I recently coauthored this article examining the new security exposures created by the rapid adoption of generative AI across enterprises. As organizations embed GenAI into everyday workflows, the risks are shifting beyond traditional threats to how AI interacts with data, code, and business processes. From insecure AI-generated code and software supply chain issues to increased data leakage through prompts and outputs, these exposures are often introduced unintentionally as teams move quickly to adopt the technology.
The article highlights that blocking AI is not a viable solution. Instead, organizations must focus on visibility, governance, and control. This includes understanding what data is being accessed, how AI tools are being used, and where sensitive information may be exposed. Ultimately, secure GenAI adoption requires a shift toward context-driven data security, where organizations monitor and manage both inputs and outputs in real time to reduce risk while still enabling innovation.