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Duration 14 hours
Course Outline
Introduction to Privacy in AI Deployments
- Privacy challenges inherent in AI systems
- Ollama's function within privacy-focused environments
- Overview of key compliance considerations (GDPR, HIPAA, etc.)
Secure Containerization and Deployment
- Enhancing the security of Docker and Kubernetes environments
- Techniques for network security and isolation
- Managing secrets and executing key rotation
On-Device and On-Prem Inference
- Privacy benefits of local inference
- Patterns for edge deployment
- Balancing performance requirements with compliance obligations
Differential Privacy and Data Protection
- Core principles of differential privacy
- Integrating noise mechanisms into AI workflows
- Strategies for data minimization and anonymization
Logging, Monitoring, and Auditing
- Best practices for secure logging
- Maintaining audit trails for compliance verification
- Implementing real-time monitoring and alert systems
Access Control and Policy Enforcement
- Implementing Role-based access control (RBAC)
- Enforcing policies using Open Policy Agent
- Adopting data governance frameworks
Case Studies and Best Practices
- Deploying Ollama in highly regulated industries
- Striking a balance between usability and privacy
- Key takeaways from real-world implementations
Summary and Next Steps
Requirements
- A solid grasp of IT security fundamentals
- Hands-on experience with containerization and deployment workflows
- Working knowledge of compliance frameworks such as GDPR or HIPAA
Target Audience
- Security engineers
- IT architects
- Privacy officers
- Compliance teams
