What You Will Learn
- How to decide what data can and cannot go into public AI systems.
- How to think about anonymization, private deployment, hostable models, and deployment trade-offs.
- How to design meaningful review, access control, logging, and retention for higher-risk AI workflows.
- How to evaluate vendors, monitor production systems, and respond to risk after rollout.
Lessons in This Section
| Lesson | Focus |
|---|---|
| When Not to Send Data to a Public LLM | How to decide when a workflow should avoid public LLM endpoints based on data sensitivity, contractual exposure, and safer design alternatives. |
| Anonymize Customer Data with AI | How to redact, mask, or pseudonymize customer data safely, and where automated anonymization can fail in practice. |
| Deploy Your Own Private LLM | How to compare managed private inference, self-hosting, and hybrid architectures based on cost, latency, ops burden, and governance needs. |
| Open-Source LLMs You Can Host | How to choose a hostable open-weight model based on task fit, hardware limits, governance needs, and support burden rather than hype. |
| How to Design Human Review for AI Systems | How to build a risk-tiered human review model so oversight is meaningful, efficient, and matched to business impact. |
| AI Access Control, Logging, and Retention Policies | How to design access rules, logging depth, and retention policies so AI systems remain auditable and proportional to risk. |
| AI Vendor Risk Assessment and Procurement Checklist | How to evaluate AI vendors using a practical checklist for data handling, governance, contract risk, and operational fit. |
| AI Evaluation, Monitoring, and Incident Response for Production Systems | How to monitor production AI systems, define rollback triggers, and respond when quality, safety, or governance fail after launch. |
Suggested Learning Path
Data Exposure and Use Boundaries
Hosting and Deployment Choices
Review, Governance, and Operational Control
- How to Design Human Review for AI Systems
- AI Access Control, Logging, and Retention Policies
- AI Vendor Risk Assessment and Procurement Checklist
- AI Evaluation, Monitoring, and Incident Response for Production Systems
How This Module Fits Together
This module is built around a practical governance idea: AI risk is rarely just about the model. It is usually about the full system around the model — what data enters it, where the model runs, who can use it, what gets logged, when humans review outputs, and how the organization responds when something goes wrong.
The lessons therefore move from data exposure and transformation, to deployment choices, and finally to review, governance, and operational control. That progression helps teams move from vague privacy concerns toward concrete design decisions that make AI systems more governable in real business settings.
Where to Go Next
- Continue with Build Your Own AI Tools
- Return to the Academy home