AI systems change the security conversation. They consume sensitive data, influence decisions, and create new surfaces — prompts, models, pipelines — that traditional controls were never designed to cover. Bolting security on afterward is how organizations end up with innovation they cannot trust.
Know what your AI touches
Every intelligent workflow has a data diet. Document what goes in, where it is stored, who can see the outputs, and what happens when the model is wrong. If you cannot answer those four questions, you are not ready to scale.
Treat prompts and models as assets
Prompt injection, data leakage, and model misuse are real risks with real business impact. Apply the same discipline you would to any production system: access control, logging, testing, and a clear owner.
Resilience beats perfection
You will not prevent every issue. Design for detection and recovery: human review where stakes are high, graceful fallbacks when models misbehave, and honest communication when something goes wrong. Trust is built in how you respond, not in the promise that nothing will ever fail.