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Duration 14 hours
Course Outline
Introduction to Privacy in AI Deployments
- Addressing 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 setups
- Techniques for network security and isolation
- Managing secrets and rotating keys
On-Device and On-Prem 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
- 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
- Setting up real-time monitoring and alerts
Access Control and Policy Enforcement
- Implementing Role-Based Access Control (RBAC)
- Enforcing policies using the Open Policy Agent
- Establishing data governance frameworks
Case Studies and Best Practices
- Implementing Ollama in regulated sectors
- Balancing user experience with privacy
- Insights from real-world implementation experiences
Summary and Next Steps
Requirements
- A solid grasp of IT security principles
- Practical experience with containerization and deployment processes
- Familiarity with compliance frameworks like GDPR or HIPAA
Target Audience
- Security engineers
- IT architects
- Privacy officers
- Compliance teams
