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Duration 14 hours
Course Outline
Foundations of Privacy in AI Deployments
- Navigating privacy challenges within AI systems
- The role of Ollama in privacy-centric environments
- Key compliance considerations, including GDPR and HIPAA
Securing Containerization and Deployment
- Hardening Docker and Kubernetes infrastructures
- Techniques for network security and isolation
- Managing secrets and implementing key rotation
On-Device and On-Premises Inference
- Privacy benefits of local inference
- Patterns for edge deployment
- Striking a balance between performance and compliance
Differential Privacy and Data Protection
- Core principles of differential privacy
- Implementing noise mechanisms in AI workflows
- Strategies for data minimization and anonymization
Logging, Monitoring, and Audit Processes
- Best practices for secure logging
- Maintaining audit trails for compliance verification
- Real-time monitoring and alerting systems
Access Control and Policy Management
- Implementing Role-Based Access Control (RBAC)
- Enforcing policies using Open Policy Agent
- Applying data governance frameworks
Case Studies and Industry Best Practices
- Deploying Ollama in highly regulated sectors
- Harmonizing usability with privacy requirements
- Insights from real-world implementation experiences
Conclusion and Future Directions
Requirements
- Foundational knowledge of IT security principles
- Practical experience with containerization and deployment processes
- Working familiarity with compliance frameworks like GDPR or HIPAA
Target Audience
- Security engineers
- IT architects
- Privacy officers
- Compliance teams
