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ISACA Advanced in AI Security Management (AAISM) Certification
Course 2019
2 DAY COURSE

Price:
Course Outline

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.

ISACA Advanced in AI Security Management (AAISM) Certification Benefits

  • 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

Learning Objectives

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
Course Dates

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Private Team Training

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