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Duration 14 hours
Course Outline
Privacy Fundamentals in AI Deployments
- Addressing privacy challenges in AI systems
- Ollama's function within privacy-conscious environments
- Key compliance considerations (GDPR, HIPAA, etc.)
Secure Containerization and Deployment Strategies
- Strengthening Docker and Kubernetes environments
- Techniques for network security and isolation
- Managing secrets and rotating keys
On-Device and On-Premises Inference
- Benefits of local inference for data privacy
- Patterns for edge deployment
- Striking a balance between performance and compliance
Differential Privacy and Data Safeguarding
- Core principles of differential privacy
- Integrating noise mechanisms into AI workflows
- Strategies for data minimization and anonymization
Logging, Monitoring, and Audit Management
- Best practices for secure logging
- Maintaining audit trails for compliance
- Real-time monitoring and alerting systems
Access Control and Policy Implementation
- Role-based access control (RBAC)
- Enforcing policies using Open Policy Agent
- Data governance frameworks
Case Studies and Industry Best Practices
- Deploying Ollama in highly regulated sectors
- Balancing user experience with privacy needs
- Key takeaways from real-world implementations
Conclusion and Path Forward
Requirements
- A solid grasp of IT security principles
- Practical experience with containerization and deployment processes
- Familiarity with compliance frameworks such as GDPR or HIPAA
Target Audience
- Security engineers
- IT architects
- Privacy officers
- Compliance teams
