AI Security: The Hidden Cost of Unreadiness

Many AI companies are losing deals not because of their products, but due to poor security. Security unreadiness is costing these companies revenue and slowing down enterprise adoption. When an AI product goes through an enterprise security review, it faces a vendor security questionnaire with 50 to over 300 questions. These cover data architecture, model transparency, access controls, subprocessor risk, incident response, and certifications. For AI startups built on foundation models, the underlying model's data handling practices become part of their security story. AI products face unique security challenges compared to traditional software. They are non-deterministic, meaning models don't always produce the same output for the same input, making auditing difficult. Also, most AI companies rely on third-party foundation models. This means their security posture is tied to these external dependencies. Ultimately, robust security is now a critical requirement for any business building, buying, or investing in AI systems.