Why it exists
AI tools are being adopted faster than security policy can follow. The result is unclear data flows, ungoverned model access, unreviewed vendor terms, and no process for deciding what AI use is acceptable.
The risk path
Sensitive data enters AI tools without visibility. Vendor retention policies are rarely reviewed. DLP controls often do not extend to browser-based AI tools. Output influences decisions without human review.
Signals I look for
No formal AI tool approval process · Users entering customer data into consumer AI tools · Vendor data retention not reviewed · No logging of AI tool usage · AI output acting on data without human sign-off
Engineering decision
Start with data movement, not model behavior. Understand what enters each tool, where it goes, and what the vendor can do with it. Build approval tiers based on data sensitivity.
Implementation path
All AI tools are in the approved inventory · Vendor data handling reviewed · DLP coverage verified for high-sensitivity data paths · AI tool request process operational
What good looks like
An AI tool environment where every tool is approved, data exposure is bounded, vendor terms are reviewed, and output handling includes human checkpoints.
Related
/notes#prompt-injection /notes#ai-tool-request /notes#ai-data