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Duration 14 hours
Course Outline
Foundations of Privacy in AI Deployments
- Privacy challenges within AI systems
- The role of Ollama in privacy-focused environments
- Key compliance considerations (GDPR, HIPAA, etc.)
Secure Containerization and Deployment
- Hardening Docker and Kubernetes environments
- Network security and isolation strategies
- Secrets management and key rotation
On-Device and On-Prem Inference
- Benefits of local inference for data privacy
- Edge deployment patterns
- Optimizing performance while maintaining compliance
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
- Establishing audit trails for compliance
- Real-time monitoring and alerting mechanisms
Access Control and Policy Enforcement
- Role-based access control (RBAC)
- Policy enforcement using Open Policy Agent
- Data governance frameworks
Case Studies and Industry Best Practices
- Implementing Ollama in regulated sectors
- Striking a balance between usability and privacy
- Insights from real-world implementation experiences
Conclusion and Future Steps
Requirements
- Familiarity with IT security fundamentals
- Practical experience with containerization and deployment processes
- Knowledge of compliance frameworks such as GDPR or HIPAA
Target Audience
- Security engineers
- IT architects
- Privacy officers
- Compliance teams
