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ISACA AAISM Exam Overview:

Certification Vendor:ISACA
Exam Name:ISACA Advanced in AI Security Management (AAISM) Exam
Exam Number:AAISM
Exam Price:US$459 (members), US$599 (non-members)
Exam Duration:150 minutes
Passing Score:450 (out of 800)
Related Certifications:CISM
CISSP
Available Languages:English
Real Exam Qty:90
Certificate Validity Period:3 years
Exam Format:Scenario-based, Multiple-choice
Recommended Training:ISACA Official AAISM Training
Exam Registration:ISACA AAISM Registration
Sample Questions:ISACA AAISM Sample Questions
Exam Way:Computer-based; available at authorized PSI testing centers or via remote proctoring
Pre Condition:Hold an active CISM or CISSP certification
Official Syllabus URL:https://www.isaca.org/credentialing/aaism/aaism-exam-content-outline

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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.

ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q411-Q416):

NEW QUESTION # 411
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 requires formal model change governance: documented justification, risk assessment, validation
/verification (V&V), approvals, and post-deployment monitoring when altering features, thresholds, or signals. Disabling risk indicators to reduce false positives without rigorous validation and controlled rollout reflects a failure in model validation and change control, which AAISM treats as a core safeguard against unintended harms and regulatory breaches.
References: AI Security Management™ (AAISM) Body of Knowledge - Model Risk Governance; Change Management & Approvals; Validation/Verification Requirements. AAISM Study Guide - Control Gates for Feature/Threshold Changes; Post-Change Monitoring and Backout Criteria.


NEW QUESTION # 412
Which of the following controls would BEST help to prevent data poisoning in AI models?

Answer: C

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.


NEW QUESTION # 413
When robust input controls cannot prevent prompt injections in an LLM, what is the BEST compensating control?

Answer: A

Explanation:
AAISM identifies output review and annotation as the most practical compensating control when robust input validation cannot be applied.
Output moderation detects:
* maliciously influenced responses
* unsafe outputs
* security-policy violations
IAM (B) does not mitigate prompt injection itself. Human review of inputs (C) is unrealistic at scale. Fine- tuning (A) cannot guarantee full prevention.
References: AAISM Study Guide - Generative AI Safeguards; Output Moderation Controls.


NEW QUESTION # 414
Which of the following is the PRIMARY purpose of a dedicated AI system policy?

Answer: A

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 # 415
Which of the following types of data is used to tune hyperparameters?

Answer: D

Explanation:
According to AAISM, hyperparameter tuning uses validation data, not training or test data.
Validation datasets are specifically designed to evaluate different hyperparameter configurations without contaminating the training or testing sets.


NEW QUESTION # 416
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