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| Section | Weight | Objectives |
|---|---|---|
| AI Risk Management | 31% | - AI risk assessment, thresholds, and treatment strategies - AI vendor and supply chain risk management - Regulatory compliance and ethical considerations in AI - AI threat and vulnerability management |
| AI Technologies and Controls | 38% | - AI security architecture and secure design principles - Security monitoring, testing, and continuous assurance for AI systems - AI lifecycle security: model selection, training, validation, and deployment - Privacy, ethics, trust, and safety controls - Data management and protection controls for AI |
| AI Governance and Program Management | 31% | - AI governance frameworks and alignment with business objectives - AI security program development and management - AI security policies, standards, and procedures - Business continuity and incident response for AI systems - AI asset and data lifecycle management |
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NEW QUESTION # 235
Which of the following would BEST protect an AI system against an evasion attack?
Answer: D
Explanation:
Adversarial training exposes the model to manipulated or adversarial inputs during training, enabling it to recognize and resist such inputs during operation, which directly mitigates evasion attacks.
NEW QUESTION # 236
An organization has discovered that employees have started regularly utilizing open-source generative AI without formal guidance. Which of the following should be the CISO's GREATEST concern?
Answer: A
Explanation:
The greatest immediate risk from unsanctioned use of public or open-source generative AI tools is data leakage-employees may paste confidential or regulated information into third-party systems, resulting in loss of confidentiality, regulatory exposure, and loss of intellectual property. AAISM emphasizes that when AI use occurs outside approved channels, the top control priority is preventing exfiltration of sensitive data via prompts, attachments, and context sharing. Monitoring and policy are necessary enablers, but leakage is the highest-impact failure mode in the short term; hallucinations primarily affect accuracy, not confidentiality.
References:* AI Security Management™ (AAISM) Body of Knowledge: Generative AI governance; human- in-the-loop risks; data loss and exfiltration vectors in prompts; sanctioned vs. unsanctioned AI usage.* AI Security Management™ Study Guide: Immediate risk triage for shadow AI; DLP and input-control safeguards; confidentiality-first posture for generative AI adoption.
NEW QUESTION # 237
An AI-powered drone surveillance system is deployed at a construction site. Which of the following is the MOST appropriate control to detect adversarial threats in the visual recognition model?
Answer: B
Explanation:
Adversarial threats in visual recognition models involve manipulated inputs designed to deceive the model. Testing the model's robustness against image manipulation directly evaluates its ability to detect and resist such adversarial inputs, making it the most appropriate control.
NEW QUESTION # 238
The PRIMARY AI governance risk of inconsistently applying data augmentation and cleansing across datasets is that the model may:
Answer: A
Explanation:
Inconsistent data augmentation and cleansing can introduce or amplify bias within datasets, resulting in unreliable or unfair model behavior. This undermines both data integrity and model integrity, creating significant governance, ethical, and operational risks.
NEW QUESTION # 239
A health services organization is developing a proprietary generative AI chatbot to assist patients with medical devices. Which of the following should be the organization's HIGHEST priority?
Answer: B
Explanation:
AAISM prioritizes training data suitability-lawful sourcing, provenance, quality, representativeness, and safety-especially in health-related applications. The correctness and appropriateness of training data determine clinical safety, reduction of harmful outputs, and compliance with data protection/sector obligations. Larger models or more data do not compensate for inappropriate or low-quality datasets; tuning is secondary to ensuring the right data with rigorous curation, labeling quality, and guardrails aligned to patient safety requirements.
References:* AI Security Management (AAISM) Body of Knowledge: Data Governance & Quality; High- Risk/Health Context Controls; Safety & Harm Minimization* AAISM Study Guide: Data Provenance & Suitability, Domain-Specific Dataset Controls; Compliance-by-Design for Sensitive Sectors
NEW QUESTION # 240
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