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Duration 14 hours
Course Outline
Foundations of Privacy in AI Deployments
- Privacy challenges inherent in AI systems.
- The role of Ollama in privacy-focused environments.
- Overview of key compliance considerations (GDPR, HIPAA, etc.).
Secure Containerization and Deployment
- Hardening Docker and Kubernetes environments.
- Techniques for network security and isolation.
- Managing secrets and key rotation.
On-Device and On-Premises Inference
- Privacy benefits of local inference.
- Edge deployment strategies.
- Balancing performance with compliance needs.
Differential Privacy and Data Protection
- Core principles of differential privacy.
- Implementing noise mechanisms in 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 in highly regulated industries.
- Balancing usability with privacy.
- Key lessons from real-world implementations.
Summary and Future Steps
Requirements
- A solid grasp of IT security principles.
- Practical experience with containerization and deployment processes.
- Working knowledge of compliance frameworks such as GDPR or HIPAA.
Target Audience
- Security engineers.
- IT architects.
- Privacy officers.
- Compliance teams.
