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ISACA AAISM Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: AI Technologies and Controls	38%	- 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 - AI Security Architecture 1. Model lifecycle security 2. Secure AI system design
Topic 2: 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. Stakeholder considerations 3. Organizational structure and governance - Frameworks and Compliance 1. Industry frameworks and standards (e.g., NIST AI RMF, ISO/IEC 42001) 2. Privacy and ethics considerations 3. Regulatory requirements
Topic 3: AI Risk Management	31%	- AI Threats and Vulnerabilities 1. Supply chain and vendor risks 2. Adversarial AI and ML attacks 3. Data poisoning and model manipulation - AI Risk Assessment 1. Risk identification and analysis 2. Risk thresholds and treatment

Section

Weight

Objectives

Topic 1: AI Technologies and Controls

38%

- 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

- AI Security Architecture

1. Model lifecycle security
2. Secure AI system design

Topic 2: 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. Stakeholder considerations
3. Organizational structure and governance

- Frameworks and Compliance

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

Topic 3: AI Risk Management

31%

- AI Threats and Vulnerabilities

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

- AI Risk Assessment

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

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ISACA Advanced in AI Security Management (AAISM) Exam Exam Training Torrent & AAISM Online Test Engine & ISACA Advanced in AI Security Management (AAISM) Exam Free Pdf Study

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q293-Q298):

NEW QUESTION # 293
Which of the following is the BEST way to ensure an organization remains compliant with industry regulations when decommissioning an AI system used to record patient data?

A. Ensure the certificate of destruction is received and archived in line with data retention policies
B. Update governance policies based on lessons learned and ensure a feedback loop exists
C. Ensure backups are tested and access controls are recorded and audited to ensure compliance
D. Perform a post-destruction risk assessment to verify that there is no residual exposure of data

Answer: A

Explanation:
For regulated data such as patient information, AAISM requires provable data lifecycle closure at decommissioning. The authoritative evidence is a certificate of destruction (covering primary, replicas, backups, and caches) retained per the organization's records retention policy.

NEW QUESTION # 294
An organization plans to use an open-source foundational AI model. Which of the following is MOST important for the AI governance committee to consider when approving its use?

A. Confidential data leakage
B. AI model support
C. Employee privacy rights
D. AI model accuracy

Answer: A

Explanation:
AAISM emphasizes that open-source AI models present elevated data leakage risks because internal data may flow into external, uncontrolled repositories or be used for further training. Governance bodies must prioritize the risk of data exposure, model reuse, data retention uncertainty, and uncontrolled model behavior.
While accuracy (B) and support (C) are important operational considerations, they are not the primary governance risk. Employee privacy rights (D) matter but are encompassed within the broader risk of data leakage.
References: AAISM Study Guide - Open-Source AI Governance; Data Leakage and Uncontrolled Model Behavior.

NEW QUESTION # 295
Which of the following BEST describes an adversarial attack on an AI model?

A. Providing inputs that mislead the AI model into incorrect predictions
B. Attacking the underlying hardware of the AI system
C. Conducting denial-of-service (DoS) attacks against AI APIs
D. Reverse engineering the AI model using social engineering techniques

Answer: A

Explanation:
In AAISM, an adversarial attack is defined by maliciously crafted inputs designed to cause erroneous model behavior (misclassification, targeted misprediction) while the model architecture and parameters remain unchanged.

NEW QUESTION # 296
Which of the following BEST describes how supervised learning models help reduce false positives in cybersecurity threat detection?

A. They use real-time feature engineering to automatically adjust decision boundaries
B. They analyze patterns in data to group legitimate activity from actual threats
C. They dynamically generate new labeled data sets
D. They learn from historical labeled data

Answer: D

Explanation:
According to AAISM technical content, supervised learning models reduce false positives by learning from historical labeled data that distinguishes between legitimate activity and actual threats. This training enables the model to recognize patterns and improve its discrimination ability over time. Grouping patterns (A) describes clustering, an unsupervised method. Real-time feature engineering (B) and generating new labeled data (D) are advanced techniques but not the fundamental supervised learning approach. The essence of supervised learning is leveraging labeled data to minimize misclassification, including false positives.

NEW QUESTION # 297
Which of the following recommendations would BEST help a service provider mitigate the risk of lawsuits arising from generative AI's access to and use of internet data?

A. Disclose service provider policies to declare compliance with regulations
B. Review log information that records how data was collected
C. Appoint a data steward specialized in AI to strengthen security governance
D. Activate filtering logic to exclude intellectual property flags

Answer: D

Explanation:
The AAISM materials highlight that one of the primary legal risks with generative AI systems is the unauthorized use of copyrighted or intellectual property-protected data drawn from internet sources. To mitigate lawsuits, the most effective recommendation is to implement filtering logic that actively excludes data flagged for intellectual property risks before ingestion or generation. While disclosing compliance policies, appointing governance roles, or reviewing logs are supportive measures, they do not directly prevent the core liability of using restricted content. The study guide explicitly emphasizes that proactive filtering and data governance controls are the most effective safeguards against legal disputes concerning content origin.
References:
AAISM Exam Content Outline - AI Risk Management (Legal and Intellectual Property Risks) AI Security Management Study Guide - Generative AI Data Governance

NEW QUESTION # 298
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