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ISACA AAISM Exam Syllabus Topics:

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

Section

Weight

Objectives

Topic 1: AI Technologies and Controls

38%

- AI Assurance and Safety

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

- 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

Topic 2: AI Governance and Program Management

31%

- AI Strategy and Policy

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

- Frameworks and Compliance

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

- AI Governance Foundations

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

Topic 3: AI Risk Management

31%

- AI Risk Assessment

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

- AI Threats and Vulnerabilities

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

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q205-Q210):

NEW QUESTION # 205
An organization plans to apply an AI system to its business, but developers find it difficult to predict system results due to lack of visibility to the inner workings of the AI model. Which of the following is the GREATEST challenge associated with this situation?

A. Gaining the trust of end users through explainability and transparency
B. Determining average turnaround time for AI transaction completion
C. Assigning a risk owner who is responsible for system uptime and performance
D. Continuing operations to meet expected AI security requirements

Answer: A

Explanation:
AAISM materials identify explainability and transparency as the greatest challenge when models operate as
"black boxes" where inner logic is opaque. Inability to interpret how results are produced undermines the trust of business users, customers, regulators, and auditors. Explainability is emphasized as a critical governance requirement, because without it, ethical validation, accountability, and regulatory compliance are at risk.
Assigning risk owners or measuring transaction times are operational concerns, but they do not address the core trust deficit caused by lack of visibility. The greatest challenge in this situation is therefore the loss of end-user trust due to insufficient explainability.
References:
AAISM Study Guide - AI Governance and Program Management (Transparency and Explainability) ISACA AI Security Management - Ethical and Trust Considerations

NEW QUESTION # 206
A financial services firm received a regulatory fine after a vendor switched its chatbot's AI model without due diligence, resulting in unethical investment advice to the firm's clients. Which of the following controls should be implemented by the firm to BEST prevent recurrence of this scenario?

A. Master services agreement
B. Shared responsibility model
C. Change management
D. Data minimization

Answer: C

Explanation:
AAISM requires formal change management for AI systems, including vendor-initiated changes: pre- approval, documented impact assessment (ethics/compliance/performance), regression testing, sign-off by accountable owners, and traceable release records. While MSAs (A) and shared responsibility models (B) set contractual/role baselines, they do not enforce per-change approvals. Data minimization (C) reduces exposure but does not control model substitutions.
References: AI Security Management (AAISM) Body of Knowledge - AI Governance: Change Control & Release Management; Third-Party AI Assurance and Approval Workflows; Accountability and Sign-off for Model Changes.

NEW QUESTION # 207
An organization deploying an LLM is concerned input manipulations could compromise security.
What is the MOST effective way to determine an acceptable risk threshold?

A. Assess the business impact of known threats
B. Implement a static threshold limiting LLM outputs
C. Restrict all inputs containing special characters
D. Deploy real-time logging and monitoring

Answer: A

Explanation:
AAISM instructs that acceptable risk thresholds must be determined using business impact analysis. This aligns with the broader enterprise risk management principle of defining tolerances based on:
- potential harm
- regulatory exposure
- financial impact
- operational disruption

NEW QUESTION # 208
In a new supply chain management system, AI models used by participating parties are interactively connected to generate advice in support of management decision making. Which of the following is the GREATEST challenge related to this architecture?

A. Determining the aggregate risk of the system
B. Identifying hallucinations returned by AI models
C. Explaining the overall benefit of the system to stakeholders
D. Establishing clear lines of responsibility for AI model outputs

Answer: D

Explanation:
The AAISM governance framework notes that in multi-party AI ecosystems, the greatest challenge is ensuring clear accountability for AI outputs. When models from different parties interact, responsibility for errors, bias, or harmful recommendations can be unclear, leading to disputes and compliance gaps. While aggregate risk assessment and error identification are significant, they are secondary to the fundamental governance requirement of establishing transparent lines of responsibility. Without defined accountability, no stakeholder can reliably manage or mitigate risks. Therefore, the greatest challenge in such a distributed architecture is responsibility for AI outputs.
References:
AAISM Study Guide - AI Governance and Program Management (Accountability in Multi-Party Systems) ISACA AI Governance Guidance - Roles and Responsibilities in AI Collaboration

NEW QUESTION # 209
A bank's fraud detection AI model is trained on customer transaction data. A customer submits a data erasure request under privacy law, but the data also exists in training sets. Which of the following is the BEST approach if there is concern that complete data removal may reduce accuracy?

A. Inform the customer that deletion from AI models is technically infeasible.
B. Update standard operating procedures to ensure that the customer's data is fully deleted.
C. Suspend the AI model until all erasure requests have been processed.
D. Deny the request because legitimate purpose of use outweighs privacy rights.

Answer: B

Explanation:
Updating standard operating procedures to ensure the customer's data is fully deleted is the best approach because organizations must comply with applicable privacy and data erasure requirements. Operational procedures should address how data is removed from training datasets and associated AI processes while balancing model maintenance and retraining considerations.

NEW QUESTION # 210
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