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| Section | Weight | Objectives |
|---|---|---|
| AI Risk Management | 31% | - AI vendor and supply chain risk management - Regulatory compliance and ethical considerations in AI - AI threat and vulnerability management - AI risk assessment, thresholds, and treatment strategies |
| AI Technologies and Controls | 38% | - AI lifecycle security: model selection, training, validation, and deployment - Data management and protection controls for AI - AI security architecture and secure design principles - Privacy, ethics, trust, and safety controls - Security monitoring, testing, and continuous assurance for AI systems |
| AI Governance and Program Management | 31% | - AI governance frameworks and alignment with business objectives - AI asset and data lifecycle management - Business continuity and incident response for AI systems - AI security program development and management - AI security policies, standards, and procedures |
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NEW QUESTION # 369
When robust input controls cannot prevent prompt injections in an LLM, what is the BEST compensating control?
Answer: C
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 # 370
Within which stage of the AI development life cycle should effective feature engineering be conducted?
Answer: D
NEW QUESTION # 371
Which of the following is the MOST effective way to prevent a model inversion attack?
Answer: A
Explanation:
AAISM identifies differential privacy as the primary mitigation technique against model inversion attacks, which attempt to reconstruct sensitive training data by probing model outputs.
Pseudonymization (B) and minimization (D) reduce exposure but do not prevent inversion. Output monitoring (A) detects anomalies but doesn't block reconstruction.
References: AAISM Study Guide - Privacy Attacks and Defenses; Differential Privacy.
NEW QUESTION # 372
The extent of stakeholder engagement in generative AI policy development PRIMARILY depends on the:
Answer: B
Explanation:
The level of stakeholder engagement should be driven by how significantly AI use can affect the organization, including risks, ethical considerations, and business outcomes. Greater potential impact requires broader and deeper stakeholder involvement to ensure appropriate governance and risk management.
NEW QUESTION # 373
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: B
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 # 374
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