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Duration 14 hours
Course Outline
Introduction to Privacy in AI Deployments
- Addressing privacy challenges in AI systems
- The role of Ollama in privacy-conscious environments
- Overview of compliance considerations (GDPR, HIPAA, etc.)
Secure Containerization and Deployment
- Hardening Docker and Kubernetes environments
- Network security and isolation strategies
- Secrets management and key rotation
On-Device and On-Prem Inference
- Privacy benefits of local inference
- Edge deployment patterns
- Balancing performance with compliance requirements
Differential Privacy and Data Protection
- Core principles of differential privacy
- Integrating noise mechanisms into AI workflows
- Data minimization and anonymization approaches
Logging, Monitoring, and Auditing
- Best practices for secure logging
- Maintaining audit trails for compliance
- Real-time monitoring and alerting systems
Access Control and Policy Enforcement
- Implementing role-based access control (RBAC)
- Enforcing policies using Open Policy Agent
- Establishing data governance frameworks
Case Studies and Best Practices
- Deploying Ollama in highly regulated sectors
- Striking a balance between usability and privacy
- Insights from real-world implementations
Summary and Next Steps
Requirements
- Foundational knowledge of IT security principles
- Hands-on experience with containerization and deployment workflows
- Familiarity with compliance frameworks such as GDPR or HIPAA
Audience
- Security engineers
- IT architects
- Privacy officers
- Compliance teams
