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ISACA AAISM Exam Overview:

Certification Vendor:	ISACA
Exam Name:	Advanced in AI Security Management (AAISM) Exam
Exam Number:	AAISM
Passing Score:	450/800
Exam Price:	USD 459 (member) / USD 599 (non-member)
Exam Format:	Multiple-choice questions
Available Languages:	Spanish, English
Certificate Validity Period:	3 years (continuing professional education required for renewal)
Exam Duration:	150 minutes
Related Certifications:	CISSP CISM
Real Exam Qty:	90
Recommended Training:	ISACA Official Training & Review Materials
Exam Registration:	ISACA AAISM Credentialing Page
Sample Questions:	ISACA AAISM Sample Questions
Exam Way:	Computer-based exam (online proctored or authorized test center)
Pre Condition:	Recommended: Active CISM or CISSP certification and experience in IT/security management or advisory roles
Official Syllabus URL:	https://www.isaca.org/credentialing/aaism

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ISACA AAISM Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 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 (Q154-Q159):

NEW QUESTION # 154
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. Implement resilient payment approval processes
D. Issue a security policy on deepfakes

Answer: C

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 # 155
Which of the following is the BEST reason to involve a diverse team when evaluating the data used within an AI system?

A. It reduces the perception of bias by the media and users.
B. It ensures that scenarios are reviewed thoroughly.
C. It ensures the output is representative of target end users.
D. It reduces the likelihood of a risk being overlooked.

Answer: D

Explanation:
Involving a diverse team provides broader perspectives, experiences, and expertise during evaluation of AI data. This helps identify hidden biases, ethical concerns, security issues, and other risks that might otherwise be overlooked by a less diverse review group.

NEW QUESTION # 156
Continuous monitoring and training of an AI system BEST helps to control:

A. private data leakage.
B. evasion attacks.
C. model poisoning.
D. model drift.

Answer: D

Explanation:
Continuous monitoring and training allow the organization to detect changes in model performance over time and retrain the model as needed, directly addressing model drift caused by evolving data patterns.

NEW QUESTION # 157
Which of the following is the MOST effective way to identify and address security risk in an AI model?

A. Conduct threat modeling to identify vulnerabilities and possible attack methods
B. Add more data to the model to increase its accuracy and reduce errors
C. Assign staff to review AI model outputs for accuracy
D. Encrypt the training data and model parameters to prevent unauthorized access

Answer: A

Explanation:
AI/ML threat modeling is the most effective structured method to both identify and address model security risks. It systematically surfaces attack classes (poisoning, evasion, membership inference, model extraction, inversion), maps system-specific attack surfaces (data pipelines, feature stores, training artifacts, inference APIs), and drives prioritized mitigations (ingestion validation, robust training, rate-limiting, watermarking, differential privacy, monitoring, red teaming). Output spot-checking (A) finds errors but not security vulnerabilities; encryption (C) protects confidentiality but does not reveal threats or mitigate inference-time attacks; adding data (D) may improve accuracy but does not target adversarial risk.
References: AI Security Management (AAISM) Body of Knowledge - AI Risk Identification & Threat Modeling; Attack Surface Analysis for ML; Risk Treatment Planning. AAISM Study Guide - Evasion
/Poisoning/Extraction Controls; Mapping Risks to Controls; Validation and Assurance Activities.

NEW QUESTION # 158
An organization is developing an AI application to help domestic students with their coursework.
The marketing team has requested that training data in non-native languages be included in anticipation of future expansion to international markets. Which of the following would be the GREATEST security concern in this situation?

A. Increased hallucinations
B. Unstable infrastructure
C. Increased log generation
D. Expanded attack surface

Answer: D

Explanation:
Including training data in multiple non-native languages increases the AI system's exposure to a broader range of inputs, linguistic variations, and adversarial manipulation techniques. This expands the attack surface and creates additional challenges for monitoring, validation, content filtering, and security control effectiveness across languages.

NEW QUESTION # 159
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