This program equips professionals to manage and audit AI assets. Focus is on model governance, data lineage, and API security. Participants learn to ensure AI artifact integrity and compliance.
Audience: Asset managers, data stewards.
Learning Objectives:
- Establish AI asset inventory and lineage.
- Implement AI artifact governance.
- Manage change control and validation.
- Conduct API risk audits and access logging.
Program Modules:
- Model Inventory and Lineage:
- Model identification and documentation.
- Data source tracking.
- Version control implementation.
- Lineage visualization tools.
- Metadata management.
- Audit trail creation.
- Governance of AI Artifacts:
- Policy development.
- Compliance frameworks.
- Ethical considerations.
- Risk assessment strategies.
- Stakeholder communication.
- Governance tool usage.
- Change Control and Validation:
- Change request protocols.
- Validation testing methods.
- Deployment procedures.
- Performance monitoring.
- Rollback strategies.
- Change impact analysis.
- API Risk Audits and Access Logging:
- API security best practices.
- Access control mechanisms.
- Logging and monitoring.
- Vulnerability assessments.
- Data leakage prevention.
- Audit report generation.
- Data Stewardship and Quality:
- Data quality metrics.
- Data governance principles.
- Data lifecycle management.
- Data privacy regulations.
- Data cleansing techniques.
- Data access control.
- AI Asset Compliance and Reporting:
- Regulatory requirements.
- Audit preparation.
- Reporting standards.
- Documentation practices.
- Compliance monitoring.
- Remediation planning.
Exam Domains:
- AI Model Provenance.
- AI Governance Frameworks.
- AI Change Management.
- API Security Evaluation.
- Data Integrity Assurance.
- Regulatory Compliance in AI.
Course Delivery:
The course is delivered through lectures and interactive discussions. Participants access online resources, including readings and case studies.
Assessment and Certification:
Participants are assessed through quizzes and assignments. Upon successful completion, participants receive a certificate.
Question Types:
- Multiple Choice Questions (MCQs)
- True/False Statements
- Scenario-based Questions
- Fill in the Blank Questions
- Matching Questions (Matching concepts or terms with definitions)
- Short Answer Questions
Passing Criteria:
To pass the CAIAMA Certification Training exam, candidates must achieve a score of 70% or higher.
Enroll today and become a Certified AI Asset Management Assessor.