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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 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 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 (Q309-Q314):

NEW QUESTION # 309
Secure aggregation enhances the security of federated learning systems by:

Answer: C

Explanation:
Secure aggregation cryptographically aggregates client updates so that the server learns only the sum/aggregate, not any single client's update. Properly implemented, the server cannot recover individual contributions--even if compromised--thereby preserving client confidentiality.


NEW QUESTION # 310
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: B

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 # 311
Which of the following is the MOST effective strategy for penetration testers assessing the security of an AI model against membership inference attacks?

Answer: C

Explanation:
AAISM identifies confidence-score analysis as a principal technique for evaluating exposure to membership inference: models often yield measurably higher confidence for points seen during training. Testers compare output probabilities/entropies for known in-training vs. out-of-training samples to assess leakage. Disabling logs (A) reduces evidence; test-set accuracy (B) does not measure privacy leakage; synthetic data generation (D) is a mitigation strategy, not a penetration-testing method.
References: AI Security Management (AAISM) Body of Knowledge - Model Privacy Threats:
Membership Inference; Red/Blue Team Evaluation Techniques; Confidence/Entropy-based Privacy Testing.


NEW QUESTION # 312
A retailer's third-party large language model (LLM) for customer queries has a high likelihood of prompt injection with critical impact if personally identifiable information (PII) is leaked, exceeding the organization's risk tolerance. Which of the following would BEST reduce this risk to an acceptable level?

Answer: D

Explanation:
Prompt injection exploits inputs to manipulate the model into exposing sensitive data. Applying data minimization and input filtering directly reduces the likelihood of PII being included in prompts or processed in a way that could be exposed, thereby lowering the risk to an acceptable level.


NEW QUESTION # 313
An organization has implemented a natural language processing model to respond to customer questions when personnel are not available. A pre-implementation security assessment revealed attackers could access sensitive company data through a chat interface injection attack. Which of the following is the BEST way to prevent this attack?

Answer: A

Explanation:
To prevent prompt/interface injection, AAISM prioritizes preventive technical controls at the boundary: input validation/sanitization, structured templates/system prompts, allow/deny lists, and context isolation. These measures constrain user-supplied content and block adversarial instructions from being interpreted as system directives. Monitoring (A) and audits (D) are detective/assurance activities; manual output review (B) is compensating but less scalable and does not prevent injection.
References: AI Security Management™ (AAISM) Body of Knowledge - Secure Prompting & Input Controls; Interface Injection Mitigations; Context and Instruction Isolation Patterns.


NEW QUESTION # 314
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