ISACA AAISM Free Practice & AAISM Flexible Testing Engine

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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 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 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 (Q247-Q252):

NEW QUESTION # 247
Which of the following would BEST ensure a proper business continuity plan (BCP) is in place for an AI solution?

Answer: D

Explanation:
Effective AI BCP requires validation through exercises and controlled failover tests to prove recovery objectives can be met in practice. Merely documenting backups (Option D), hardening access (Option B), or improving monitoring (Option A) does not confirm that the AI stack-data pipelines, feature stores, model registries, inference services, and dependent infrastructure-can actually fail over and recover within RTO
/RPO. AAISM prescribes periodic BCP/DR testing (including model artifact restoration, configuration reconstitution, dependency failover, and data pipeline continuity) to verify readiness and identify gaps before real incidents.
References:AI Security Management™ (AAISM) Body of Knowledge: Business Continuity & Disaster Recovery for AI; Validation and Exercising of Continuity Plans; RTO/RPO for Models, Data, and Pipelines.
AAISM Study Guide: Operational Resilience for AI Systems; BCP/DR Test Scenarios (model registry, feature store, pipeline recovery); Continuity Metrics and Evidence of Readiness.


NEW QUESTION # 248
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: D

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 # 249
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?

Answer: A

Explanation:
AAISM directs organizations to manage third-party AI risks through contractual and technical controls that explicitly govern data use, retention, training/fine-tuning, isolation, and deletion. The most effective data- security action when consuming generative AI features is to require enforceable opt-out provisions that prohibit the provider from using the organization's data for training or secondary purposes and that mandate retention limits and secure deletion. Third-party audit reports (A) provide assurance but do not guarantee provider behavior for your specific data; awareness policies (B) are necessary but insufficient to control external processing; IP ownership guidelines (D) address legal rights, not data-security risk.
References: AI Security Management™ (AAISM) Body of Knowledge - Third-Party/Procurement Controls; Data Use & Retention Clauses; Training/Fine-tuning Opt-Out; Secure Deletion and Purpose Limitation.


NEW QUESTION # 250
The PRIMARY goal of data poisoning attacks is to:

Answer: D

Explanation:
AAISM defines data poisoning as the insertion of malicious or corrupted data into training (or fine-tuning) pipelines to degrade or bias model behavior, thereby compromising output integrity in production. While poisoning occurs during development/training (C), its primary objective is the downstream integrity impact on predictions/outputs (D). Options A and B relate to confidentiality threats (e.g., inversion or leakage), not poisoning.
References:* AI Security Management (AAISM) Body of Knowledge: Model Integrity Threats-data poisoning aims and effects; supply-side data controls* AAISM Study Guide: Risk scenarios for poisoning; mitigations via data provenance checks, robust training, and anomaly detection


NEW QUESTION # 251
Which of the following is the MOST effective defense against cyberattacks that alter input data to avoid detection by the model?

Answer: A

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 # 252
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