Data Privacy & AI G overnance Ch ec k list
Before you unleash GenAI on your experimentation workflow, run through this checklist to ensure privacy, security, and
compliance are in check:
Align With Your Organization's AI Policy: Verify that using AI in this manner adheres to your company’s AI usage
guidelines. Many large firms have policies on which AI tools are approved, data handling requirements, and review
processes. Get approval if needed and choose tools (public or internal) that are sanctioned for enterprise use.
Protect Sensitive Data: Never input confidential or personally identifiable information (PII) (customer names, emails, etc.)
into a public AI tool. Anonymize or aggregate experiment data before sharing with an external model. For example, use
relative metrics (“Variant B had +5% lift”) instead of raw user counts. If you must use customer data to personalize with AI,
opt for a self-hosted or private model where data stays within your controlled environment.
Use Privacy-Friendly Settings: If using public LLM services, enable features that prevent data retention. For instance,
OpenAI allows users to turn off chat history (so prompts aren’t used to train the model) – use such features. Ensure any
vendor contracts include data privacy clauses (no storing or re-using your data). Data privacy missteps can derail AI
initiatives – 21% of failed enterprise AI projects cite data privacy issues as a cause, so lock this down upfront.
E ns u re I n f or m ation Sec u rity: Treat AI like any other software from a security perspective. Avoid sharing code or URLs that
could expose system vulnerabilities unless you’re using a trusted internal tool. If you have an internal generative model,
keep it behind your firewall and enforce access controls. Monitor AI usage for any unusual activity – for example, if an AI
integration is making external requests, ensure they’re expected and secure.
GenAI Quick-Win Playbook for Experimentation
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