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NEW QUESTION # 32
Which of the following would BEST protect trade secrets related to AI technologies during their life cycle?
Answer: C
Explanation:
Restricting access to sensitive data and artifacts (e.g., training data, feature stores, model weights, prompts, system designs) using least-privilege, segregation, encryption, and monitoring is the most effective way to protect trade secrets throughout the AI lifecycle. Patents require public disclosure, trademarks protect branding (not secrets), and output watermarks help provenance/abuse deterrence but do not secure underlying proprietary know-how.
References: AI Security Management™ (AAISM) Body of Knowledge: Information Protection for AI- Access Control, Segmentation, and Secrets Management; AAISM Study Guide: Lifecycle Security of AI Artifacts and Trade-Secret Safeguards.
NEW QUESTION # 33
A global organization has experienced multiple incidents of staff copying confidential data into public chatbots and acting on the model outputs. Which of the following is MOST important to reduce short-term risk when launching an AI security awareness initiative?
Answer: D
Explanation:
AAISM prescribes targeted, role-based, scenario-driven training aligned to policy and job tasks as the highest-impact near-term intervention for human-factor AI risks. By mapping concrete "do/don't" behaviors (e.g., what data may/may not be pasted into public chatbots, required redaction steps, approved tools, verification of outputs) to specific roles, organizations rapidly reduce incident likelihood and harmful actions.
* A (blocking) is a technical containment option but is not an awareness-initiative control and may cause workarounds; AAISM treats it as complementary, not a substitute for behavior change.
* B generic modules fail to address the specific misuse pattern.
* D signatures provide attestations without ensuring comprehension or changed behavior.
References:* AI Security Management (AAISM) Body of Knowledge: Human-centric Controls-Role- based training, policy-to-practice mapping, and scenario exercises for rapid risk reduction.* AI Security Management Study Guide: Awareness program design for generative AI misuse; behavior-anchored training outcomes.
NEW QUESTION # 34
An organization deploys a large language model (LLM) to automate legal document analysis.
Which of the following is MOST critical before model deployment?
Answer: D
Explanation:
Before deployment, it is most critical to validate that the model produces accurate, reliable, and expected outputs for legal document analysis. Testing model behavior helps identify errors, inconsistencies, hallucinations, or unsafe outcomes that could create significant legal and operational risks.
NEW QUESTION # 35
An organization is implementing AI agent development across multiple engineering teams. Which of the following is the MOST important focus of AI-specific security training for developers?
Answer: B
Explanation:
For developer-facing, near-term hardening of AI agents, AAISM prioritizes secure agent design and runtime controls against prompt injection, unsafe memory/tool use, and tool-execution compromise. These are primary exploitation paths for agents that read external content, persist memory, and call tools with elevated privileges. Training must center on: guarding tool invocation, constraining memory scope, sanitizing
/validating inputs, and isolating high-risk actions. Topics like bias/fairness (B) and policy/hallucinations (C) are important but are governance/assurance concerns; API abuse and plug-in risk (D) matter, yet the core, developer-controlled attack surface for agents is injection and unsafe tool/memory design.
References:* AI Security Management (AAISM) Body of Knowledge: Agent Security-prompt injection defenses, tool execution constraints, memory governance* AAISM Study Guide: Developer controls for agent frameworks; input validation, sandboxing, scoped permissions, guardrail patterns
NEW QUESTION # 36
Which of the following will BEST reduce data bias in machine learning (ML) algorithms?
Answer: B
Explanation:
AAISM guidance clearly states that the most effective way to mitigate data bias is through diverse training data that fairly represents all relevant populations, scenarios, and contexts. Simplified models may reduce complexity but do not remove bias. Unstructured data sets may introduce new errors without addressing fairness. Securing training data protects confidentiality and integrity but does not resolve representational imbalance. Therefore, the best practice for reducing bias in ML is diversification of training datasets.
NEW QUESTION # 37
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