Identity security has become a cornerstone of modern cybersecurity strategy. It focuses on reducing risks tied to digital identities—whether they belong to people, applications, or devices. The goal is to identify, govern, and protect every identity across an organization’s digital ecosystem. But as technology evolves, so does identity complexity. What began as protecting human users has expanded to include machines, and now, the next wave of challenge is emerging: securing AI agents.
The Expanding Scope of Identity Security
Traditionally, identity security was about managing human access—making sure employees, partners, and contractors had the right permissions to perform their work. Over the past decade, however, a new category of identities has exploded: machine identities. These include service accounts, digital certificates, bots, and workloads that interact across networks and applications.
According to industry estimates, machine identities now outnumber human identities by more than 80 to 1, creating an enormous challenge for IT and security teams. Managing, monitoring, and protecting these digital entities has become critical to preventing misuse, breaches, or unauthorized access.
Today, we are witnessing another evolution. The rise of agentic AI systems—AI models capable of making autonomous decisions—has created a hybrid identity that blends traits of both humans and machines. These AI agents can perceive their environment, process complex data, make decisions, and even learn over time. While they promise significant efficiency gains, they also introduce new and unprecedented security risks.
AI Agents: A New Class of Identities
By definition, AI agents are machines. Yet, their capacity to reason, adapt, and act independently gives them qualities once reserved for human users. This makes them a new identity class—one that blurs the line between human and machine identity management.
Unlike traditional software, AI agents can operate with minimal supervision. They can initiate actions, modify processes, and even interact with other systems autonomously. These capabilities, while revolutionary, raise important security questions:
- How much access should an AI agent have?
- Who—or what—authorizes its actions?
- How can organizations prevent an AI agent from being manipulated or exploited?
These questions highlight the growing need for a new approach to AI identity governance—one that balances innovation with rigorous security.
Challenges in Managing AI Identities
The rise of AI-driven automation introduces challenges similar to those faced with machine identities but on a much greater scale. Gartner predicts that by 2028, one-third of enterprise software applications will integrate agentic AI, up from less than 1% in 2024. That surge means organizations must be prepared to onboard, manage, and retire thousands of new digital identities—each with potential access to sensitive data and systems.
Access management is one of the most pressing concerns. AI agents often require broad permissions to perform their tasks efficiently. However, granting too much access increases the attack surface. If compromised, an AI agent could unintentionally—or maliciously—cause significant harm, from data exfiltration to operational disruptions.
AI agents also lack moral and contextual understanding. While they can detect anomalies or unusual patterns, they do not inherently comprehend ethics, compliance, or “right versus wrong.” This makes them susceptible to subtle manipulation, biased learning, or malicious redirection.
Another growing threat is shadow AI—when employees deploy AI tools or agents without notifying IT. These unsanctioned agents may lack security controls, proper authentication, or data governance, exposing organizations to hidden risks. Without full visibility into who or what is accessing enterprise systems, security teams remain blind to potential breaches.
Why Traditional Identity Security Isn’t Enough
Conventional identity security systems were designed for predictable, rule-based identities—human users logging in, or machines exchanging credentials. AI agents, however, behave dynamically. They can learn, adapt, and even alter their operational behavior based on new data inputs.
This dynamic nature requires a shift in how organizations think about identity. Instead of static permissions and fixed roles, enterprises must adopt context-aware, adaptive identity controls that evolve alongside the AI agent’s learning process.
Moreover, AI agents can proliferate rapidly. As organizations automate workflows or deploy self-learning models, the number of agentic identities can multiply overnight. Without proper lifecycle management—covering creation, privilege assignment, monitoring, and deprovisioning—enterprises risk losing control of their digital environment.
Building a Secure AI Identity Strategy
Securing AI identities requires extending existing identity governance principles while adapting them to the unique characteristics of AI systems. A comprehensive AI identity security framework should include:
Full Visibility and Inventory
Establish complete visibility into all human, machine, and AI identities within your environment. Maintain a centralized inventory that tracks who (or what) has access to what resources.
Strong Authentication and Authorization
Implement multi-layered authentication for AI agents, just as you do for human users. Use digital certificates, cryptographic keys, and dynamic access tokens that verify legitimacy before granting access.
Least-Privilege and Just-in-Time Access
Enforce least-privilege principles—grant AI agents only the access they need for specific tasks and revoke it immediately after completion. Just-in-time access ensures privileges aren’t left open unnecessarily.
Continuous Monitoring and Behavioral Analytics
Deploy AI-powered security analytics to monitor agent behavior in real-time. Detect anomalies such as unusual data access patterns, privilege escalations, or unauthorized interactions.
Secure Communication Protocols
Adopt standards like the Model Context Protocol (MCP) for agent communication but remember: it isn’t secure by default. Apply encryption, authentication, and policy enforcement layers to safeguard agent exchanges.
Governance and Compliance Frameworks
Establish clear policies for AI use within the organization. Require every deployed agent to undergo security assessment, approval, and onboarding through official IT channels.
The Role of Leadership and Culture
Technology alone cannot secure AI identities. Organizations must foster a security-first culture that prioritizes governance, transparency, and accountability. Leaders should engage in strategic discussions about AI adoption, understanding not just its potential but also its risks.
Before integrating AI agents into workflows, businesses must ask:
- Does our identity management system scale to support AI identities?
- Can we monitor and revoke AI access as effectively as human or machine access?
- Are our teams trained to handle AI-related security incidents?
Answering these questions early ensures that organizations remain proactive, not reactive, as AI adoption accelerates.
The Future of Identity Security
As AI becomes embedded in daily operations, the concept of identity security will continue to evolve. The next generation of identity management solutions will likely merge AI, automation, and zero-trust architecture to secure every entity—human, machine, and intelligent agent—within a unified framework.
CyberArk, a global leader in identity security, exemplifies this approach. Its AI-powered Identity Security Platform integrates intelligent privilege controls with continuous threat detection, prevention, and response across the entire identity lifecycle. This kind of intelligent automation will be essential for securing the growing web of AI-driven systems.
Frequently Asked Questions:
What is Agentic AI?
Agentic AI refers to artificial intelligence systems capable of acting autonomously, making decisions, and learning from experience with minimal human input. These AI agents can perform tasks, analyze data, and adapt behavior over time, enhancing efficiency but also introducing new security challenges.
Why is Agentic AI considered a “dual-edged” innovation?
Agentic AI offers tremendous business benefits—automation, faster decision-making, and cost savings—but it also carries significant risks. Because these agents operate independently, they can be exploited if not properly secured, leading to data breaches or unauthorized access.
How does Agentic AI affect identity security?
Agentic AI blurs the line between human and machine identities. Each AI agent becomes a new identity that requires management, authentication, and monitoring. Without proper governance, these identities can multiply rapidly, creating hidden vulnerabilities across an organization.
What are the main risks of unmanaged AI identities?
Unsecured or shadow AI agents can expose organizations to unauthorized access, data leaks, privilege abuse, and compliance violations. If an AI identity is compromised, it can act autonomously—causing harm before humans can intervene.
How can organizations protect AI identities?
Businesses should extend existing identity and access management (IAM) frameworks to include AI agents. Key measures include enforcing least-privilege access, using strong authentication, continuous monitoring, and implementing secure communication protocols for AI interactions.
What is the role of least-privilege access in AI security?
Least-privilege access ensures AI agents only receive permissions necessary to perform specific tasks. This limits potential damage if an AI identity is compromised and helps maintain tighter control over sensitive systems and data.
How can shadow AI be prevented in the workplace?
Organizations must create clear AI usage policies, require IT approval for new agents, and educate employees on the dangers of unauthorized AI tools. Continuous visibility and automated identity governance can help detect unapproved AI deployments.
Conclusion
Agentic AI represents both a remarkable advancement and a serious responsibility for modern enterprises. Its ability to think, learn, and act autonomously offers unprecedented efficiency, insight, and innovation—but it also creates new layers of identity risk that cannot be ignored. As these intelligent agents become part of daily business operations, organizations must evolve their identity security strategies to keep pace with this transformation.
