[2026] ISACA AAISM Questions: Fosters Your Exam Passing Abilities

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ISACA AAISM Exam Syllabus Topics:

TopicDetails
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.

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q47-Q52):

NEW QUESTION # 47
When evaluating a new AI tool for intrusion prevention, which is MOST important to ensure fit within the existing program architecture?

Answer: A

Explanation:
AAISM stresses that AI tools must align with the organization's existing control objectives and governance requirements, ensuring consistency with risk management, detection philosophy, and operational processes.
Integration with SIEM (D) is important but secondary. Anomaly detection (B) is a feature, not an architectural requirement. Automated orchestration (A) is optional.
References: AAISM Study Guide - AI Security Architecture & Control Alignment.


NEW QUESTION # 48
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.


NEW QUESTION # 49
When using AI as part of incident response, which of the following BEST ensures the automation aligns with regulatory and governance obligations?

Answer: D

Explanation:
AAISM prescribes risk-based, human-in-the-loop orchestration for safety-critical or regulated actions. A tiered automation strategy that gates autonomy by incident severity, data sensitivity, and regulatory requirements ensures accountability, auditability, and proportionality, satisfying governance obligations. Full autonomy (A) risks non-compliance; simply mirroring legacy workflows (B) may not meet current obligations; broad auto-containment (C) lacks necessary oversight controls.
References: AI Security Management (AAISM) Body of Knowledge - Governance of AI-Driven Security Automation; Human Oversight and Escalation; Risk-Based Orchestration. AAISM Study Guide - Incident Response with AI: Controls, Approvals, and Auditability.


NEW QUESTION # 50
An organization using an AI model for financial forecasting identifies inaccuracies caused by missing data. Which of the following is the MOST effective data cleaning technique to improve model performance?

Answer: B

Explanation:
The AAISM study content emphasizes that data quality management is a central pillar of AI risk reduction. Missing data introduces bias and undermines predictive accuracy if not addressed systematically. The most effective remediation is to apply statistical imputation and related methods to fill in or adjust for missing values in a way that minimizes bias and preserves data integrity. Retraining on flawed data does not solve the underlying issue. Deleting outliers may harm model robustness, and hyperparameter tuning optimizes model mechanics but cannot resolve missing information. Therefore, the proper corrective technique for missing data is the application of statistical methods to reduce bias.


NEW QUESTION # 51
Which of the following should be included in an AI acceptable use policy?

Answer: A

Explanation:
An AI acceptable use policy (AUP) sets the organizational expectations and boundaries for how AI systems may be used by employees and third parties. AAISM guidance places emphasis on ethical and legal compliance standards as core elements of an AUP to govern responsible behavior, prevent misuse, and align with regulatory and organizational principles. While data requirements, collection/storage processes, and monitoring may be covered in adjacent standards and procedures (e.g., data management policies, SOPs, and operational runbooks), the AUP's essential function is to codify permissible use anchored to ethics, legality, and organizational values.
References: AI Security Management (AAISM) Body of Knowledge - AI Governance Policies and Codes of Conduct; Responsible Use Principles. AAISM Study Guide - Policy Hierarchy and Control Mapping; Acceptable Use and Staff Obligations.


NEW QUESTION # 52
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