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Duration 14 hours
Course Outline
Introduction to Privacy in AI Deployments
- Privacy challenges inherent in AI systems
- Ollama’s role in privacy-focused environments
- Key compliance considerations, including GDPR and HIPAA
Secure Containerization and Deployment
- Hardening Docker and Kubernetes environments
- Network security and isolation methods
- Managing secrets and key rotation
On-Device and On-Prem Inference
- Privacy benefits of local inference
- Edge deployment strategies
- Striking a balance between performance and compliance
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
- Maintaining audit trails for compliance
- Implementing real-time monitoring and alerts
Access Control and Policy Enforcement
- Implementing role-based access control (RBAC)
- Enforcing policies using Open Policy Agent
- Applying data governance frameworks
Case Studies and Best Practices
- Deploying Ollama within regulated industries
- Balancing user experience with privacy needs
- Insights from real-world implementations
Summary and Next Steps
Requirements
- Knowledge of IT security principles
- Hands-on experience with containerization and deployment workflows
- Working familiarity with compliance frameworks like GDPR or HIPAA
Target Audience
- Security engineers
- IT architects
- Privacy officers
- Compliance teams
