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Duration 14 hours
Course Outline
Introduction to Privacy in AI Deployments
- Privacy challenges inherent in AI systems
- Ollama’s contribution to privacy-conscious environments
- Key compliance considerations, including GDPR and HIPAA
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
- Privacy benefits of local inference
- Edge deployment patterns
- Balancing performance needs with compliance obligations
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
- Maintaining 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
- Deploying Ollama within 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
- Practical experience with containerization and deployment processes
- Knowledge of compliance frameworks such as GDPR or HIPAA
Target Audience
- Security engineers
- IT architects
- Privacy officers
- Compliance teams
