One of the most sophisticated developments in the recent AI explosion has been the growth of agentic AI technology, capable of autonomous action and decision-making. While the advanced capabilities of AI agents can offer convenience and efficiency, they also blur traditional identity and access boundaries by operating as an identity without being constrained the way human identities are.
Agentic AI systems carry new risks that most security strategies do not account for, representing significant additional dangers among already-alarming insider threats. A recent report from Exabeam and Sapio Research reveals that 64% of cybersecurity professionals now see insiders, including AI-driven ones, as the greatest threat.
Why Zero Trust Alone No Longer Suffices
The original Zero Trust framework is based on the principle of never trusting an identity, but verifying and authenticating authorization before granting access. This framework, fully or partially implemented by 63% of organizations worldwide, is designed to account for the security of human actors and endpoints. The use of static verification and perimeterless design has significant limitations in the context of AI autonomy: AI agents have the ability to carry out actions that bypass these measures, such as spawning sub-agents, often in a way that is difficult to audit using traditional logging and monitoring methods.
Of course, this does not mean that Zero Trust as a whole is ineffective; it merely means that the framework must be evolved to understand and work with autonomous AI functionality. The idea of “Hybrid Zero Trust” is the next step in bridging the gap between traditional security architecture and modern technological advances and threats.
Building the Next Wave of Zero Trust
Shifting Zero Trust goals to better align with emerging and evolving technology requires extending Zero Trust principles to non-human entities such as agents, models, and automation systems. “As AI agents start making autonomous decisions, we must evolve our controls to verify intent as much as identity,” according to Den Jones, Founder & CEO, 909Cyber. “The next frontier isn’t human Zero Trust—it’s AI-aware Zero Trust, where machine logic is held to the same accountability as human judgment.”
A modern, sophisticated Zero Trust framework must enforce context boundaries and trusted domain controls for AI decision-making. It should also integrate AI-specific code reviews, security audits, and behavioral monitoring.
When AI Becomes the Insider Threat
An autonomous AI agent can easily become compromised, carrying out unauthorized actions within sensitive systems that could have catastrophic consequences. This includes operational as well as security factors: agentic AI tools can do anything from deleting massive swaths of code to enabling hackers to leverage them for malicious ends through prompt injection. “According to our latest research, 75% of organizations experienced at least one AI-related breach in the past year, primarily due to oversharing sensitive employee or customer data,” says Dana Simberkoff, Chief Risk, Privacy, and Information Security Officer at AvePoint.
Potential response strategies for dealing with agentic AI compromise include implementing effective isolation protocols, rollback mechanisms, and explainability auditing. Machine-driven threats can operate as a parallel to human insider risk: the AI agent begins as a tool designed to help with many of the same actions that human users carry out, but develops over time into an instrument of harm through behavior drift.
From Reaction to Resilience
To properly defend against agentic AI threats, it is crucial for organizations to ensure continuous visibility and adaptive trust scoring to account for the risks of autonomous AI agents. “This begins with the application of Zero Trust principles to AI agents, recognizing them as emerging non-human identities within an enterprise,” says Anudeep Parhar, Chief Operating Officer at Entrust. “As agentic AI becomes increasingly embedded into the business, organizations must enforce strict context boundaries, trusted domain controls, and AI-specific security reviews as baseline defenses.”
The Exabeam report highlights the importance of behavioral analytics and contextual insight to identify risky AI agent behavior compared to its baseline. Organizations must implement detection and response frameworks that are capable of managing both human and AI entities, including refined and standardized detection and identification capabilities. As human and machine identities continue to blend and agentic AI grows more common and more sophisticated, security operations should also see a convergence of human and machine identity management.
Redefining Trust in a Hybrid Future
In the face of autonomous AI agents with more power and less oversight than ever before, it is essential for security measures to evolve to manage complex and sophisticated risks. Security is no longer about disbelief in humans, but about dynamic verification across all actors. The future of cyber resilience depends on recognizing AI as both a tool and a potential threat vector, and implementing advanced security frameworks to account for that fact.
