Is Your Cloud Environment Actually Ready for AI?
Every vendor demo makes AI rollout look like flipping a switch. In practice, the switch is connected to a lot of wiring most companies have not looked at in years – permissions structures, data classification, network segmentation, identity governance.
Before any AI tool touches production data, here is what actually needs to be true.
Your permissions model has to be accurate, not aspirational
AI tools are extraordinarily good at finding and surfacing whatever they have access to. Audit access before you automate discovery.
Data classification cannot be a spreadsheet nobody updates
Knowing what is sensitive has to be enforced at the system level, not documented in a policy PDF.
AI does not create new risks so much as it finds every old one at once.
Network segmentation determines your blast radius
If an AI integration gets compromised or misconfigured, segmentation determines whether that is a contained incident or a company-wide one.
Identity governance needs to include machine identities
AI tools authenticate as service accounts and API keys, not humans. Those identities need the same lifecycle discipline as employee accounts.
Logging has to be good enough to answer what did it access
When someone asks what a given AI integration touched over the last 90 days, you need an actual answer, not a shrug.
Someone needs to own AI governance specifically
Not as an addendum to an existing security policy – as its own owned responsibility, with a name attached.
The bottom line
None of this is a reason to avoid AI. It is a reason to do the unglamorous infrastructure work first.