Google doesn't have AI security figured out.
Their own cloud security team admitted it: everyone is navigating AI security in real time — even them.
This matters because most builders assume the platform handles it. You deploy on @GoogleCloud, you use @Gemini, you trust that the security layer is covered. It's not.
Here's the reality:
Model safety is not the same as application safety. Google can make Gemini refuse harmful prompts. They can't stop your RAG pipeline from leaking data or your agent from executing unintended actions. That's your responsibility.
Three things every builder should do right now:
1. Log every agent action like you'd log a human admin. Same blast radius, same accountability. If your agent can read a database, you need an audit trail.
2. Review system prompts like database credentials. The prompt is the access control layer. If someone can manipulate it, they own your agent.
3. Assume every AI feature ships with a new social engineering entry point. The more helpful your AI feels, the more ways there are to exploit it.
The cloud provider secures the infrastructure. You secure the AI layer on top. That division of responsibility is not optional — it's the model.
What's the first security review you'd run on your AI features?
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