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ISACA AAISM Exam Syllabus Topics:

Section	Weight	Objectives
AI Risk Management	31%	- AI Risk Assessment 1. Risk identification and analysis 2. Risk thresholds and treatment - AI Threats and Vulnerabilities 1. Adversarial AI and ML attacks 2. Data poisoning and model manipulation 3. Supply chain and vendor risks
AI Technologies and Controls	38%	- AI Assurance and Safety 1. Bias mitigation 2. Ethical AI controls 3. Model validation and testing - AI Security Architecture 1. Secure AI system design 2. Model lifecycle security - Data and Model Controls 1. Privacy-preserving techniques 2. Monitoring and runtime controls 3. Data protection and governance
AI Governance and Program Management	31%	- AI Strategy and Policy 1. Policies and procedures 2. AI strategy development 3. Responsible and acceptable use - AI Governance Foundations 1. Roles and responsibilities 2. Organizational structure and governance 3. Stakeholder considerations - Frameworks and Compliance 1. Privacy and ethics considerations 2. Industry frameworks and standards (e.g., NIST AI RMF, ISO/IEC 42001) 3. Regulatory requirements

Section

Weight

Objectives

AI Risk Management

31%

- AI Risk Assessment

1. Risk identification and analysis
2. Risk thresholds and treatment

- AI Threats and Vulnerabilities

1. Adversarial AI and ML attacks
2. Data poisoning and model manipulation
3. Supply chain and vendor risks

AI Technologies and Controls

38%

- AI Assurance and Safety

1. Bias mitigation
2. Ethical AI controls
3. Model validation and testing

- AI Security Architecture

1. Secure AI system design
2. Model lifecycle security

- Data and Model Controls

1. Privacy-preserving techniques
2. Monitoring and runtime controls
3. Data protection and governance

AI Governance and Program Management

31%

- AI Strategy and Policy

1. Policies and procedures
2. AI strategy development
3. Responsible and acceptable use

- AI Governance Foundations

1. Roles and responsibilities
2. Organizational structure and governance
3. Stakeholder considerations

- Frameworks and Compliance

1. Privacy and ethics considerations
2. Industry frameworks and standards (e.g., NIST AI RMF, ISO/IEC 42001)
3. Regulatory requirements

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ISACA Advanced in AI Security Management (AAISM) Exam AAISM Prüfungsfragen mit Lösungen (Q206-Q211):

206. Frage
Which of the following AI data life cycle phases presents the GREATEST inherent risk?

A. Maintenance
B. Training
C. Preparation
D. Monitoring

Antwort: B

Begründung:
AAISM identifies training as the phase with the highest inherent risk because this is where:
- data poisoning can occur
- sensitive data may be exposed
- bias can be introduced
- model inversion risks originate
- security and privacy vulnerabilities are embedded

207. Frage
Which of the following would MOST effectively obtain ongoing support from stakeholders to align AI initiatives with business objectives?

A. Conducting periodic organization-wide AI staff training
B. Addressing and optimizing AI-related risk
C. Developing and monitoring the AI strategic roadmap
D. Quantifying and communicating the value of AI solutions

Antwort: D

Begründung:
Sustained stakeholder sponsorship hinges on demonstrated, quantified business value communicated in terms they own (KPIs, ROI, cost-to-serve, risk-adjusted outcomes). AAISM frames stakeholder alignment as a value-assurance loop: define value hypotheses, measure realized value, and continuously communicate results to sponsors. While an AI roadmap (C), risk optimization (B), and training (A) are important, they support rather than drive ongoing executive buy-in. Quantified value narratives secure resources and reinforce alignment to strategic goals.
References:* AI Security Management (AAISM) Body of Knowledge: Strategy & Value Realization- value metrics, benefits tracking, stakeholder reporting* AAISM Study Guide: Business alignment for AI- OKRs/KPIs, ROI cases, benefits realization management

208. Frage
Which of the following would BEST protect trade secrets related to AI technologies during their life cycle?

A. Introducing watermarks when generating AI output
B. Patenting AI algorithms along with data sets
C. Restricting access to sensitive data
D. Enforcing trademark rights in AI systems

Antwort: C

Begründung:
Restricting access to sensitive data and artifacts (e.g., training data, feature stores, model weights, prompts, system designs) using least-privilege, segregation, encryption, and monitoring is the most effective way to protect trade secrets throughout the AI lifecycle. Patents require public disclosure, trademarks protect branding (not secrets), and output watermarks help provenance/abuse deterrence but do not secure underlying proprietary know-how.
References: AI Security Management™ (AAISM) Body of Knowledge: Information Protection for AI- Access Control, Segmentation, and Secrets Management; AAISM Study Guide: Lifecycle Security of AI Artifacts and Trade-Secret Safeguards.

209. Frage
Which phase of the AI data life cycle presents the GREATEST inherent risk?

A. Maintenance
B. Training
C. Preparation
D. Monitoring

Antwort: B

Begründung:
AAISM identifies training as the phase with the highest inherent risk because this is where:
* data poisoning can occur
* sensitive data may be exposed
* bias can be introduced
* model inversion risks originate
* security and privacy vulnerabilities are embedded
Preparation (C) carries risk but is less critical. Maintenance (B) and monitoring (A) involve operational safeguards, not foundational risk creation.
References: AAISM Study Guide - AI Data Life Cycle Risks; High-Risk Model Training Phase.

210. Frage
Which of the following is the MOST significant risk during the inference phase of the AI model life cycle?

A. Undetected erroneous or biased decisions
B. Lack of an audit trail for model version control
C. Increased computational costs for retraining
D. Inability to measure precision and recall

Antwort: A

Begründung:
During the inference phase, the AI model is actively making decisions or predictions that affect real-world outcomes. Undetected erroneous or biased decisions present the most significant risk because they can directly impact users, operations, compliance, and organizational trust.

211. Frage
......

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