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Duration 14 hours
Course Outline
Introduction to Privacy in AI Deployments
- Privacy challenges inherent in AI systems
- The role of Ollama in privacy-centric environments
- An overview of compliance considerations (GDPR, HIPAA, etc.)
Secure Containerization and Deployment
- Hardening Docker and Kubernetes environments
- Network security and isolation techniques
- Management of secrets and key rotation
On-Device and On-Prem Inference
- The privacy benefits of local inference
- Edge deployment patterns
- Balancing performance with compliance needs
Differential Privacy and Data Protection
- Core principles of differential privacy
- Applying noise mechanisms within AI workflows
- Strategies for data minimization and anonymization
Logging, Monitoring, and Auditing
- Best practices for secure logging
- Creating audit trails for compliance
- Real-time monitoring and alerting systems
Access Control and Policy Enforcement
- Role-based access control (RBAC)
- Policy enforcement using Open Policy Agent
- Data governance frameworks
Case Studies and Best Practices
- Implementing Ollama in heavily regulated industries
- Striking a balance between usability and privacy
- Insights gained from real-world implementations
Summary and Next Steps
Requirements
- A solid grasp of IT security principles.
- Hands-on 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
