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Duration 14 hours
Course Outline
Privacy Foundations 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 ecosystems
- Network security and isolation strategies
- Management of secrets and key rotation
On-Device and On-Premises Inference
- Privacy benefits of local inference
- Edge deployment architectures
- Striking a balance between performance and compliance
Differential Privacy and Data Safeguards
- Core principles of differential privacy
- Incorporating noise mechanisms into AI workflows
- Data minimization and anonymization methods
Logging, Monitoring, and Audit Trails
- Best practices for secure logging
- Maintaining audit trails for compliance
- Real-time monitoring and alerting systems
Access Control and Policy Management
- Role-based access control (RBAC)
- Policy enforcement via Open Policy Agent
- Data governance frameworks
Case Studies and Industry Best Practices
- Deploying Ollama within regulated sectors
- Harmonizing usability with privacy requirements
- Insights from real-world implementations
Wrap-up and Future Directions
Requirements
- A solid grasp of IT security fundamentals
- Practical experience with containerization and deployment workflows
- Knowledge of compliance frameworks like GDPR or HIPAA
Target Audience
- Security engineers
- IT architects
- Privacy officers
- Compliance teams
