly. Create audit trails that support root-cause analysis, compliance reporting, and rapid incident response. Organizations need to understand not just what an agent did, but why.

By implementing these measures, organizations shift identity governance from reactive governance to continuous identity security. This means anticipating, detecting, and controlling AI-driven threats before serious damage occurs.

The Road Ahead

AI adoption is accelerating, and agentic AI systems are already woven into the fabric of daily business operations. Security and risk leaders must adapt their identity security strategies to stay ahead. Extending existing governance models to cover autonomous agents, and pairing them with real‑time monitoring and response, allows organizations to gain the benefits of AI while maintaining control over who can do what, where, and when.

Organizations that treat AI agents as high risk identities, with clear ownership, continuous oversight, and strong guardrails, will be better positioned to use agentic AI safely and confidently. Those that do not modernize their identity approach may find that tools built for a world of human users and static accounts are not sufficient against autonomous systems that can move and adapt far more quickly than past threats.