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ISACA AAISM Exam Syllabus Topics:

Topic	Details
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 (Q402-Q407):

NEW QUESTION # 402
Which of the following is the BEST way to reduce the risk of misuse of an AI agent that has access to critical data and systems?

A. Validate agent compliance with output restrictions
B. Limit human review of AI decisions
C. Allow users to configure the agent for productivity
D. Prohibit users from manipulating agent behavior

Answer: A

Explanation:
AAISM frameworks highlight output-based controls, including output filtering, restriction validation, and policy-aligned guardrails as primary defenses for AI agents with high privileges. Ensuring the agent does not output unauthorized instructions or sensitive data directly mitigates misuse.
Allowing user configuration (B) increases risk. Prohibiting manipulation entirely (C) is impractical. Reducing human oversight (D) increases system abuse potential.
References: AAISM Study Guide - AI Agents, Output Controls, and Guardrails.

NEW QUESTION # 403
Which of the following is the MOST effective strategy for penetration testers assessing the security of an AI model against membership inference attacks?

A. Analyzing AI model confidence scores to indicate training data
B. Disabling AI model logging to reduce noise during testing
C. Generating synthetic data to replace the training data
D. Measuring AI model accuracy on the test set

Answer: A

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 # 404
Which of the following BEST reduces the risk of exposing sensitive data through the output of large language models (LLMs) in applications?

A. Conducting adversarial testing
B. Encrypting data in transit and at rest
C. Implementing data sanitization techniques
D. Enforcing least privilege access

Answer: C

Explanation:
AAISM materials make clear that the best safeguard against sensitive information being leaked through the outputs of LLMs is data sanitization. This involves filtering, redacting, or masking sensitive content before the model can use it, thereby preventing unintended disclosure in outputs. Encryption protects confidentiality in storage and transmission but does not stop output leaks. Adversarial testing helps identify vulnerabilities but does not prevent exposure by itself.
Least privilege access restricts who can interact with the model but does not sanitize the content of its outputs. The control most directly tied to preventing leakage is implementing data sanitization techniques.

NEW QUESTION # 405
Which of the following is the BEST way to ensure an organization remains compliant with industry regulations when decommissioning an AI system used to record patient data?

A. Perform a post-destruction risk assessment to verify that there is no residual exposure of data
B. Ensure backups are tested and access controls are recorded and audited to ensure compliance
C. Update governance policies based on lessons learned and ensure a feedback loop exists
D. Ensure the certificate of destruction is received and archived in line with data retention policies

Answer: D

Explanation:
For regulated data such as patient information, AAISM requires provable data lifecycle closure at decommissioning. The authoritative evidence is a certificate of destruction (covering primary, replicas, backups, and caches) retained per the organization's records retention policy.

NEW QUESTION # 406
Which of the following would BEST ensure a proper business continuity plan (BCP) is in place for an AI solution?

A. Enhancing monitoring and detection of model failures and anomalies
B. Testing the AI infrastructure failover mechanisms
C. Implementing access controls to protect the AI system from unauthorized use
D. Increasing the detail of AI solution backup and restoration processes

Answer: B

Explanation:
Effective AI BCP requires validation through exercises and controlled failover tests to prove recovery objectives can be met in practice. Merely documenting backups (Option D), hardening access (Option B), or improving monitoring (Option A) does not confirm that the AI stack-data pipelines, feature stores, model registries, inference services, and dependent infrastructure-can actually fail over and recover within RTO
/RPO. AAISM prescribes periodic BCP/DR testing (including model artifact restoration, configuration reconstitution, dependency failover, and data pipeline continuity) to verify readiness and identify gaps before real incidents.
References:AI Security Management (AAISM) Body of Knowledge: Business Continuity & Disaster Recovery for AI; Validation and Exercising of Continuity Plans; RTO/RPO for Models, Data, and Pipelines.
AAISM Study Guide: Operational Resilience for AI Systems; BCP/DR Test Scenarios (model registry, feature store, pipeline recovery); Continuity Metrics and Evidence of Readiness.

NEW QUESTION # 407
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