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ISACA AAISM Exam Syllabus Topics:

TopicDetails
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 (Q410-Q415):

NEW QUESTION # 410
An organization plans to use a natural language processing (NLP) system that processes and analyzes sensitive data. The data science team has proposed storing all raw text data indefinitely for potential model improvements and additional analytics. Which of the following is the BEST course of action?

Answer: A

Explanation:
Establishing data retention limits based on legitimate purpose ensures sensitive data is only stored as long as necessary, reducing privacy risk and aligning with data protection principles and regulatory requirements.


NEW QUESTION # 411
Which of the following is MOST important for an organization to consider when implementing a preventive security safeguard into a new AI product?

Answer: A

Explanation:
AAISM materials emphasize that the most effective preventive safeguard is to ensure input sanitization. Preventive controls stop malicious or malformed inputs from reaching the model in the first place, thereby reducing the likelihood of prompt injection, evasion, or poisoning at inference time. Model output monitoring is a detective control, not preventive. Penetration testing is an assessment technique rather than a safeguard. Differential privacy protects data privacy but does not prevent adversarial input manipulation. Therefore, the most important preventive safeguard in a new AI product is robust input sanitization.


NEW QUESTION # 412
A post-incident investigation finds that an AI-powered anti-money laundering system inadvertently allowed suspicious transactions because certain risk signals were disabled to reduce false positives. Which of the following governance failures does this BEST demonstrate?

Answer: A

Explanation:
AAISM states that AI risk signals, thresholds, and model logic must be governed through strict validation and change control processes. Disabling key risk indicators without formal review or testing directly reflects a failure in:
- AI model validation
- Change management
- Governance oversight


NEW QUESTION # 413
Which of the following is the PRIMARY purpose of a dedicated AI system policy?

Answer: C

Explanation:
Per AAISM, an AI policy is a governance instrument that defines objectives, principles, roles, responsibilities, accountability, and control requirements for AI systems across their lifecycle. It establishes the framework within which performance, compliance, ethics, risk appetite, security, privacy, and sustainability objectives are set and operationalized. Environmental considerations (A), accuracy optimization (B), and regulatory compliance (D) are important outcomes addressed under the policy, but the primary purpose is to provide the overarching framework for objectives and controls.
References: AI Security Management (AAISM) Body of Knowledge - AI Governance Frameworks; Policies, Standards, and Procedures; Roles and Accountability in AI Programs.


NEW QUESTION # 414
When robust input controls are not practical on a large language model (LLM) to prevent prompt injection attacks from external threats, which of the following would be the BEST compensating control to address the risk?

Answer: D

Explanation:
When preventive input hardening isn't feasible for LLMs, AAISM prescribes compensating detective and corrective controls-notably human review and annotation of outputs prior to downstream action-to reduce harm from prompt injection. Output-side review gates prevent untrusted instructions from propagating, enable rapid suppression/feedback loops, and provide labeled examples for subsequent model hardening. IAM (B) is necessary but does not mitigate injection in content; reviewing inputs (C) is less effective than auditing what the model is about to act on; fine-tuning for validation (D) is helpful long-term but is not an immediate compensating control when robust input validation is impractical.
References: AI Security Management™ (AAISM) Body of Knowledge - LLM Threats & Compensating Controls; Human Oversight & Output Review Gates; Post-incident Feedback and Labeling for Model Hardening.


NEW QUESTION # 415
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