Privacy & Deployment
The real deployment question is rarely ‘Can we use AI?’ It is usually ‘Which data can go where, under which controls, and with what human review?’
What You Will Learn
- How to decide what data can and cannot go into public AI systems.
- How to think about anonymization, private deployments, and hostable models.
- How to design meaningful human review around higher-risk AI workflows.
Lessons in This Section
| Lesson | Focus |
|---|---|
| Anonymize Customer Data with AI | How to use AI to detect and redact sensitive customer information while understanding the limits of automated anonymization. |
| Deploy Your Own Private LLM | What a private LLM deployment means in practice, when it makes sense, and how to evaluate the operational trade-offs beyond simple privacy slogans. |
| Open-Source LLMs You Can Host | A practical overview of hostable open-weight models and how to think about choosing one for real business tasks. |
| When Not to Send Data to a Public LLM | A plain-English guide to deciding which business data should not be sent to public LLM endpoints and what safer alternatives exist. |
| How to Design Human Review for AI Systems | How to build human review into AI workflows so oversight is meaningful, efficient, and matched to business risk rather than added as decoration. |
Suggested Learning Path
- When Not to Send Data to a Public LLM
- Anonymize Customer Data with AI
- Deploy Your Own Private LLM
- Open-Source LLMs You Can Host
- How to Design Human Review for AI Systems
Where to Go Next
- Continue with Build Your Own AI Tools
- Return to the Academy home