How to Detect and Eliminate Shadow AI in 5 Steps

The rapid integration of AI technologies in development processes often leads to the emergence of Shadow AI, which encompasses unmanaged AI assets that exist outside established governance frameworks, posing significant security and compliance risks. Shadow AI includes external API calls, open-source models, and custom models, each with its own vulnerabilities such as data leakage, malicious injection, and license violations. To manage these risks, organizations can adopt a structured approach using tools like the JFrog AI Catalog, which offers a comprehensive solution for detecting, auditing, and governing AI assets. The steps involve scanning repositories to identify Shadow AI, prioritizing and assessing risks, enforcing compliance policies, and creating a trusted environment for AI development. This methodology not only mitigates the risks associated with unmanaged AI but also transforms the chaotic integration of AI into a streamlined, secure, and compliant process, thereby facilitating innovation without compromising security.