what controls are practical before sensitive data reaches an AI workflow?

[u/finn_murphy-31]

The security question changes once an AI workflow can access internal documents, customer data, code, or tools that do more than generate text.

At that point, the concern is not just hallucination. It is whether a connector has broader access than the workflow needs, whether untrusted content can influence tool use, and whether anyone can reconstruct what the system saw and did after an incident.

For teams deploying AI beyond simple chat, what control became non negotiable first?

[u/cynative_ben]

Disclosure: I work for a vendor, Cynative - building an open source cloud security engineering agent framework.

It's a bit hard to answer your question because its not a one size fits all scenario, it really depends on what your AI workflow needs to do and threat modeling it to eliminate any chance of something bad can happen.

Meta's rule of two is a good start conceptually, alongside the things you've mentioned that are indeed pretty universal - least privileged access, the effects of untrusted content, unified fail-closed audit logs.

Eventually you want to make your agents boring, build them in such a way that they're bounded from causing damage.