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ISACA AAISM Exam Overview:

Certification Vendor:	ISACA
Exam Name:	ISACA Advanced in AI Security Management (AAISM) Exam
Exam Number:	AAISM
Available Languages:	English
Passing Score:	450 (out of 800)
Exam Format:	Multiple-choice, Scenario-based
Real Exam Qty:	90
Related Certifications:	CISM CISSP
Certificate Validity Period:	3 years
Exam Duration:	150 minutes
Exam Price:	US$459 (members), US$599 (non-members)
Recommended Training:	ISACA Official AAISM Training
Exam Registration:	ISACA AAISM Registration
Sample Questions:	ISACA AAISM Sample Questions
Exam Way:	Computer-based; available at authorized PSI testing centers or via remote proctoring
Pre Condition:	Hold an active CISM or CISSP certification
Official Syllabus URL:	https://www.isaca.org/credentialing/aaism/aaism-exam-content-outline

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ISACA AAISM Exam Syllabus Topics:

Topic	Details
Topic 1	AI Risk Management: This section of the exam measures the skills of AI Risk Managers and covers assessing enterprise threats, vulnerabilities, and supply chain risk associated with AI adoption, including risk treatment plans and vendor oversight.
Topic 2	AI Governance and Program Management: This section of the exam measures the abilities of AI Security Governance Professionals and focuses on advising stakeholders in implementing AI security through governance frameworks, policy creation, data lifecycle management, program development, and incident response protocols.
Topic 3	AI Technologies and Controls: This section of the exam measures the expertise of AI Security Architects and assesses knowledge in designing secure AI architecture and controls. It addresses privacy, ethical, and trust concerns, data management controls, monitoring mechanisms, and security control implementation tailored to AI systems.

ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q164-Q169):

NEW QUESTION # 164
When documenting information about machine learning (ML) models, which of the following artifacts BEST helps enhance stakeholder trust?

A. Data quality controls
B. Model card
C. Hyperparameters
D. Model prototyping

Answer: B

Explanation:
Themodel cardis a governance artifact that communicatesintended use, performance characteristics, limitations, fairness considerations, and ethical notesof an ML model.
AAISM governance materials highlight thatstakeholder trustcomes from transparency and explainability.
While hyperparameters and data quality controls are technical details, they lack stakeholder-facing clarity.
Model prototyping is part of development but not a governance record.
Model cards are explicitly recommended to:
* Provideexplainabilityand context for decision-making.
* Demonstrate governance, transparency, and compliance.
* Enable stakeholders (regulators, auditors, business leaders) to trust the system.
Therefore, themodel cardis the artifact that best enhances trust.

NEW QUESTION # 165
An AI fraud detection system in a bank uses transaction data that includes customer names and account numbers. Which of the following BEST helps the bank comply with privacy regulations and data classification policies?

A. Masking or tokenizing customer-related data in use
B. Conducting awareness training for AI developers
C. Performing periodic audits of the AI model for compliance
D. Security training to avoid leakage of personal data

Answer: A

Explanation:
Masking or tokenizing customer names and account numbers directly protects sensitive and regulated data while allowing the AI system to continue processing transactions. This is the most effective control for supporting compliance with privacy regulations and data classification requirements.

NEW QUESTION # 166
A financial organization is concerned about AI data poisoning. Which control BEST mitigates this risk?

A. Using training data from multiple sources
B. Implementing a break-glass policy
C. Transparency with customers about data sources
D. Delivering AI-specific security awareness training

Answer: A

Explanation:
AAISM outlines that diversifying training data sources reduces the likelihood and impact of poisoning because:
* attack samples become harder to inject
* anomalies are easier to detect
* corrupted data has reduced influence on model behavior
Break-glass procedures (A) relate to incident response, not mitigation. Customer transparency (B) does not stop poisoning. Awareness training (D) is insufficient alone.
References: AAISM Study Guide - Data Poisoning Mitigation; Data Diversity and Source Validation.

NEW QUESTION # 167
A financial organization uses AI to detect potential fraudulent activities but is concerned about the impact of potential data poisoning. Which of the following controls would BEST mitigate this risk?

A. Using training data from multiple sources
B. Being transparent with customers about the data sources
C. Implementing an updated and tested break-glass policy
D. Delivering AI-specific security awareness training

Answer: A

Explanation:
AAISM identifies training-data diversity and provenance assurance as primary treatments against data poisoning. Sourcing data from multiple, independently governed providers, combined with ingestion validation and anomaly screening, reduces the chance that a single compromised source can skew model behavior and improves cross-source consistency checks. Transparency, break-glass, and awareness are valuable but do not directly reduce poisoning exposure at the training boundary.
References: AI Security Management™ (AAISM) Body of Knowledge - Data Governance & Integrity for AI; Adversarial ML: Poisoning Threats and Mitigations; Supplier and Source Diversification Controls.

NEW QUESTION # 168
Which of the following is the MOST effective way to mitigate the risk of deepfake attacks?

A. Validating the provenance of the data source
B. Using a general-purpose large language model (LLM) to detect fraud
C. Relying on human judgment for oversight
D. Limiting employee access to AI tools

Answer: A

Explanation:
AAISM study content identifies validating the provenance of data sources as the most effective way to counter deepfake risks. Provenance validation ensures that content is authentic, verifiable, and traceable, preventing malicious synthetic media from being trusted as legitimate. Human oversight helps but cannot reliably detect sophisticated fakes. Limiting tool access reduces exposure but does not prevent external attacks. General-purpose LLMs are not optimized for fraud detection. The strongest control is verifying the origin and authenticity of data before acceptance.
References:
AAISM Study Guide - AI Risk Management (Deepfake and Content Integrity Risks) ISACA AI Security Management - Provenance Validation as a Defense

NEW QUESTION # 169
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