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The value of professional qualification has been shown to rise with time. For the advancement of your profession, exams like the ISACA exam given by ISACA are crucial. Candidates aim to pass the ISACA Advanced in AI Security Management (AAISM) Exam exam on their first attempt. With ISACA AAISM Exam Questions, applicants may study for and pass their desired certification exam on the first attempt. You may use BraindumpsPrep's top AAISM study resources to prepare for the ISACA Advanced in AI Security Management (AAISM) Exam exam. The ISACA AAISM exam questions offered by BraindumpsPrep are dependable and trustworthy sources of preparation.

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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 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 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.

ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q149-Q154):

NEW QUESTION # 149
Which attack type is MOST likely to cause model drift?

Answer: A

Explanation:
AAISM defines data poisoning as directly capable of causing model drift because corrupted training data shifts the statistical distribution, leading to degraded or unsafe performance.
Model stealing (A) extracts model behavior but does not cause drift. Perfect knowledge (B) is an attacker capability, not an attack causing drift. Membership inference (D) attacks privacy, not performance.
References: AAISM Study Guide - Model Drift Causes; Data Poisoning Impact.


NEW QUESTION # 150
Which of the following is the GREATEST concern when a vendor enables generative AI features for an organization's critical system?

Answer: D

Explanation:
When enabling genAI capabilities in a critical system, AAISM prioritizes controlling access to the model and its interfaces (prompt surfaces, context windows, tools/functions, and connected data) because exposure expands the attack surface for prompt injection, data exfiltration, jailbreaks, and misuse. Monitoring (C) is necessary but detective; ethics and bias (D) are vital but secondary to immediate safety and security of a mission-critical environment; proposed regulations (B) are not an immediate operational risk.
References: AAISM Body of Knowledge: GenAI Security-Access Governance, Interface Hardening, and Prompt Surface Controls; AAISM Study Guide: Critical System Safeguards-Least Privilege, Guardrails, and Abuse Prevention.


NEW QUESTION # 151
Which of the following actions BEST enables the evaluation of bias during an AI impact assessment?

Answer: D

Explanation:
In AI Security Management, bias evaluation is primarily a data problem before it is a model or performance problem. The official AI Security Management content explains that impact assessments must specifically analyze training data representativeness against the demographics and characteristics of all relevant users and stakeholders. If the data used to train an AI system underrepresents or omits particular groups, the resulting model will systematically disadvantage them, regardless of how fast or "accurate" it is on average. While comparing outputs to historical data (option B) can help, historical data itself may be biased. Performance- related options (C and D) relate to efficiency and scalability, not fairness. Therefore, systematically assessing whether the training data covers all relevant end-user groups is the most direct and effective way to evaluate and mitigate bias.
References: AI Security Management™ (AAISM) Study Guide - AI Risk Identification and Impact Assessment; Data Governance and Bias Section.


NEW QUESTION # 152
When preparing for an AI incident, which of the following should be done FIRST?

Answer: C

Explanation:
AAISM prescribes Preparation as the foundational phase of AI incident response. The first priority is to form and empower a cross-functional incident response (IR) team with AI/ML expertise (security, data science, product, legal/compliance). Only once the accountable team exists can you define playbooks, communications, containment/eradication steps, recovery processes, and escalation paths. Without a designated team, procedures and channels lack ownership and effectiveness.
References:* AI Security Management™ (AAISM) Body of Knowledge: Incident Management-Preparation; Roles & Responsibilities; Cross-functional Coordination* AAISM Study Guide: AI IR Operating Model; Stakeholder Mapping; Authority & Escalation* AAISM Mapping to Standards: Security Operations- Preparation Before Procedures (people and roles precede playbooks)


NEW QUESTION # 153
An organization is adopting an agentic AI solution from an external vendor to support its internal IT operations. To evaluate the security posture of this system, which of the following provides the MOST reliable and independently verifiable evidence of implemented security controls?

Answer: A

Explanation:
Third-party audit reports provide independent assurance that the vendor's stated controls are designed and operating effectively against recognized criteria. Such attestations (e.g., audit/assurance frameworks) are traceable, repeatable, and verifiable, and they support supply-chain risk reviews and contractual assurance.
Internal red-team reports are not independent, industry "peer reviews" are not control attestations, and whitepapers are marketing/educational materials without evidence of control operation.
References: AI Security Management™ (AAISM) Body of Knowledge: Third-Party & Supply-Chain Assurance; Independent Assessment and Audit Evidence; Vendor Risk Management. AAISM Study Guide:
Assurance Artifacts for AI Services; Evaluating External AI Providers.


NEW QUESTION # 154
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