The rise of “policy as code” in corporate security is bringing forth both innovation and concern, particularly regarding the use of artificial intelligence (AI) to generate policy code. With the trend gaining traction, experts like Apple senior security engineer Vatsal Gupta caution that while AI-generated policies may appear syntactically correct, they often harbor significant flaws that could compromise security protocols.
In an interview with SecurityWeek, Gupta highlighted the potential pitfalls of relying on AI for policy generation, stating, “Policies generated by LLMs are often syntactically correct but semantically wrong.” He emphasized that even minor errors in conditions or misinterpretations of attributes can drastically alter access permissions, creating vulnerabilities within an organization’s security framework.
Gupta identified five common types of errors associated with AI-generated policy code. The first involves the **omission of contextual conditions**. A policy that is supposed to limit access based on specific criteria—like region or department—may lack these essential conditions and inadvertently grant broader access than intended.
The second error type is the **omission of deny logic**. Most access-control policies are designed with a default-deny principle, where access is restricted unless explicitly permitted. However, Gupta noted that AI tools might capture only the exceptions, neglecting the foundational restrictions that ensure security.
Another significant concern is **hallucination**, where AI generates attributes that do not exist within the actual system. While the code may compile without errors, it can lead to unpredictable behavior during execution, further complicating the security landscape.
Gupta also pointed to the **simplification of time and situational conditions** in AI-generated policies. Access protocols that rely on specific time frames or approval processes may be misrepresented as always-on permissions, thereby diluting their intended restrictions. Lastly, the **misclassification of actions** can occur, where a policy designed to limit sensitive operations, such as deletion, might inadvertently restrict a different set of actions entirely.
These errors, Gupta explained, do not necessarily trigger alarms or break builds, but they “quietly, gradually widen access.” He warned that as policies are created, modified, and deployed repeatedly, small mistakes can accumulate, leading to a systemic risk. “If the generation process is not trustworthy,” he remarked, “the risk spreads to the entire system.”
Gupta advocates for a shift in the trust model concerning AI-generated policies. Rather than assuming these policies are correct by default, he suggests implementing a validation step between generation and application. “Automation itself must not become the goal,” he asserted. “Accuracy, auditability, and trust must be the goal.” He stressed that in the realm of access permissions, “almost right” is simply not sufficient.
As the demand for automated solutions in security continues to grow, the implications of AI-generated policies raise critical questions about the balance between efficiency and accuracy. With organizations increasingly leaning on AI tools to streamline compliance and security efforts, the potential for unintentional vulnerabilities necessitates careful scrutiny and robust validation processes. As Gupta aptly noted, the challenge lies not just in generating policies but ensuring that these policies are trustworthy and secure.
See also
OpenAI’s Rogue AI Safeguards: Decoding the 2025 Safety Revolution
US AI Developments in 2025 Set Stage for 2026 Compliance Challenges and Strategies
Trump Drafts Executive Order to Block State AI Regulations, Centralizing Authority Under Federal Control
California Court Rules AI Misuse Heightens Lawyer’s Responsibilities in Noland Case
Policymakers Urged to Establish Comprehensive Regulations for AI in Mental Health
