Enterprises Grapple with Unintended Consequences as Half of Organizations Report Unauthorized AI Agent Actions

Enthusiastic deployment meets lack of control

The scale of adoption is a major factor in the complexity of the problem. The survey found that a vast majority of organizations—86%—are currently utilizing at least one AI agent in their daily operations. The density of use is also high, with 60% of organizations running more than ten different agents across various departments.

This proliferation is further complicated by the way these agents interact. According to the report, 77% of respondents said their agents "chain" actions across other agents, tools, and APIs. These sequences are often dynamic, meaning the path an agent takes to complete a task is not designed in advance by a human team but is instead determined by the AI in real-time based on the instructions it receives. This creates a "black box" effect where the logic behind a specific outcome becomes increasingly difficult to parse.

Visibility remains the primary hurdle for security teams. Only 12.5% of those surveyed claimed they could consistently trace a complete agent path across all systems, from the initial human instruction through various tools and APIs to the final outcome. For the rest, the process of investigating an incident is an arduous manual task. More than 60% of professionals believe they would require hours, or even days, of manual forensic work to reconstruct exactly what a specific agent did during the previous day’s operations.

This lack of visibility is compounded by a lack of real-time control. When an agent begins to behave erratically or perform unauthorized tasks in a production environment, most organizations are powerless to stop it instantly. Only 17.5% of respondents reported having the capability to automatically halt an agent that begins behaving unexpectedly. For the vast majority, the only options are manual intervention—which is often too slow to prevent damage—or taking the entire application offline, resulting in significant downtime and business disruption.

The Security Implications of AI-Generated Code

The report also shed light on the role of AI in the software development lifecycle and its impact on application security. As developers increasingly rely on AI-generated or AI-assisted code, new vulnerabilities are being introduced into the enterprise ecosystem. More than 80% of respondents admitted that AI-assisted code contributes to at least some of their organization’s application security findings.

The problem lies not just in the creation of the code, but in how it is vetted. Only 7% of organizations reported resolving security issues within the coding assistant at the time of generation. Instead, the majority of vulnerabilities are only caught later during traditional code reviews, automated testing, or, in the worst-case scenarios, after the code has already been deployed to production. This "shift-right" of security issues increases the window of opportunity for attackers and adds to the technical debt of the security team.