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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: 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 |
| Topic 2: AI Governance and Program Management | 31% | - AI security policies, standards, and procedures - AI security program development and management - AI asset and data lifecycle management - AI governance frameworks and alignment with business objectives - Business continuity and incident response for AI systems |
| Topic 3: AI Technologies and Controls | 38% | - Data management and protection controls for AI - AI lifecycle security: model selection, training, validation, and deployment - Security monitoring, testing, and continuous assurance for AI systems - AI security architecture and secure design principles - Privacy, ethics, trust, and safety controls |
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NEW QUESTION # 140
An audit reveals that a third-party AI vendor of a financial organization does not provide documentation on model training and testing. Which of the following is the MOST critical risk for the organization?
Answer: B
Explanation:
Without documentation on how the model was trained and tested, the organization cannot demonstrate transparency, explainability, or proper validation. This creates significant regulatory compliance risk, especially in financial services where decisions must be explainable and auditable.
NEW QUESTION # 141
A financial organization is concerned about the risk of prompt injection attacks on its customer service chatbot. Which of the following controls BEST addresses this concern?
Answer: B
Explanation:
AAISM emphasizes preventive technical controls for LLM threats such as prompt injection, including input validation/sanitization, instruction isolation, allow/deny lists, context segmentation, and output filtering.
These reduce the model's exposure to adversarial instructions embedded in user prompts or retrieved context.
Monitoring (A) is detective, not preventive; increasing parameters (B) does not inherently improve security against injection; human-in-the-loop (D) is valuable for high-risk decisions but does not directly neutralize injection vectors at the control boundary the way input validation and content filtering do.
References: AI Security Management (AAISM) Body of Knowledge - Technical Controls for LLM Security; Input/Output Filtering and Context Isolation; Secure Inference and Prompt Injection Mitigations.
NEW QUESTION # 142
Who is responsible for implementing recommendations in a final report after an external AI compliance audit?
Answer: A
Explanation:
Under AAISM governance, management and control owners are accountable for remediation. For AI systems, the accountable role is the Model Owner (or equivalent business/service owner), who coordinates with architects, engineers, and operations to implement corrective actions and report closure. Internal auditors provide independent assurance and do not implement fixes; end users are not remediation owners.
References: AI Security Management (AAISM) Body of Knowledge - Roles & Accountability (Model Owner, Control Owner, Assurance); Audit Findings Management and Remediation Governance.
NEW QUESTION # 143
An organization plans to leverage AI in the software development process to speed up coding.
Which of the following should the information security manager do FIRST?
Answer: D
Explanation:
AAISM guidance specifies that before introducing AI into any business or technical workflow, an AI Impact Assessment must be conducted early to determine potential risks, privacy implications, misuse scenarios, governance gaps, and required security controls. This aligns with the principle that AI adoption must begin with governance and risk identification, not training or policy modification.
NEW QUESTION # 144
Which proactive control BEST mitigates the risk of memory poisoning in agentic AI systems?
Answer: B
Explanation:
Enforcing validation before persistence helps prevent malicious or manipulated information from being stored in the agent's memory. This proactive control ensures that only trusted and verified data is retained, directly mitigating the risk of memory poisoning attacks.
NEW QUESTION # 145
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