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ISACA AAISM Exam Syllabus Topics:

Topic	Details
Topic 1	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 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 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.

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q345-Q350):

NEW QUESTION # 345
Which of the following BEST ensures AI components are validated during disaster recovery testing?

A. Disconnecting model training clusters to test retraining workflows
B. Running simulated data-loss scenarios by deleting test feature-store records
C. Monitoring model performance during failover and recovery
D. Simulating DoS attacks on AI APIs

Answer: C

Explanation:
AAISM states that AI disaster recovery testing must validate that models behave correctly during failover. The only option that tests actual operational continuity of AI components is:
monitoring model performance during failover
This validates stability, functionality, and resilience under disaster conditions.

NEW QUESTION # 346
An AI system that supports critical processes has deviated from expected performance and is producing biased outcomes. Which of the following is the BEST course of action?

A. Retrain the model with a new and expanded dataset
B. Activate the model kill switch
C. Perform a root cause analysis to identify mitigation steps
D. Conduct audits of the data and the model

Answer: C

Explanation:
AAISM directs that when harmful or biased behavior is observed in a production AI system, the organization should enter a formal incident/variance handling workflow that begins with root cause analysis (RCA) to identify the source of deviation (data drift, concept drift, feature leakage, pipeline changes, control failures) and determine proportionate risk treatments.

NEW QUESTION # 347
A security assessment revealed that attackers could access sensitive company data through chat interface injection. What is the BEST mitigation?

A. Implementing input validation and templates
B. Manually reviewing AI model outputs
C. Conducting regular security audits
D. Ensuring continuous monitoring and tagging

Answer: A

Explanation:
AAISM explains that prompt injection attacks are best mitigated by:
* strict input validation
* templated prompts
* controlled context windows
* guardrail enforcement
These prevent malicious instructions from overriding system prompts.
Audits (A) are periodic, not preventive. Manual review (B) is not scalable. Monitoring (D) detects issues but does not block injection.
References: AAISM Study Guide - Prompt Injection & Input Control Mechanisms.

NEW QUESTION # 348
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?

A. Require opt-out provisions for data usage in service agreements
B. Establish policies and awareness training for acceptable use of AI
C. Establish guidelines and best practices with third parties for intellectual property ownership
D. Rely on the AI provider's independent third-party audit reports for assurance

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 # 349
Which of the following is the MOST important factor to consider when selecting industry frameworks to align organizational AI governance with business objectives?

A. Risk threshold
B. Risk tolerance
C. Risk appetite
D. Risk register

Answer: C

Explanation:
According to AAISM governance principles, the risk appetite of the organization is the most important factor in selecting appropriate frameworks for AI governance. Risk appetite defines the level of risk an organization is willing to accept in pursuit of its objectives, ensuring frameworks are aligned with strategic goals. Risk tolerance and thresholds are operational measures derived from appetite, and the risk register is a documentation tool. The foundational consideration for framework alignment is the organization's risk appetite.

NEW QUESTION # 350
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