teiss – Artificial Intelligence – Visibility and the shadow AI crisis
teiss – Artificial Intelligence – Visibility and the shadow AI crisis
https://www.teiss.co.uk/artificial-intelligence/visibility-and-the-shadow-ai-crisis
Publish Date: 2026-07-03 19:03:00
Source Domain: www.teiss.co.uk
Enterprises are in the middle of a global AI gold rush. Development teams are scrambling to work Large Language Models (LLMs) and generative AI into their products and workflows at a breakneck pace. For many, that pressure is piling even greater pressure onto already strained security practices. IBM found that in 2025, one in five organisations reported a security incident tied to shadow AI. What’s more, those with high levels of shadow AI were found to have paid an average of $670,000 more per breach than those that kept it under control.
While that number is striking, the AI-Native Application Security report suggests it’s only the surface of a deeper problem. Shadow AI isn’t just a new risk category – it’s a symptom of organisations losing sight of where their software components live, how they behave, and who is responsible for securing them. Meanwhile, structurally siloed development and security functions and manual reporting and communication processes are allowing that gap to widen by the day. Without addressing these issues, organisations risk trading short-term innovation speed for a security posture they can no longer see or defend.
Shadow AI as the invisible attack surface
AI-native applications have moved from experiment to enterprise standard faster than most security teams can react. Today, 61% of new enterprise applications are being designed with AI components baked in from the start. However, as these applications flood the enterprise, teams are struggling to keep track of where AI technologies are used or the vulnerabilities they bring. This visibility gap is giving rise to shadow AI, with 62% of security practitioners admitting they have no way to tell where LLMs are deployed across their organisation.
Governance frameworks weren’t built for systems that learn, adapt, and evolve daily – they were built for static code and predictable systems. This mismatch is making shadow AI increasingly difficult to maintain….
