AI Governance and Program Management

How the AI security program is governed and run: stakeholders, frameworks and regulation, AI security strategy and policies, AI asset and data life cycle management, program development and metrics, and business continuity and incident response for AI.

5 minutes 5 Questions

AI Governance and Program Management, within Advanced AI Security Management, refers to the structured framework an organization establishes to direct, control, and continuously improve how artificial intelligence systems are developed, deployed, secured, and monitored throughout their lifecycle. I…

Concepts covered
Data Protection Law and AI ProcessingBoard and Executive Reporting on AI SecurityDisaster Recovery for Models, Data and PipelinesRisk-Tiered AI Regulation and Prohibited UsesShadow AI Discovery and Policy EnforcementAI Security Program Roadmap, Resources and BudgetAI Incident Response Playbooks and EscalationSector and Contractual Requirements for AI SecurityAdding AI Requirements to Existing Security PoliciesTraining Data Provenance and Lineage RecordsInvestigating and Documenting AI Security IncidentsAI Asset Inventory and Model RegistryClassification of AI Systems and Their DataBusiness Continuity Planning for AI-Dependent ProcessesThird-Party Assurance Reporting for AI SecurityISO/IEC 42001 AI Management SystemsBuilding the AI Security ProgramUsing AI to Improve Security OperationsNIST AI Risk Management Framework for Security ManagersIP and Licensing of Models and DatasetsAI Data Life Cycle From Collection to DisposalDetecting and Triaging AI Security IncidentsAI Security Incident Definition and ClassificationOWASP Top 10 for LLM Applications and MITRE ATLASAI Acceptable Use Policies and Generative AI GuidelinesPolicy Exceptions and Risk Acceptance for AI UseRoles and Responsibilities in AI Security PoliciesAI Governance Charter, Committee and RACIAI Security Strategy Aligned to Business ObjectivesAI Asset Decommissioning and Model RetirementRetention and Secure Disposal of AI Data and ModelsManaging Embedded AI Features in Enterprise SoftwareSelecting and Implementing AI Security ToolsResponsible AI Principles and Ethical Use MandatesAI Security Metrics, KPIs and KRIsAI Security Stakeholders and Their ExpectationsData Ownership, Stewardship and Custodianship for AIPost-Incident Review and Lessons Learned for AI SystemsReporting AI Security Program PerformancePolicy Review Cycles for a Fast-Moving AI LandscapeAI Standards, Procedures and Guidelines HierarchyContainment, Eradication and Recovery for AI IncidentsRegulatory and Contractual Incident Reporting for AIAI Security Program Maturity AssessmentAI Security in the SDLC and MLOps PipelinesAI Security Awareness Training ProgramsAI-Specific Security Policy Development
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AAISM - AI Governance and Program Management Example Questions

Test your knowledge of AI Governance and Program Management

Question 1

An enterprise licenses a third-party provider's foundation model weights and runs the model on its own infrastructure. During vendor review, the provider supplies a Type 2 trust services report with an unmodified opinion and no exceptions. The report covers the provider's hosted inference platform. What is the MOST important conclusion for the information security manager?

Question 2

An information security manager reviews a third-party LLM provider's Type 2 report. The service auditor noted an exception: privileged access to the model weights repository was not reviewed quarterly during the period. In a section titled 'other information,' the provider states that the issue was remediated after the period ended. What should the manager do NEXT?

Question 3

In a Type 2 trust services report on a third-party AI provider's service, what is the PRIMARY purpose of the management's written assertion section?

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