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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
Related Certifications:	CISM CISSP
Exam Price:	USD 459 (member) / USD 599 (non-member)
Exam Format:	Multiple-choice questions
Real Exam Qty:	90
Certificate Validity Period:	3 years (continuing professional education required for renewal)
Available Languages:	Spanish, English
Exam Duration:	150 minutes
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 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.
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 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.

ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q286-Q291):

NEW QUESTION # 286
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. Security training to avoid leakage of personal data
B. Masking or tokenizing customer-related data in use
C. Conducting awareness training for AI developers
D. Performing periodic audits of the AI model for compliance

Answer: B

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 # 287
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. Conducting denial-of-service (DoS) attacks against AI APIs
C. Reverse engineering the AI model using social engineering techniques
D. Attacking the underlying hardware of the AI system

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. Hardware compromise (A) and DoS (D) are system-level attacks, not adversarial ML per se.
Social engineering to obtain model information (C) is an information-gathering or security breach vector, distinct from input-space adversarial manipulation.
References:AI Security Management™ (AAISM) Body of Knowledge: Taxonomy of AI Threats- Adversarial Examples and Evasion; Distinction from System/Network Attacks.AAISM Study Guide:
Definitions and Threat Models in Adversarial ML; Attack-Defense Mapping for Model Predictions.

NEW QUESTION # 288
AI developers often find deep learning systems difficult to explain PRIMARILY because:

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

Answer: B

Explanation:
AAISM notes that deep learning systems lack transparency due to complex neural architectures, where internal representations are statistical, nonlinear, and not directly interpretable.
While probability (C) and data sourcing (D) contribute to opacity, the root cause is the intrinsic complexity and opacity of deep neural networks.
References: AAISM Study Guide - Explainability Challenges in Deep Learning.

NEW QUESTION # 289
Which of the following is the MOST effective action an organization can take to address data security risk when using generative AI features in an application?

A. Establish policies and awareness training for acceptable AI use
B. Establish IP ownership guidelines with third parties
C. Require opt-out provisions for data usage
D. Rely on the AI provider's independent audit reports

Answer: A

Explanation:
AAISM stresses that the largest and most immediate risk for generative AI is unintentional data leakage by employees, making acceptable-use policies and staff training the most effective short-term risk mitigation.
Users must be instructed not to input sensitive data, understand usage restrictions, and follow governance requirements.
Relying solely on third-party audits (D) is insufficient. IP agreements (A) and opt-out clauses (B) are contractual protections, not operational safeguards.
References: AAISM Study Guide - Generative AI Security Risks; Policy and Awareness as Primary Controls.

NEW QUESTION # 290
During red-team testing of an AI system used to make lending decisions, which of the following techniques BEST simulates a data poisoning attack?

A. Adding noise to output predictions
B. Inputting encrypted data into the model
C. Corrupting training data sets to manipulate outcomes
D. Stealing model weights from a deployed API

Answer: C

Explanation:
AAISM defines data poisoning as the intentional manipulation of training data so that the learned model behaves incorrectly (e.g., skewed lending approvals/denials) while appearing valid. The correct simulation in red-team exercises is to corrupt or seed the training set with adversarial examples or mislabeled records to induce biased or erroneous decision boundaries. Encrypting inputs (A) is unrelated; output noise (B) describes perturbation of predictions, not training; model weight theft (C) is model extraction, not poisoning.
References: AI Security Management (AAISM) Body of Knowledge - Adversarial ML Threats; Data Poisoning and Training-Time Attacks. AAISM Study Guide - Red-Team Methods for AI; Poisoning vs.
Evasion vs. Model Extraction; Controls and Testing for Safety-Critical Decisions.

NEW QUESTION # 291
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