Generative AI is moving from experiment to production across the Kingdom. As it does, the security questions shift from 'can we build this?' to 'can we trust it, and can we defend it?'
The most common risks fall into a few buckets: prompt injection and data exfiltration, leakage of sensitive information into model context, over-broad permissions granted to AI agents, and a lack of auditability around what the system actually did.
The good news is that established security principles still apply. Least privilege, input validation, output filtering, and thorough logging go a long way. What's new is applying them to nondeterministic systems, which means testing with adversarial prompts and monitoring behavior over time, not just at launch.
Adopt AI deliberately: start with well-scoped use cases, keep a human in the loop for high-impact actions, and treat private deployments as the default when handling regulated data.
