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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 (Q268-Q273):

NEW QUESTION # 268
After implementing a third-party generative AI tool, an organization learns about new regulations related to how organizations use AI. Which of the following would be the BEST justification for the organization to decide not to comply?

Answer: A

Explanation:
The AAISM framework clarifies that compliance decisions must always be tied to an organization's risk appetite and tolerance. When new regulations emerge, management may choose not to comply if the associated risk remains within the documented and approved risk appetite, provided that accountability is established and governance structures support this decision. Other options such as widespread industry use, third-party audits, or lack of cost assessment do not justify noncompliance under the governance principles.
The risk appetite framework is the only recognized justification under AI governance principles.
References:
AAISM Study Guide - AI Governance and Program Management
ISACA AI Risk Guidance - Risk Appetite and Compliance Decisions


NEW QUESTION # 269
An organization is adopting an agentic AI solution from an external vendor to support its internal IT operations. To evaluate the security posture of this system, which of the following provides the MOST reliable and independently verifiable evidence of implemented security controls?

Answer: A

Explanation:
Third-party audit reports provide independent assurance that the vendor's stated controls are designed and operating effectively against recognized criteria. Such attestations (e.g., audit/assurance frameworks) are traceable, repeatable, and verifiable, and they support supply-chain risk reviews and contractual assurance.
Internal red-team reports are not independent, industry "peer reviews" are not control attestations, and whitepapers are marketing/educational materials without evidence of control operation.
References: AI Security Management (AAISM) Body of Knowledge: Third-Party & Supply-Chain Assurance; Independent Assessment and Audit Evidence; Vendor Risk Management. AAISM Study Guide:
Assurance Artifacts for AI Services; Evaluating External AI Providers.


NEW QUESTION # 270
Which of the following technologies can be used to manage deepfake risk?

Answer: A

Explanation:
The AAISM study material highlights blockchain as a control mechanism for managing deepfake risk because it provides immutable verification of digital media provenance. By anchoring original data signatures on a blockchain, organizations can verify authenticity and detect tampered or synthetic content. Data tagging helps organize but does not guarantee authenticity. MFA and adaptive authentication strengthen identity security but do not address content manipulation risks. Blockchain's immutability and traceability make it the recognized technology for mitigating deepfake challenges.
References:
AAISM Study Guide - AI Technologies and Controls (Emerging Controls for Content Authenticity) ISACA AI Governance Guidance - Blockchain for Data Integrity and Deepfake Mitigation


NEW QUESTION # 271
When addressing privacy concerns related to AI systems, which of the following is the GREATEST significance of user consent for an organization?

Answer: B

Explanation:
Within AAISM's privacy governance, consent is a lawful basis that authorizes processing of personal data for defined purposes. Its principal significance is granting the organization the authority to process user data in AI workflows in line with stated purposes and limits. While fairness (A) and security controls (C) are essential, they are distinct obligations; data subject rights such as rectification/erasure (B) exist regardless of consent and are not "enabled" by it. Therefore, the greatest significance of consent is that it legally permits processing under declared purposes and constraints.
References: AI Security Management™ (AAISM) Body of Knowledge - Privacy & Data Protection in AI; Lawful Basis and Purpose Limitation. AAISM Study Guide - Consent, Transparency, and Data Subject Rights; Privacy-by-Design Controls in AI Pipelines.


NEW QUESTION # 272
The PRIMARY AI governance risk of inconsistently applying data augmentation and cleansing across datasets is that the model may:

Answer: C

Explanation:
Inconsistent data augmentation and cleansing can introduce or amplify bias within datasets, resulting in unreliable or unfair model behavior. This undermines both data integrity and model integrity, creating significant governance, ethical, and operational risks.


NEW QUESTION # 273
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