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ISACA AAISM Exam Syllabus Topics:

TopicDetails
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.

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q79-Q84):

NEW QUESTION # 79
Which of the following is the MOST effective defense against cyberattacks that alter input data to avoid detection by the model?

Answer: B

Explanation:
Evasion attacks manipulate inputs to induce misclassification while leaving the model unchanged. AAISM prescribes adversarial robustness controls, with adversarial training as a primary measure: incorporate adversarially perturbed examples into training/validation to harden decision boundaries and improve resilience across threat models (e.g., Lp-bounded perturbations). Monitoring (A) is detective, not preventive.
Restricting parameter access (C) protects confidentiality but does not mitigate input-space attacks.
Differential privacy (D) addresses training data leakage, not robustness to adversarial inputs.
References:AI Security Management (AAISM) Body of Knowledge: Adversarial ML-Evasion vs.
Poisoning; Robustness and Resilience Controls; Adversarial Training.AAISM Study Guide: Model Hardening Techniques; Evaluation of Robust Accuracy; Security Testing with Adversarial Examples.


NEW QUESTION # 80
An organization develops and implements an AI-based plug-in for users that summarizes their individual emails. Which of the following is the GREATEST risk associated with this application?

Answer: B

Explanation:
According to AAISM risk management guidance, the greatest risk in AI applications handling personal communication data is inadequate parameter controls, which may allow unintended access, manipulation, or leakage of sensitive information. Plug-ins that interact with emails must enforce strict parameter validation and security restrictions to prevent unauthorized or manipulated inputs. While vulnerability scanning, format incompatibility, and API rate limiting are valid concerns, they are secondary. The primary risk is a lack of strong parameter controls that could expose sensitive content.
References:
AAISM Exam Content Outline - AI Risk Management (Application Security Risks) AI Security Management Study Guide - Plug-in and API Security Risks


NEW QUESTION # 81
During red-team testing of an AI system used to make lending decisions, which of the following techniques BEST simulates a data poisoning attack?

Answer: D

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 # 82
A large financial services organization is integrating a third-party AI solution into its critical fraud detection system. Which of the following is the BEST way for the organization to reduce risk associated with AI vendor and supply chain dependencies?

Answer: A

Explanation:
AAISM emphasizes supplier assurance through contractual obligations as the foundational control for AI supply chain risk. Contracts should require verifiable evidence of secure development practices (e.g., secure SDLC, model and data provenance documentation, SBOM/MBOM where applicable, vulnerability disclosure, patch SLAs, audit rights, incident notification, and regulatory compliance assertions). This creates enforceable, continuous assurance beyond point-in-time tests.
* A is necessary but reactive and limited to your environment.
* B addresses performance, not supply chain security.
* D is a good isolation/validation practice but does not create vendor accountability across the lifecycle.
References:* AI Security Management™ (AAISM) Body of Knowledge: Third-Party and Supply Chain Governance-Contractual security requirements, evidence-based assurance, right-to-audit.* AI Security Management™ Study Guide: Vendor due diligence artifacts, secure development evidence, lifecycle obligations for AI providers.


NEW QUESTION # 83
Embedding unique identifiers into AI models would BEST help with:

Answer: B

Explanation:
The AAISM framework explains that embedding unique identifiers-such as digital watermarks or model fingerprints-enables organizations to trace and verify model provenance. This technique is used for tracking ownership and intellectual property rights over models, particularly when sharing, licensing, or distributing AI systems. While identifiers may support certain security functions, their primary control objective is ownership verification, not preventing access, bias removal, or adversarial detection. The correct alignment with AAISM controls is tracking ownership.
References:
AAISM Exam Content Outline - AI Technologies and Controls (Model Provenance and Watermarking) AI Security Management Study Guide - Ownership and Accountability of Models


NEW QUESTION # 84
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