7 Critical AI Security Mistakes
Common security gaps organizations overlook while adopting generative AI and LLMs.
AI adoption often moves faster than the security model around it. Teams connect models to private data, internal tools, and customer-facing workflows before they have a complete view of the risk surface.
Common mistakes include testing only direct prompts, ignoring retrieval content, logging sensitive data without controls, assuming model providers solve application-layer risk, and treating safety evaluation as a one-time launch gate.
The fix is an operating model: evaluate realistic attacks, define runtime policy, monitor production behavior, and review failures as the system evolves.