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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-sensitive environments
- Key compliance considerations, including GDPR and HIPAA
Secure Containerization and Deployment Strategies
- Hardening Docker and Kubernetes environments
- Techniques for network security and isolation
- Managing secrets and rotating keys effectively
On-Device and On-Premises Inference
- The privacy benefits of local inference
- Edge deployment patterns and best practices
- 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
- Creating audit trails for compliance verification
- Implementing real-time monitoring and alerting systems
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 Industry Best Practices
- Deploying Ollama within regulated industries
- Reconciling usability with strict privacy requirements
- Insights gained from real-world implementations
Conclusion and Future Directions
Requirements
- A solid grasp of fundamental IT security principles
- Practical experience with containerization and deployment workflows
- Knowledge of compliance frameworks such as GDPR or HIPAA
Target Audience
- Security Engineers
- IT Architects
- Privacy Officers
- Compliance Teams
