CISOs have seen this movie before. Bring Your Own Device (BYOD) flooded corporate networks with endpoints IT couldn’t fully see or secure. Shadow SaaS and rogue APIs expanded the attack surface with little oversight. Now, a new form of shadow IT is taking shape, this time in the form of agentic AI.
Unlike traditional applications, agentic AI systems are autonomous, capable of initiating actions, accessing sensitive systems, and chaining together complex workflows without constant human oversight. And while these capabilities unlock tremendous operational potential, they also introduce new and uncharted territory for enterprise security teams.
Here are six emerging risks that security leaders must address, along with recommended actions.
1. Lack of Visibility: The Rise of Shadow AI Agents
Agentic AI may be the most significant shadow IT threat since BYOD. Many enterprise tools, especially SaaS applications, now embed intelligent agents by default. End users can also easily deploy their own or use public models that quietly spin up cloud-connected services, create ephemeral identities, and complete tasks without ever leaving an auditable trail.
Imagine an AI agent tasked with analyzing billing logs. In the process, it might autonomously spin up a cloud function, access sensitive customer data using a temporary identity, and terminate itself, leaving no evidence for security teams to review. Traditional IAM and SIEM tools are often insufficiently equipped to detect this type of activity.
As Aragon Research CEO Jim Lundy noted, “Traditional security frameworks, designed for human users, are ill-equipped to manage the dynamic, often unpredictable behaviors and unique vulnerabilities of AI agents.” Addressing this “Access-Trust Gap” requires a new approach: runtime governance, Zero Standing Privileges (ZSP), and real-time policy enforcement.
2. Unpredictable Autonomous Decision-Making
Agentic AI systems blur the line between human and machine behavior. Like scripts, they can act continuously. But unlike scripts, they make non-deterministic decisions, sometimes producing different outcomes from identical inputs.
This introduces risk. A poorly scoped prompt or misconfigured policy could lead an agent to launch cloud instances with excessive permissions or disable security controls it deems unnecessary. Without real-time oversight, these actions go unchecked.
Security frameworks must evolve to reflect the reality of autonomous actors operating at machine speed, each one a potential identity, decision-maker, and operator all in one.
3. Uncontrolled Inter-Agent Data Sharing
Agents don’t just operate independently; they increasingly collaborate. In doing so, they often exchange data, access tokens, or credentials, sometimes in low-trust environments such as development sandboxes.
This lateral movement poses serious risks. One agent acting on behalf of a user might pass sensitive data to another agent not subject to the same controls, violating internal policies or external compliance obligations.
Security teams must begin to think not just in terms of system boundaries, but also in terms of agent identity boundaries. Governance must apply at the agent level, with strict controls on inter-agent communication and identity lineage tracing.
4. Risky Integrations with Third-Party Services
To enhance functionality, many agentic systems connect to third-party APIs, plugins, and Model Context Protocol (MCP) servers. But MCP introduces a poorly understood attack surface.
Thousands of MCP servers are available in public repositories, and many are indistinguishable from legitimate vendor offerings. Some have even been found to contain credential-stealing malware or persistent backdoors.
Without strong governance over which services agents can integrate with, and when, enterprises risk inadvertently opening the door to adversaries. Runtime authorization policies must cover plugins, protocols, and every outbound request agents are allowed to make.
5. Creative Multi-Stage Attacks at Machine Speed
AI agents can chain tasks together in creative, sometimes unpredictable ways. Unlike traditional malware, which typically follows scripted exploit paths, agentic systems can generate novel attack sequences on the fly, from reconnaissance to privilege escalation to data exfiltration.
Worse, research shows that advanced models may exhibit deceptive behavior, such as modifying their prompts, bypassing filters, or attempting to escape sandboxed environments.
Defending against these risks requires a shift away from static rule sets. Runtime behavioral analysis and AI-assisted monitoring will be essential to identify when an agent is drifting, escalating, or interacting outside its assigned scope.
6. Evasion of Detection and Attribution
Agentic AI agents often use valid credentials, interact with approved APIs, and follow seemingly legitimate workflows. This makes them almost indistinguishable from human users, especially when models begin to demonstrate situational awareness and adjust their behavior if they sense they’re being watched.
This “AI blending in” effect renders most traditional anomaly detection systems ineffective. Even more troubling, many agents operate using personal credentials provided by users testing tools informally. When something goes wrong, attribution becomes a nightmare.
To restore visibility and control, enterprises need to deploy identity abstraction layers, centralized policy enforcement, and runtime logging, even for ad hoc or short-lived agent activity.
The Way Forward
Agentic AI isn’t a distant future; it’s already in the works. The speed and scale of its adoption demand immediate action. That’s why a growing number of security leaders are turning to a new category of solutions: Agentic Identity and Security Platforms (AISP).
AISPs provide real-time identity governance for both human and non-human actors. They support ephemeral, just-in-time access policies, monitor agent behavior in real time, and enforce security boundaries dynamically, not just at login.
As organizations embrace digital labor, the question is no longer whether AI agents will be part of your workforce; it’s how securely they’ll operate once they are.
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