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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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2026 AAISM Valid Dumps - High-quality ISACA Reliable AAISM Exam Cram: ISACA Advanced in AI Security Management (AAISM) Exam

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q32-Q37):

NEW QUESTION # 32
An organization implementing a large language model (LLM) application notices significant and unexpected cost increases due to excessive computational resource usage. Which vulnerability is MOST likely in need of mitigation?

Answer: B

Explanation:
AAISM highlights unbounded consumption (token/payment exhaustion, unmetered tool calls, prompt bombs) as a key LLM risk affecting cost and availability. Controls include request quotas, max tokens, rate-limits, budget guards, circuit breakers, and cost-aware routing. Excessive agency (A) relates to unsupervised actions; sensitive disclosure (B) and prompt leakage (C) are confidentiality risks, not primary drivers of runaway compute spend.
References: AI Security Management™ (AAISM) Body of Knowledge - LLM Risk Taxonomy (Abuse & Cost Risks); Guardrails: Rate-Limiting, Quotas, and Budget Controls; Resilience and Cost-Containment Patterns.


NEW QUESTION # 33
Which of the following controls would BEST help to prevent data poisoning in AI models?

Answer: B

Explanation:
The most direct preventative control against data poisoning is robust data validation/ingestion gating:
provenance checks, schema and constraint validation, anomaly/outlier screening, label consistency tests, and whitelist/blacklist source controls before data reaches training pipelines. Larger datasets (A) don't inherently prevent poisoning; monitoring (C) is detective; updating a foundation model (D) does not address tainted inputs entering the pipeline.
References: AI Security Management (AAISM) Body of Knowledge - Adversarial ML Threats and Training-Time Attacks; Secure Data Ingestion and Validation Controls. AAISM Study Guide - Poisoning Prevention: Provenance, Validation, and Sanitization Gates.


NEW QUESTION # 34
An organization has discovered that employees have started regularly utilizing open-source generative AI without formal guidance. Which of the following should be the CISO's GREATEST concern?

Answer: B

Explanation:
The greatest immediate risk from unsanctioned use of public or open-source generative AI tools is data leakage-employees may paste confidential or regulated information into third-party systems, resulting in loss of confidentiality, regulatory exposure, and loss of intellectual property. AAISM emphasizes that when AI use occurs outside approved channels, the top control priority is preventing exfiltration of sensitive data via prompts, attachments, and context sharing. Monitoring and policy are necessary enablers, but leakage is the highest-impact failure mode in the short term; hallucinations primarily affect accuracy, not confidentiality.
References:* AI Security Management™ (AAISM) Body of Knowledge: Generative AI governance; human- in-the-loop risks; data loss and exfiltration vectors in prompts; sanctioned vs. unsanctioned AI usage.* AI Security Management™ Study Guide: Immediate risk triage for shadow AI; DLP and input-control safeguards; confidentiality-first posture for generative AI adoption.


NEW QUESTION # 35
Security and assurance requirements for AI systems should FIRST be embedded in the:

Answer: D

Explanation:
AAISM directs organizations to embed security, safety, and compliance controls at design time ("secure-by- design" and "shift-left"), ensuring requirements for robustness, privacy, and governance are defined as non- functional constraints on architecture, data sourcing, model choices, and evaluation criteria before any model is trained. Deferring these requirements to training, testing, or deployment increases residual risk and rework, and weakens traceability of control coverage.
References:* AI Security Management™ (AAISM) Body of Knowledge: Governance-Secure-by-Design; Policy-to-Control Traceability; Requirements Management* AAISM Study Guide: AI Program Lifecycle- Planning & Design Controls; Design-time Threat Modeling and Control Selection* AAISM Mapping to Standards: Design-phase Risk Identification and Requirements Engineering for AI


NEW QUESTION # 36
An organization is looking to purchase an AI application from a vendor but is concerned about the security of its data. Which of the following is the MOST effective way to address this concern?

Answer: B

Explanation:
AAISM's approach to third-party and vendor risk for AI systems stresses data usage transparency as a primary control. The guidance explains that organizations must obtain clear documentation on "what data is collected, how it is processed, stored, retained, and whether it is reused for training or shared with other parties." Option C directly addresses this by requiring the vendor to disclose how the application uses organizational data, enabling appropriate risk assessment, contractual controls, and technical safeguards. An external audit (A) can be useful but may be costly and not always feasible pre-procurement. Legal discussions (B) are important but ineffective without clarity on data flows. Publicly available policies (D) are often high- level and marketing-oriented, lacking the specificity required for proper risk evaluation. Therefore, obtaining explicit data usage disclosures from the vendor is the most effective starting point.
References: AI Security Management™ (AAISM) Study Guide - Third-Party AI Risk and Data Sharing; Vendor Governance Requirements.


NEW QUESTION # 37
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