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[General] Free PDF 2026 ISACA Updated AAISM: Valid ISACA Advanced in AI Security Managemen

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【General】 Free PDF 2026 ISACA Updated AAISM: Valid ISACA Advanced in AI Security Managemen

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q17-Q22):NEW QUESTION # 17
Which of the following is the BEST way to ensure an organization remains compliant with industry regulations when decommissioning an AI system used to record patient data?
  • A. Ensure the certificate of destruction is received and archived in line with data retention policies
  • B. Ensure backups are tested and access controls are recorded and audited to ensure compliance
  • C. Update governance policies based on lessons learned and ensure a feedback loop exists
  • D. Perform a post-destruction risk assessment to verify that there is no residual exposure of data
Answer: A
Explanation:
For regulated data such as patient information, AAISM requires provable data lifecycle closure at decommissioning. The authoritative evidence is a certificate of destruction (covering primary, replicas, backups, and caches) retained per the organization's records retention policy. While testing backups and auditing access (A), updating policies (B), and doing post-destruction risk assessment (C) are valuable practices, documented destruction attestation is the primary compliance proof point that the data was disposed of in accordance with regulatory and contractual obligations.
References: AI Security Management™ (AAISM) Body of Knowledge - Data Lifecycle Governance; Decommissioning & Secure Disposal; Records Retention and Evidence of Destruction.

NEW QUESTION # 18
Which BEST describes the role of model cards in AI solutions?
  • A. They visualize AI model performance
  • B. They document training data and AI model use cases
  • C. They help developers create synthetic data
  • D. They automatically fine-tune AI models
Answer: B
Explanation:
AAISM explains that model cards provide structured documentation about AI models, including:
* intended use cases
* training data characteristics
* ethical considerations
* known limitations
* risk factors
* performance benchmarks
They are not visualization tools (A), do not create synthetic data (C), and do not tune models (D).
References: AAISM Study Guide - AI Transparency & Model Cards.

NEW QUESTION # 19
Who is responsible for implementing recommendations in a final report after an external AI compliance audit?
  • A. Model owners
  • B. Internal auditors
  • C. System architects
  • D. End users
Answer: A
Explanation:
Under AAISM governance, management and control owners are accountable for remediation. For AI systems, the accountable role is the Model Owner (or equivalent business/service owner), who coordinates with architects, engineers, and operations to implement corrective actions and report closure. Internal auditors provide independent assurance and do not implement fixes; end users are not remediation owners.
References: AI Security Management (AAISM) Body of Knowledge - Roles & Accountability (Model Owner, Control Owner, Assurance); Audit Findings Management and Remediation Governance.

NEW QUESTION # 20
Which of the following approaches BEST enables the separation of sensitive and shareable data to prevent an AI chatbot from inadvertently disclosing confidential information?
  • A. Sandboxing
  • B. Zero Trust
  • C. Siloing
  • D. Containerization
Answer: C
Explanation:
AAISM materials describe data segregation and segmented access as core technical controls to prevent unintended information disclosure by AI systems. Siloing refers to logically or physically separating data into distinct repositories or contexts, ensuring that sensitive datasets are not available to components or applications that only require non-sensitive information. This is directly aligned with preventing a chatbot from accessing or mixing confidential data with general conversational content. Zero Trust (A) is an overarching security architecture principle, focusing on identity and continuous verification; it does not by itself guarantee separation of data. Sandboxing (B) isolates processes but is less about fine-grained data separation. Containerization (D) packages applications and their dependencies, again not necessarily solving the specific problem of mixing sensitive and non-sensitive datasets. Siloing is explicitly highlighted as a way to prevent cross-context leakage in AI use cases.
References: AI Security Management™ (AAISM) Study Guide - Technical Controls for AI Data Protection; Data Segregation and Access Boundaries.

NEW QUESTION # 21
A retail organization implements an AI-driven recommendation system that utilizes customer purchase history. Which of the following is the BEST way for the organization to ensure privacy and comply with regulatory standards?
  • A. Storing customer data indefinitely to ensure the AI model has a complete history
  • B. Conducting quarterly retraining of the AI model to maintain the accuracy of recommendations
  • C. Establishing a governance committee to oversee AI privacy practices
  • D. Maintaining a register of legal and regulatory requirements for privacy
Answer: D
Explanation:
According to the AI Security Management™ (AAISM) study framework, compliance with privacy and regulatory standards must begin with a formalized process of identifying, documenting, and maintaining applicable obligations. The guidance explicitly notes that organizations should maintain a comprehensive register of legal and regulatory requirements to ensure accountability and alignment with privacy laws. This register serves as the foundation for all governance, risk, and control practices surrounding AI systems that handle personal data.
Maintaining such a register ensures that the recommendation system operates under the principles of privacy by design and privacy by default. It allows decision-makers and auditors to trace every AI data processing activity back to relevant compliance obligations, thereby demonstrating adherence to laws such as GDPR, CCPA, or other jurisdictional mandates.
Other measures listed in the options contribute to good practice but do not achieve the same direct compliance outcome. Retraining models improves technical accuracy but does not address legal obligations. Oversight committees are valuable but require the documented register as a baseline to oversee effectively. Indefinite storage of customer data contradicts regulatory requirements, particularly the principle of data minimization and storage limitation.
AAISM Domain Alignment:
This requirement falls under Domain 1 - AI Governance and Program Management, which emphasizes organizational accountability, policy creation, and maintaining compliance documentation as part of a structured governance program.
References from AAISM and ISACA materials:
AAISM Exam Content Outline - Domain 1: AI Governance and Program Management AI Security Management Study Guide - Privacy and Regulatory Compliance Controls ISACA AI Governance Guidance - Maintaining Registers of Applicable Legal Requirements

NEW QUESTION # 22
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