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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-focused environments
- Overview of compliance requirements (GDPR, HIPAA, etc.)
Secure Containerization and Deployment
- Hardening Docker and Kubernetes environments
- Network security and isolation methods
- Secrets management and key rotation strategies
On-Device and On-Prem Inference
- Privacy advantages of local inference
- Edge deployment architectures
- Striking a balance between performance and compliance
Differential Privacy and Data Protection
- Fundamentals 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
- Role-based access control (RBAC)
- Policy enforcement using Open Policy Agent
- Data governance frameworks
Case Studies and Best Practices
- Ollama deployment in regulated sectors
- Reconciling usability with privacy
- Insights from real-world implementations
Summary and Next Steps
Requirements
- Core knowledge of IT security principles
- Practical experience with containerization and deployment processes
- Knowledge of compliance frameworks like GDPR or HIPAA
Target Audience
- Security engineers
- IT architects
- Privacy officers
- Compliance teams
