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ISACA AAISM Valid Exam Answers & AAISM Examcollection Dumps Torrent

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ISACA AAISM Exam Syllabus Topics:

Topic
Details

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 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 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.

>> ISACA AAISM Valid Exam Answers <<

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q221-Q226):

NEW QUESTION # 221
An organization is implementing an AI-based credit assessment engine using internal and third-party customer data. Which of the following BEST aligns with data management controls for the AI life cycle?

A. Limitation of model training to structured data from vetted sources to minimize ingestion risk
B. Documented procedures for data sourcing, lineage tracking, and quality validation
C. Encrypted isolation and dynamic access controls on training data pipelines
D. Use of hashed identifiers to anonymize datasets used for model validation and internal analytics

Answer: B

Explanation:
The strongest alignment with AI life-cycle data management controls is establishing documented procedures for data sourcing, lineage tracking, and quality validation. Governance requires end-to-end data controls- provenance, consent, sourcing approvals, lineage, quality checks, labeling standards, retention, and auditability-so models are trained and validated on trustworthy, compliant data. Hashing alone is pseudo- anonymization and does not deliver full governance. Restricting to structured data is an implementation constraint, not a control set. Encryption and access control protect confidentiality/availability but do not ensure lineage or quality.
References: AI Security Management (AAISM) Body of Knowledge: Data Governance & Stewardship; AI Data Lifecycle Controls; Data Provenance, Lineage, and Quality Assurance. AAISM Study Guide: Data Intake Procedures; Lineage and Audit Evidence; Quality Gates and Approval Workflows.

NEW QUESTION # 222
Which of the following is the GREATEST concern when a vendor enables generative AI features for an organization's critical system?

A. Security monitoring and alerting
B. Bias and ethical practices
C. Proposed regulatory enhancements
D. Access to the model

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 # 223
A model producing contradictory outputs based on highly similar inputs MOST likely indicates the presence of:

A. Poisoning attacks
B. Model exfiltration
C. Evasion attacks
D. Membership inference

Answer: C

Explanation:
The AAISM study framework describes evasion attacks as attempts to manipulate or probe a trained model during inference by using crafted inputs that appear normal but cause the system to generate inconsistent or erroneous outputs. Contradictory results from nearly identical queries are a typical symptom of evasion, as the attacker is probing decision boundaries to find weaknesses. Poisoning attacks occur during training, not inference, while membership inference relates to exposing whether data was part of the training set, and model exfiltration involves extracting proprietary parameters or architecture. The clearest indication of contradictory outputs from similar queries therefore aligns directly with the definition of evasion attacks in AAISM materials.
References:
AAISM Study Guide - AI Technologies and Controls (Adversarial Machine Learning and Attack Types) ISACA AI Security Management - Inference-time Attack Scenarios

NEW QUESTION # 224
When robust input controls cannot prevent prompt injections in an LLM, what is the BEST compensating control?

A. Review and annotate the AI system's outputs
B. Conduct human reviews of AI system inputs
C. Fine-tune the system to validate inputs
D. Implement identity and access management (IAM)

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 # 225
Which of the following BEST ensures AI components are validated during disaster recovery testing?

A. Disconnecting model training clusters to test retraining workflows
B. Monitoring model performance during failover and recovery
C. Running simulated data-loss scenarios by deleting test feature-store records
D. Simulating DoS attacks on AI APIs

Answer: B

Explanation:
AAISM states that AI disaster recovery testing must validate that models behave correctly during failover.
The only option that tests actual operational continuity of AI components is:
# monitoring model performance during failover
This validates stability, functionality, and resilience under disaster conditions.
Options A, B, and C test isolated scenarios but do not validate end-to-end AI operational continuity.
References: AAISM Study Guide - AI Resilience & Disaster Recovery Testing.

NEW QUESTION # 226
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