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Advanced in AI Security Management (AAISM)

245934068464

Course Overview Print

Enterprises are deploying AI at speed, increasing exposure across data, models and third-party ecosystems. This course equips participants to structure governance, quantify risk and apply effective controls so AI delivers value without compromising security. They will work through modules on programme governance, risk management, and technical controls, using scenarios, practical exercises and management reporting to translate policy into operational results, guided by experienced trainers.

Learning Outcomes

  • Analyse AI risks across governance, data, models and operations.
  • Design and document AI governance structures, roles and programme plans.
  • Implement strategies, policies, procedures and lifecycle controls for AI systems.
  • Evaluate technical, privacy, ethical and trust-and-safety controls for effectiveness.
  • Manage incidents, continuity and disaster recovery for AI use cases.
  • Assess vendor and supply-chain risks and define accountability models.

Key Topics

  • AI governance and programme management, stakeholder roles and frameworks.
  • AI risk assessment and treatment, thresholds, KPIs/KRIs and documentation.
  • Vendor and supply-chain management, contracts, SLAs and monitoring.
  • AI security architecture and lifecycle testing, validation and TEVV.
  • Data management controls, privacy, ethics, transparency and human-in-the-loop.
  • Security controls and continuous monitoring; certification preparation for ISACA Advanced in AI Security Management (AAISM) exam.

Exam Details

This course is designed to build participants’ understanding of key concepts and domains covered in the ISACA Advanced in AI Security Management (AAISM) certification.

To maximise success, participants are strongly encouraged to complement the course with additional self-study, revision of course materials, and dedicated practice before attempting the exam.

Course Outline

1) AI Governance and Programme Management

A. Stakeholder considerations, industry frameworks, and regulatory requirements

  • Organisational 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

B. AI-related strategies, policies, and procedures

  • AI strategy (consumer vs enterprise; buy vs build)

  • Policies: responsible use, acceptable use

  • Procedures: implementation, manuals

  • Ethics

C. AI asset and data lifecycle management

  • AI asset and data inventory (inventory management, model cards)

  • Data handling, classification, discovery

  • Data augmentation and cleaning; storage; protection; destruction

D. AI security programme development and management

  • Documented programme plan

  • Security team roles, responsibilities, proficiencies

  • Alignment to existing information security

  • Use of AI-enabled security tools

  • Metrics and management (KRIs, KPIs)

  • Management reporting

E. Business continuity and incident response

  • Detection, notification, classification (criticality/severity)

  • Resiliency; Business Continuity Plan

  • Compliance “red-button” requirements

  • AI-specific incident response playbooks

  • Break-glass / go-no-go; authority

  • RTO/RPO from an AI perspective

  • Disaster recovery; testing

     

2) AI Risk Management
A. AI risk assessment, thresholds, and treatment

  • Impact and conformity assessments; PIAs; documentation

  • Acceptable risk levels; treatment plans

  • KRIs and KPIs for AI use

B. Threats, testing, and assurance

  • Penetration testing; vulnerability testing; red teaming

  • AI-related vulnerabilities; adversarial threats

  • Threat intelligence; AI-enabled attack chains; anomalies; landscape

  • Deepfakes; insider threat; AI agents

C. AI vendor and supply-chain management

  • Software/library dependencies

  • Vendor due diligence and contracts; SLAs; usage

  • Accountability models (provider vs deployer)

  • Third/fourth/fifth parties; ownership/IP; access controls; liability

  • Vendor monitoring for risk/changes

 

3) AI Technologies and Controls
A. Security architecture and design

  • Change management; SDL; secure-by-design

  • Securing infrastructure as code

  • Data flows; approved base models

  • Interconnectivity with architecture

B. AI lifecycle (model selection, training, validation)

  • Testing model interconnectivity; linkages between models

  • Regression/progression; model testing; TEVV

  • Model accuracy testing and evaluation

C. Data management controls

  • Data collection and control

  • Data poisoning; bias; accuracy

  • Data position/possession requirements

D. Privacy, ethical, trust & safety controls

  • Explainability; privacy controls (e.g., right to be forgotten, data subject rights)

  • Consent; transparency; decision-making; fairness; ethics

  • Automated decision-making; human-in-the-loop

  • Trust & safety (content moderation); potential harm; environmental impacts

  • Data minimisation and anonymisation

E. Security controls and monitoring

  • Security-monitoring metrics; selecting/implementing controls

  • CSA/self-assessment; control lifecycle; continuous monitoring

  • KPIs/KRIs for controls and monitoring; technical controls; threat-controls mapping

  • Security awareness training

*Important Note : Fees are subject to Singapore's prevailing Goods and Services Tax (GST).
Advanced in AI Security Management (AAISM)
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