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ISACA AAISM Exam Syllabus Topics:

Section	Weight	Objectives
AI Governance and Program Management	31%	- AI security program development and management - AI governance frameworks and alignment with business objectives - AI security policies, standards, and procedures - AI asset and data lifecycle management - Business continuity and incident response for AI systems
AI Risk Management	31%	- AI threat and vulnerability management - AI vendor and supply chain risk management - Regulatory compliance and ethical considerations in AI - AI risk assessment, thresholds, and treatment strategies
AI Technologies and Controls	38%	- AI security architecture and secure design principles - Privacy, ethics, trust, and safety controls - Data management and protection controls for AI - Security monitoring, testing, and continuous assurance for AI systems - AI lifecycle security: model selection, training, validation, and deployment

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최신 ISACA AAISM Certification AAISM 무료샘플문제 (Q101-Q106):

질문 # 101
Which of the following BEST describes how supervised learning models help reduce false positives in cybersecurity threat detection?

A. They use real-time feature engineering to automatically adjust decision boundaries
B. They analyze patterns in data to group legitimate activity from actual threats
C. They dynamically generate new labeled data sets
D. They learn from historical labeled data

정답：D

설명：
According to AAISM technical content, supervised learning models reduce false positives by learning from historical labeled data that distinguishes between legitimate activity and actual threats. This training enables the model to recognize patterns and improve its discrimination ability over time. Grouping patterns (A) describes clustering, an unsupervised method. Real-time feature engineering (B) and generating new labeled data (D) are advanced techniques but not the fundamental supervised learning approach. The essence of supervised learning is leveraging labeled data to minimize misclassification, including false positives.
References:
AAISM Exam Content Outline - AI Technologies and Controls (Machine Learning Approaches) AI Security Management Study Guide - Supervised Learning for Threat Detection

질문 # 102
How can an organization best remain compliant when decommissioning an AI system that recorded patient data?

A. Ensure backups are tested and access controls are audited
B. Ensure a certificate of destruction is received and archived
C. Update governance policies based on lessons learned
D. Perform a post-destruction risk assessment

정답：B

설명：
AAISM and healthcare privacy regulations (HIPAA-like guidance within AAISM contexts) stress that documented destruction of sensitive data is required when decommissioning systems.
A certificate of destruction ensures:
- proof of lawful data disposal
- auditability
- regulatory compliance
- defensibility during inspections

질문 # 103
An organization is designing an AI-based credit risk assessment system that will integrate with sensitive financial datasets. Which of the following would BEST support the implementation of security-by-design principles in the AI system's architecture?

A. Restricting access to AI models using IP allow lists to reduce public exposure
B. Segmenting AI services across containers to manage resource constraints
C. Applying threat modeling specific to AI components before deployment
D. Integrating differential privacy mechanisms into model training to limit data leakage

정답：C

설명：
Security by design in AI requires establishing risk-informed requirements at the earliest stages of the lifecycle and systematically translating them into architectural controls. Conducting AI-specific threat modeling before deployment is the highest-leverage action because it identifies assets (data, models, pipelines), trust boundaries (feature stores, training/inference services), threat events (poisoning, evasion, model extraction), and attack paths unique to ML systems. The outputs (abuse/misuse cases, control objectives, verification plans) then drive selection and prioritization of controls such as privacy-enhancing techniques, access controls, isolation, monitoring, and assurance testing. While differential privacy (C) is a strong control for leakage risk, it is one control choice among many and should be selected as a result of threat modeling. IP allow lists (B) and container segmentation (A) are valuable hardening measures but are narrower and do not replace the lifecycle-wide governance and design traceability that threat modeling enables.
References: AI Security Management (AAISM) Body of Knowledge - Secure AI SDLC; AI Threat Modeling and Abuse Case Development; Architecture & Control Selection; Risk-Based Design Assurance.
AAISM Study Guide - Security-by-Design for AI; Model/System Asset Mapping; Control Objectives from Threat Models.

질문 # 104
Which of the following is the BEST control for preventing deepfakes?

A. AI governance policies
B. Regular AI risk assessment
C. System input validation
D. Output provenance verification

정답：D

설명：
Output provenance verification (e.g., robust watermarking, cryptographic signing, content credentials, and chain-of-custody attestations) is the primary preventive and detective control to combat deepfakes at scale. It enables receivers and downstream systems to verify that media originates from trusted sources and has not been tampered with. While risk assessments (Option B) and governance policies (Option C) set expectations, they do not technically prevent forged media. Input validation (Option D) does not address media authenticity once generated or received.
References:
AAISM Body of Knowledge: Content Authenticity, Watermarking, and Provenance; Trustworthy AI Outputs and Media Integrity Controls.
AAISM Study Guide: Mitigations for Synthetic Media Risks; Watermark/Signature Verification Pipelines; Content Credentials in Enterprise Controls.

질문 # 105
An information security manager has implemented key performance indicators (KPIs) and key risk indicators (KRIs) to oversee the security posture of an AI-based fraud detection system.
Which of the following would be of GREATEST concern?

A. Indicators also assess network traffic unrelated to AI functions.
B. Indicators primarily focus on quantitative fraud and threat data.
C. Indicators do not include comparisons against industry competitors.
D. Indicators fail to capture adversarial techniques targeting AI components.

정답：D

설명：
If KPIs and KRIs do not account for adversarial techniques targeting AI components, critical AI- specific threats such as evasion, poisoning, or model manipulation may go undetected, leaving significant gaps in the security posture.

질문 # 106
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