Shadow AI: the next frontier of unseen risk (www.techradar.com)

Employees across industries are quietly adopting unsanctioned AI tools—a trend called "Shadow AI"—and organizations largely lack visibility or governance over where, how, and why these models are used. Driven by convenience and blurred lines between personal and professional use, Shadow AI mirrors early Shadow IT but with far higher stakes: public models can log or retain fed data (the DeepSeek breach is a recent example), expose IP to foreign servers, violate GDPR/HIPAA, and train future models on leaked corporate inputs. Emerging practices like vibe coding (directly deploying code without review) and agentic AI (autonomous agents granted broad data/system access) expand the attack surface, create hidden backdoors, and let inaccurate or biased outputs propagate unchecked through business workflows. Mitigating Shadow AI requires first gaining visibility—map AI usage, update policies, and run enterprise-wide training—then offer sanctioned, secure models that meet developer and business needs. Technical controls should be integrated into security architecture: privileged access management for LLMs, CASB, DLP, and proxy filtering to detect or block unsanctioned calls, plus secure hosting for sensitive development and review processes. The choice isn't whether to allow AI but how to manage it: organizations that combine policy, education, sanctioned tooling, and technical controls can enable safe innovation; those that ignore Shadow AI risk data loss, regulatory fines, and systemic operational failures.