2-Day Instructor-Led Training
After-Course Instructor-Coaching Included
#2019
ISACA Advanced in AI Security Management (AAISM) Certification
Course 2019
- Duration: 2 days
- Language: English
- Level: Intermediate
ISACA Advanced in AI Security Management (AAISM) validates security management professionals’ ability to demonstrate their expertise in AI. This credential builds upon existing security best practices to enhance expertise and adapt to the evolving AI-driven landscape, ensuring robust protection and a strategic edge.
AI Security Management Certification
In-Person
Online
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AI Security Management Certification Course Information
In this course you will learn skills which:
- Establishes AI-Specific Security Expertise
- Bridges the Gap Between AI and Cybersecurity
- Aligns with Enterprise Governance and Risk Needs
- Built on ISACA’s Trusted Frameworks
Prerequisites
Must possess a CISM or CISSP to be eligible for Certification.
AI Security Management Certification Course Outline
Domain 1: AI Governance and Program Management
Stakeholder Considerations, Industry Frameworks, and Regulatory Requirements
- Organizational Structure and Overall Governance
- Roles and Responsibilities
- Charter and Steering Committee
- Identifying Stakeholders
- Risk Appetite and Tolerance
- Frameworks, Standards, and Regulations
- Selecting appropriate Frameworks
- Business and Use Cases for AI
- Privacy Considerations
AI-related Strategies, Policies, and Procedures
- AI Strategy
- Consumer v. Enterprise
- Buy vs. Build
- AI Policies
- Responsible Use
- Acceptable Use
- AI Procedures
- Implementation
- Manuals
- Ethics
AI Asset and Data Life Cycle Management
- AI Asset and Data Inventory
- Inventory management
- Model cards
- Data handling, classification, discovery
- Data Augmentation and Cleaning
- Data Storage
- Data Protection
- Destruction
AI Security Program Development and Management
- Documented Program Plan
- Security team, roles, responsibilities, and proficiencies
- Alignment to existing info sec
- Use of AI-enabled security tools in the program
- Metrics and management
- KRIs and KPIs for AI use with regard to the security
- Management reporting
Business Continuity and Incident Response
- Incident detection
- Notification
- Incident classification
- Criticality and severity
- Resiliency
- Business Continuity Plan
- Red-button requirements for compliance
- Incident response playbooks specifically for AI
- Break glass policies/ go no go • Authority
- RTO RPO – AI perspective
- Disaster recovery
- Testing
Domain 2. AI Risk Management
AI Risk Assessment, Thresholds, and Treatment
- Impact assessment
- Conformity assessment
- PIAs
- Risk documentation
- Acceptable levels of risk
- Treatment plans
- KRIs and KPIs for AI us
AI-related Strategies, Policies, and Procedures
- PEN test
- Vulnerability tests
- Red teaming
- AI related vulnerabilities
- Adversarial threats
- Threat intelligence
- AI-enabled threats/Attack chains
- Anomalies
- Threat landscape
- Deep fakes
- Insider threat
- AI agents
AI Vendor and Supply Chain Management
- Dependencies of software packages and libraries
- Vendor due diligence and contracts
- SLAs
- Vendor usage
- Accountability models
- Provider vs. deployer
- Third, fourth, and fifth parties
- Ownership and intellectual property
- Access controls
- Liability
- Vendor monitoring for risk and changes
Module 3. AI Technologies and Controls
AI Security Architecture and Design
- Change management
- SDL
- Secure by design
- Securing infrastructure as code
- Data flows
- Approved base models
- Interconnectivity and interaction with architecture
AI Life Cycle (e.g., model selection, training, and validation)
- Testing models interconnectivity
- Linkages between models
- Regression
- Model testing
- Progression
- TEVV
- Model accuracy testing and evaluation
Data Management Controls
- Data collection
- Data control
- Data Poisoning
- BIAS
- Accuracy
- Data position requirements
Privacy, Ethical, Trust and Safety Controls
- Explainability
- Privacy controls – like right to be forgotten, data subject rights
- Consent
- Transparency
- Decision making
- Fairness
- Ethics
- Automated decision making
- Human in the loop
- Trust and safety - content moderation
- Potential harm
- Environmental impacts
- Data minimization and anonymization
Security Controls and Monitoring
- Security monitoring metrics
- Selecting the right controls
- Implementing controls
- Self-assessment of controls (CSA)
- Control life cycle
- Continuous monitoring
- KPIs and KRIs for security controls and monitoring
- Technical controls
- Threat controls mapping
- Security awareness training
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AI Security Management Certification
- Exam Format: The exam is computer-based and administered as a closed-book, remotely proctored assessment.
- Number of Questions: The exam comprises 90 multiple-choice questions.
- Duration: Candidates are allotted 2 hours to complete the exam.
- Passing Score: A score of 65% or higher is required to pass the exam.