Assess access, sensitive content, classification, retention, and data governance before enabling AI assistants across the enterprise.
Why AI readiness matters
AI assistants can retrieve information from systems and repositories they are authorized to access. Excessive sharing, stale permissions, and inconsistent classification can increase the chance of exposing information in AI-generated responses. Access behavior depends on the tool and integrations deployed.
Review permissions
Prioritize document repositories, collaboration workspaces, business applications, guest access, shared links, inactive locations, and unclear group ownership. Start with repositories containing legal, personnel, financial, security, or regulated information.
Discover sensitive data
Use discovery, classification, and data security posture management tools to identify sensitive records, unlabeled content, broad access, and repositories that combine high-value data with risky permissions.
Establish labeling
Create a usable information classification scheme. Define label meanings and handling requirements, then test whether selected tools can consistently apply and enforce the rules across files, collaboration channels, and external sharing.
Fix retention and stale data
Review retention schedules, duplicate content, inactive workspaces, and records obligations. Removing information that no longer needs to be kept can reduce unnecessary AI exposure.
Strengthen DLP and auditing
Evaluate data loss prevention controls and logs for the actual applications and AI integrations in use. Tune alerts, track exceptions, and define who will investigate potential exposure.
Pilot safely
Run a controlled pilot with business, security, privacy, compliance, legal, records, and IT stakeholders. Test permitted and prohibited requests before expanding use.
Apply This Guidance
Need help turning these practices into an actionable plan?
Karatin can help assess current-state controls, identify gaps, and create a prioritized remediation roadmap.
Contact Karatin