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AAISM Tesking Torrent - AAISM Pdf Questions & AAISM Practice Training

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q126-Q131):

NEW QUESTION # 126
An attack has occurred on an AI system that has been in use for two years. Which of the following would BEST mitigate the impact of the attack?

A. Monitoring AI systems for suspicious activities
B. Updating deployed training data with new adversarial data
C. Implementing strict access controls to the model's architecture
D. Replacing the AI model with a new model that hides confidence levels

Answer: B

Explanation:
When an AI system experiences an attack after being in production for an extended period, the most effective mitigation strategy is to update the deployed training data with new adversarial data. This process strengthens the model's resilience by retraining it to recognize and resist attack vectors that were previously unknown or unaccounted for. According to the AI Security Management (AAISM) framework, risk mitigation for AI systems must address model robustness through adversarial retraining, data quality improvement, and model lifecycle hardening rather than relying solely on reactive measures.
Why Option B is Correct:
* Incorporating adversarial examples into the training set enhances the system's ability to correctly classify and withstand malicious inputs.
* This approach directly mitigates the vulnerability exploited in the attack and supports a proactive, continuous risk management cycle.
Why Other Options Are Incorrect:
* Option A: Monitoring helps detect suspicious activity but does not resolve the underlying vulnerability.
* Option C: Concealing confidence scores may reduce model transparency but does not address the attack mechanism or its root cause.
* Option D: Implementing access controls protects the model's architecture but does not improve model robustness against input manipulation attacks.
Exact Extract from Official AAISM Study Guide:
"AI risk management requires continuous improvement following incidents. After an adversarial or data poisoning event, the preferred risk treatment involves retraining the model using adversarial data and updated datasets to enhance robustness. This ensures the AI model adapts to evolving threat landscapes rather than merely restricting access or obscuring outputs." References:
AI Security Management (AAISM) Body of Knowledge: AI Risk Treatment and Mitigation Strategies, Adversarial Robustness and Resilience Engineering.
AI Security Management Study Guide: Model Lifecycle Security, Continuous Risk Treatment through Adversarial Retraining.
ISO/IEC 23894:2023, Clause 8.3.2 - Risk treatment through robustness improvement and adversarial data inclusion.

NEW QUESTION # 127
A post-incident investigation finds that an AI-powered anti-money laundering system inadvertently allowed suspicious transactions because certain risk signals were disabled to reduce false positives. Which of the following governance failures does this BEST demonstrate?

A. Absence of metrics and dashboard for analysts
B. Excessive reliance on external consultants for model design
C. Insufficient model validation and change control processes
D. Lack of sufficient computing resources for the AI system

Answer: C

Explanation:
AAISM requires formal model change governance: documented justification, risk assessment, validation
/verification (V&V), approvals, and post-deployment monitoring when altering features, thresholds, or signals. Disabling risk indicators to reduce false positives without rigorous validation and controlled rollout reflects a failure in model validation and change control, which AAISM treats as a core safeguard against unintended harms and regulatory breaches.
References: AI Security Management (AAISM) Body of Knowledge - Model Risk Governance; Change Management & Approvals; Validation/Verification Requirements. AAISM Study Guide - Control Gates for Feature/Threshold Changes; Post-Change Monitoring and Backout Criteria.

NEW QUESTION # 128
Which of the following strategies BEST ensures generative AI tools do not expose company data?

A. Ensuring AI tools are compliant with local regulations
B. Conducting an independent AI data audit
C. Testing AI tools before implementation
D. Implementing a solution to prohibit the input of sensitive data

Answer: D

Explanation:
AAISM prioritizes preventive controls at the point of use for generative AI, specifically input-governance and DLP controls that block or redact confidential, regulated, or high-risk data before it can be sent to external models. Audits, pre-deployment tests, and regulatory conformance are necessary but do not themselves prevent an employee from pasting sensitive content into prompts. Enforcing input restrictions, pattern-based redaction, policy-aware controls, and allow-lists for approved contexts provides the highest assurance of preventing exposure.
References:* AI Security Management (AAISM) Body of Knowledge: Data loss prevention for AI; prompt
/input controls; approved channels and guardrails for generative AI.* AI Security Management Study Guide: Preventive over detective controls for confidentiality; enterprise guardrails at prompt capture and egress points.

NEW QUESTION # 129
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?

A. Third-party audit reports
B. Industry benchmarking peer review
C. General AI security whitepapers
D. Internal red team testing reports

Answer: A

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 # 130
Who is responsible for implementing recommendations in a final report after an external AI compliance audit?

A. System architects
B. Internal auditors
C. End users
D. Model owners

Answer: D

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 # 131
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Jim White Jim White

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