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Course Outline
Introduction to Responsible AI with Mistral
- Principles of Responsible AI
- Mistral’s enterprise features and development roadmap
- Compliance drivers and global regulatory frameworks
Privacy and Data Protection
- Techniques for anonymization and pseudonymization of data
- Encryption methods for data at rest and in transit
- Strategies for managing data access to minimize risk
Data Residency Strategies
- Options for regional hosting
- Comparisons between on-premises and cloud deployments
- Hybrid models for data residency
Enterprise Controls and Integrations
- Implementation of role-based access control (RBAC)
- Single sign-on (SSO) and identity management solutions
- Integration with existing enterprise IT systems for government
Auditability and Governance
- Establishing audit logs and monitoring mechanisms
- Governance playbooks tailored for AI systems
- Incident response and escalation procedures
Vendor Options and Deployment Models
- Comparison of Mistral’s self-hosting and managed services options
- Evaluation criteria for vendor compliance assurances
- Considerations for cost, performance, and regulatory trade-offs
Case Studies and Future Outlook
- Examples from highly regulated industries
- Trends in emerging regulations and compliance standards
- Preparation strategies for evolving enterprise AI standards
Summary and Next Steps
Requirements
- An understanding of enterprise IT systems for government
- Experience with data governance or compliance frameworks
- Familiarity with security and privacy regulations
Audience
- Compliance leads
- Security architects
- Legal and operations stakeholders
14 Hours
