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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Technologies and Controls | 38% | - AI lifecycle security: model selection, training, validation, and deployment - Privacy, ethics, trust, and safety controls - AI security architecture and secure design principles - Data management and protection controls for AI - Security monitoring, testing, and continuous assurance for AI systems |
| Topic 2: AI Governance and Program Management | 31% | - Business continuity and incident response for AI systems - AI asset and data lifecycle management - AI security policies, standards, and procedures - AI security program development and management - AI governance frameworks and alignment with business objectives |
| Topic 3: 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 |
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NEW QUESTION # 158
A financial organization is concerned about AI data poisoning. Which control BEST mitigates this risk?
Answer: D
Explanation:
AAISM outlines that diversifying training data sources reduces the likelihood and impact of poisoning because:
* attack samples become harder to inject
* anomalies are easier to detect
* corrupted data has reduced influence on model behavior
Break-glass procedures (A) relate to incident response, not mitigation. Customer transparency (B) does not stop poisoning. Awareness training (D) is insufficient alone.
References: AAISM Study Guide - Data Poisoning Mitigation; Data Diversity and Source Validation.
NEW QUESTION # 159
Which of the following metrics BEST evaluates the ability of a model to correctly identify all true positive instances?
Answer: B
Explanation:
AAISM technical coverage identifies recall as the metric that specifically measures a model's ability to capture all true positive cases out of the total actual positives. A high recall means the system minimizes false negatives, ensuring that relevant instances are not overlooked. Precision instead measures correctness among predicted positives, specificity focuses on true negatives, and the F1 score balances precision and recall but does not by itself indicate the completeness of capturing positives. The official study guide defines recall as the most direct metric for evaluating how well a model identifies all relevant positive cases, making it the correct answer.
NEW QUESTION # 160
A model producing contradictory outputs based on highly similar inputs MOST likely indicates the presence of:
Answer: B
Explanation:
The AAISM study framework describes evasion attacks as attempts to manipulate or probe a trained model during inference by using crafted inputs that appear normal but cause the system to generate inconsistent or erroneous outputs. Contradictory results from nearly identical queries are a typical symptom of evasion, as the attacker is probing decision boundaries to find weaknesses. Poisoning attacks occur during training, not inference, while membership inference relates to exposing whether data was part of the training set, and model exfiltration involves extracting proprietary parameters or architecture. The clearest indication of contradictory outputs from similar queries therefore aligns directly with the definition of evasion attacks in AAISM materials.
NEW QUESTION # 161
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: D
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 # 162
When integrating AI for innovation, which of the following can BEST help an organization manage security risk?
Answer: B
Explanation:
AAISM emphasizes that when introducing innovative AI systems, organizations reduce security and compliance risk by following a phased adoption approach. This allows incremental deployment, controlled testing, and gradual scaling while monitoring risks in real time. Re- evaluating risk appetite and evaluating compliance are important governance steps but do not directly mitigate risks during implementation. Seeking third-party advice can add expertise but does not provide the structured control that phased integration offers. The most effective risk management approach for AI innovation is to adopt a phased rollout strategy.
NEW QUESTION # 163
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