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Duration 14 hours
Course Outline
Introduction to Privacy in AI Deployments
- Addressing privacy challenges in AI systems
- Ollama’s contribution to privacy-focused environments
- Key compliance considerations (GDPR, HIPAA, etc.)
Secure Containerization and Deployment
- Hardening Docker and Kubernetes environments
- Network security and isolation strategies
- Effective secrets management and key rotation
On-Device and On-Premises Inference
- Privacy benefits of local inference
- Edge deployment architectural patterns
- Balancing performance optimization with compliance adherence
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
- Establishing audit trails for compliance verification
- Real-time monitoring and alerting mechanisms
Access Control and Policy Enforcement
- Role-based access control (RBAC) implementation
- Policy enforcement using Open Policy Agent
- Data governance framework design
Case Studies and Best Practices
- Deploying Ollama in highly regulated industries
- Striking a balance between usability and privacy
- Insights from real-world implementation experiences
Summary and Next Steps
Requirements
- A solid understanding of IT security principles
- Practical experience with containerization and deployment workflows
- Familiarity with compliance frameworks such as GDPR or HIPAA
Target Audience
- Security engineers
- IT architects
- Privacy officers
- Compliance teams
