ISACA AAISM Questions PDF File

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ISACA AAISM Exam Syllabus Topics:

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

Section

Weight

Objectives

Topic 1: AI Risk Management

31%

- AI Risk Assessment

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

- AI Threats and Vulnerabilities

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

Topic 2: AI Technologies and Controls

38%

- AI Security Architecture

1. Secure AI system design
2. Model lifecycle security

- Data and Model Controls

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

- AI Assurance and Safety

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

Topic 3: AI Governance and Program Management

31%

- Frameworks and Compliance

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

- AI Strategy and Policy

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

- AI Governance Foundations

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

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AAISM Exam Questions & AAISM Pdf Training & AAISM Latest Vce

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q60-Q65):

NEW QUESTION # 60
Which of the following BEST helps to address concerns related to data sources for AI tools and how they arrive at their decisions and conclusions?

A. Sanitizing training data
B. Designating an AI lead
C. Mandating audits and traceability
D. Implementing Zero Trust and monitoring

Answer: C

Explanation:
Mandating audits and traceability provides visibility into AI data sources, decision processes, and model behavior. This enables organizations to verify how conclusions are reached, support accountability, and address transparency and governance concerns related to AI operations.

NEW QUESTION # 61
AI developers often find deep learning systems difficult to explain PRIMARILY because:

A. Neural network architectures include statistical methods not fully understood
B. Training data is spread across public domains
C. Algorithms rely on probability theories
D. Knowledge dynamically changes without logs

Answer: A

Explanation:
AAISM notes that deep learning systems lack transparency due to complex neural architectures, where internal representations are statistical, nonlinear, and not directly interpretable.

NEW QUESTION # 62
How can an organization BEST protect itself from payment diversions caused by deepfake attacks impersonating management?

A. Require mandatory deepfake detection training for all employees
B. Mandate that payments be sent only once per week
C. Issue a security policy on deepfakes
D. Implement resilient payment approval processes

Answer: D

Explanation:
AAISM's risk management framework stresses that the most effective defense against deepfake- enabled fraud, such as payment diversion, is resilient payment approval processes. This includes multi-step verification, segregation of duties, and independent confirmations for high-value transactions. Employee training, policies, or limiting payment frequency may reduce exposure, but they cannot guarantee prevention. Only process-based controls enforce structural safeguards that prevent fraudulent instructions from being executed, even if a deepfake impersonation attempt is successful.

NEW QUESTION # 63
Which of the following is BEST to review in order to detect issues related to data used throughout an AI system's life cycle?

A. Data quality requirements
B. Data flow diagrams
C. User access logs
D. Data retention policies

Answer: B

Explanation:
Data flow diagrams provide a comprehensive view of how data moves through the system across all stages of the AI life cycle, enabling identification of issues related to data handling, transformation, and usage.

NEW QUESTION # 64
During red-team testing of an AI system used for lending decisions, which technique BEST simulates a data poisoning attack?

A. Adding noise to output predictions
B. Corrupting training datasets to manipulate outcomes
C. Inputting encrypted data
D. Stealing model weights

Answer: B

Explanation:
AAISM defines data poisoning as intentional manipulation of the training data to influence model behavior or outputs. Corrupting training data (D) is the exact definition of this attack type.

NEW QUESTION # 65
......

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