Updated Dec-2025 Test Engine to Practice AAISM Test Questions
AAISM Real Exam Questions Test Engine Dumps Training With 257 Questions
ISACA AAISM Exam Syllabus Topics:
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NEW QUESTION # 147
An AI research team is developing a natural language processing model that relies on several open-source libraries. Which of the following is the team's BEST course of action to ensure the integrity of the software packages used?
- A. Use the latest version of all libraries from public repositories
- B. Scan the packages and libraries for malware prior to installation
- C. Retrain the model regularly to handle package and library updates
- D. Maintain a list of frequently used libraries to ensure consistent application in projects
Answer: B
Explanation:
AAISM's technical control guidance emphasizes that when using open-source libraries, the best safeguard for integrity is to scan the packages for malware before installation. This ensures that compromised or malicious code does not enter the AI system environment. Maintaining lists aids consistency but not security. Always using the latest versions may introduce unverified vulnerabilities. Retraining models addresses functionality but not software integrity. Therefore, the strongest protective measure is pre-installation malware scanning of open-source packages.
References:
AAISM Exam Content Outline - AI Technologies and Controls (Software Supply Chain Security) AI Security Management Study Guide - Open-Source Package Risk Mitigation
NEW QUESTION # 148
An organization plans to implement a new AI system. Which of the following is the MOST important factor in determining the level of risk monitoring activities required?
- A. The organization's risk appetite
- B. The organization's compensating controls
- C. The organization's number of AI system users
- D. The organization's risk tolerance
Answer: D
Explanation:
AAISM risk management guidance clarifies that the organization's risk tolerance is the most important factor in determining how much monitoring is needed. Risk tolerance specifies the amount of risk the organization is willing to accept and defines the threshold for triggering monitoring or mitigation activities. Risk appetite is broader and strategic, while tolerance sets the operational limits. The number of users may influence scale, and compensating controls may affect resilience, but neither dictates monitoring intensity as directly as risk tolerance.
References:
AAISM Study Guide - AI Risk Management (Risk Appetite vs. Tolerance)
ISACA AI Security Management - Monitoring Based on Risk Tolerance
NEW QUESTION # 149
Which of the following information is MOST important to include in a centralized AI inventory?
- A. Ownership and accountability of AI systems
- B. Foundation model and package registry
- C. AI model use cases
- D. Training data sets
Answer: A
Explanation:
AAISM governance practices identify ownership and accountability as the most critical element in any centralized AI inventory. An AI inventory provides oversight by cataloging all AI assets within an organization, and assigning responsibility ensures that each system has clear governance, monitoring, and compliance coverage. While use cases, training data, and registries are valuable metadata, they do not guarantee accountability. Without defined ownership, no party is responsible for addressing risk, bias, or incidents. Therefore, the most important information to include is ownership and accountability details for each AI system.
References:
AAISM Exam Content Outline - AI Governance and Program Management (AI Inventories and Oversight) AI Security Management Study Guide - Ownership and Accountability Structures
NEW QUESTION # 150
Which of the following is the BEST reason to immediately disable an AI system?
- A. Insufficient model training
- B. Slow model performance
- C. Excessive model drift
- D. Overly detailed model outputs
Answer: C
Explanation:
According to AAISM lifecycle management guidance, the best justification for disabling an AI system immediately is the detection of excessive model drift. Drift results in outputs that are no longer reliable, accurate, or aligned with intended purpose, creating significant risks. Performance slowness and overly detailed outputs are operational inefficiencies but not critical shutdown triggers. Insufficient training should be addressed before deployment rather than after. The trigger for immediate deactivation in production is excessive drift compromising reliability.
References:
AAISM Exam Content Outline - AI Governance and Program Management (Model Drift Management) AI Security Management Study Guide - Disabling AI Systems
NEW QUESTION # 151
Which of the following is the MOST effective defense against cyberattacks that alter input data to avoid detection by the model?
- A. Conducting periodic monitoring activities on the model's decisions
- B. Implementing restricted access to the model's internal parameters
- C. Applying differential privacy controls on training datasets
- D. Enhancing model robustness through adversarial training
Answer: D
Explanation:
Evasion attacks manipulate inputs to induce misclassification while leaving the model unchanged. AAISM prescribes adversarial robustness controls, with adversarial training as a primary measure: incorporate adversarially perturbed examples into training/validation to harden decision boundaries and improve resilience across threat models (e.g., Lp-bounded perturbations). Monitoring (A) is detective, not preventive.
Restricting parameter access (C) protects confidentiality but does not mitigate input-space attacks.
Differential privacy (D) addresses training data leakage, not robustness to adversarial inputs.
References:AI Security Management (AAISM) Body of Knowledge: Adversarial ML-Evasion vs.
Poisoning; Robustness and Resilience Controls; Adversarial Training.AAISM Study Guide: Model Hardening Techniques; Evaluation of Robust Accuracy; Security Testing with Adversarial Examples.
NEW QUESTION # 152
Which of the following MOST effectively secures ongoing stakeholder support for AI initiatives?
- A. Quantifying and communicating the value of AI solutions
- B. Addressing and optimizing AI-related risk
- C. Developing and monitoring an AI strategic roadmap
- D. Conducting periodic staff training
Answer: A
Explanation:
AAISM governance guidance emphasizes that stakeholder buy-in is sustained when the measurable value of AI initiatives is clearly communicated. Value demonstrations include:
* improved efficiency
* reduced cost
* reduced risk
* business growth
Training (B) and risk optimization (C) are important but do not guarantee stakeholder support. A roadmap (D) guides planning but does not secure buy-in.
References: AAISM Study Guide - AI Governance; Stakeholder Engagement & Value Communication.
NEW QUESTION # 153
Which of the following datasets is used to tune hyperparameters?
- A. Test
- B. Training
- C. Configuration
- D. Validation
Answer: D
Explanation:
Per AAISM's ML lifecycle controls, hyperparameter tuning is performed on the validation set, reserving the test set strictly for final, unbiased performance estimation. The training set is used to fit parameters; the validation set guides model selection and hyperparameter optimization; the test set is untouched until the end to prevent leakage and optimistic bias. "Configuration" is not a dataset type in the lifecycle split.
References:* AI Security Management™ (AAISM) Body of Knowledge: Model Development Controls- Data Splitting and Evaluation Integrity* AAISM Study Guide: Overfitting Avoidance; Validation vs. Test Separation; Leakage Prevention* AAISM Mapping to Standards: Evaluation Integrity-Hold-out Protocols and Tuning Practices
NEW QUESTION # 154
An organization is deploying an automated AI cybersecurity system. Which of the following would be the MOST effective strategy to minimize human error and improve overall security?
- A. Utilizing machine learning (ML) algorithms to ensure responsible use
- B. Implementing manual monitoring of potential alerts
- C. Using historical data to train AI detection software
- D. Conducting periodic penetration testing
Answer: C
Explanation:
Training detection models on relevant, representative historical data improves signal quality, reduces false positives, and automates triage-directly lowering human workload and error rates (e.g., alert fatigue, missed correlations). Penetration testing is valuable but episodic and does not systematically reduce day-to-day operator error. "Ensure responsible use" is a governance aim, not a concrete method to cut human error in detection. Manual monitoring increases reliance on human judgment and is prone to inconsistency.
References: AI Security Management (AAISM) Body of Knowledge: Model Development & Evaluation Controls; Data Selection and Representativeness; Operationalization to Reduce Human Error. AAISM Study Guide: Tuning Detection Systems with Historical Corpora; Alert Quality, Precision/Recall, and SOC Workflow Integration.
NEW QUESTION # 155
Which of the following will BEST reduce data bias in machine learning (ML) algorithms?
- A. Diversifying the model training data
- B. Adopting a more simplified model
- C. Utilizing unstructured data sets
- D. Securing the model training data
Answer: A
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.
References:
AAISM Study Guide - AI Risk Management (Bias and Fairness in AI)
ISACA AI Security Management - Data Diversity and Representation Controls
NEW QUESTION # 156
Which strategy is MOST effective for penetration testers assessing an AI model against membership inference attacks?
- A. Disabling model logging
- B. Generating synthetic training data
- C. Measuring accuracy on the test set
- D. Analyzing AI model confidence scores
Answer: D
Explanation:
AAISM specifies that membership inference attacks often exploit unusually high confidence scores when the model encounters data points used during training. Penetration testers identify vulnerability by analyzing model confidence behavior across known and unknown samples.
Synthetic data (A) does not test inference leakage. Disabling logs (C) removes evidence and reduces visibility. Test-set accuracy (D) is unrelated.
References: AAISM Study Guide - AI Privacy Attacks; Membership Inference Testing Techniques.
NEW QUESTION # 157
An organization is facing a deepfake attack intended to manipulate stock prices. The organization's crisis communication plan has been activated. Which of the following is MOST important to include in the initial response?
- A. Conduct a detailed forensic analysis to identify the source of the deepfake
- B. Engage with brand monitoring services to track social media activity
- C. Provide clarifying information in a pre-approved public statement
- D. Conduct employee awareness training on recognizing deepfake videos and audio
Answer: C
Explanation:
AAISM guidance on crisis management and communication emphasizes that the initial priority in responding to a reputational or market manipulation attack is to provide accurate clarifying information to the public through a pre-approved statement. This ensures stakeholders and markets are given verified facts immediately, limiting the spread of misinformation. While forensic analysis, employee training, and monitoring activities are important, they occur after the immediate need for public trust and damage control is addressed. Pre-approved statements are a central control in AI-related incident response to ensure consistency, timeliness, and credibility in communications.
References:
AAISM Study Guide - AI Governance and Program Management (Incident Response and Crisis Communication) ISACA AI Security Management - Public Communication and Trust Preservation
NEW QUESTION # 158
Which of the following is the MOST effective way to prevent a model inversion attack?
- A. Implement differential privacy during model training
- B. Utilize data pseudonymization
- C. Ensure data minimization
- D. Monitor model output for anomalies
Answer: A
Explanation:
AAISM identifies differential privacy as the primary mitigation technique against model inversion attacks, which attempt to reconstruct sensitive training data by probing model outputs.
Pseudonymization (B) and minimization (D) reduce exposure but do not prevent inversion. Output monitoring (A) detects anomalies but doesn't block reconstruction.
References: AAISM Study Guide - Privacy Attacks and Defenses; Differential Privacy.
NEW QUESTION # 159
A financial organization relies on AI-based identity verification and fraud detection services. Which of the following BEST integrates AI security risk into the business continuity plan (BCP)?
- A. Using explainable AI to document decision paths
- B. Including AI model supporting infrastructure in disaster recovery scenarios
- C. Duplicating AI microservices across multiple availability zones
- D. Periodic retraining using pre-labeled data
Answer: B
Explanation:
AAISM stresses that AI systems and their supporting infrastructure must be explicitly included in disaster recovery and continuity planning, since disruptions to models, feature stores, or pipelines can halt critical business functions.
Explainability (A) and retraining (B) are operational improvements, not continuity mechanisms. Multi-zone redundancy (D) improves availability but does not represent complete BCP integration.
References: AAISM Study Guide - AI Business Continuity and Resilience Requirements.
NEW QUESTION # 160
When preparing for an AI incident, which of the following should be done FIRST?
- A. Establish recovery processes for AI system models and datasets
- B. Implement a clear communication channel to report AI incidents
- C. Create containment and eradication procedures for AI-related incidents
- D. Establish a cross-functional incident response team with AI knowledge
Answer: D
Explanation:
AAISM prescribes Preparation as the foundational phase of AI incident response. The first priority is to form and empower a cross-functional incident response (IR) team with AI/ML expertise (security, data science, product, legal/compliance). Only once the accountable team exists can you define playbooks, communications, containment/eradication steps, recovery processes, and escalation paths. Without a designated team, procedures and channels lack ownership and effectiveness.
References:* AI Security Management™ (AAISM) Body of Knowledge: Incident Management-Preparation; Roles & Responsibilities; Cross-functional Coordination* AAISM Study Guide: AI IR Operating Model; Stakeholder Mapping; Authority & Escalation* AAISM Mapping to Standards: Security Operations- Preparation Before Procedures (people and roles precede playbooks)
NEW QUESTION # 161
Which of the following is the MOST critical success factor for an AI implementation project?
- A. Developing and using model cards
- B. Ensuring AI risk is captured in the risk register
- C. Mapping data throughout the life cycle
- D. Obtaining senior management buy-in
Answer: D
Explanation:
AAISM identifies executive sponsorship and senior management buy-in as the foremost success factor for AI initiatives. It secures resources, resolves cross-functional conflicts, sets risk appetite, and enforces adherence to governance and controls. Model cards (A), risk registers (B), and lifecycle data mapping (C) are vital practices within the program, but without top-level commitment, adoption, funding, and accountability often fail.
References: AI Security Management™ (AAISM) Body of Knowledge - AI Program Governance; Executive Sponsorship & Accountability; Strategy-to-Control Alignment for Successful AI Delivery.
NEW QUESTION # 162
Implementing which of the following would MOST effectively address bias in generative AI models?
- A. Adversarial training
- B. Data augmentation
- C. Data minimization
- D. Fairness constraints
Answer: D
Explanation:
AAISM identifies fairness constraints (e.g., constrained optimization, debiasing objectives, conditional generation controls, and post-processing calibrations) as the most direct, measurable method to mitigate disparate outcomes in generative systems. While data augmentation can help with coverage, and adversarial training improves robustness, fairness constraints explicitly target distributional fairness and outcome equity in generated content, aligning with governance and compliance goals.
References: AI Security Management™ (AAISM) Body of Knowledge - Fairness & Bias Management in Generative AI; Metrics, Constraints, and Remediation. AAISM Study Guide - Fairness Objectives, Post-hoc Debiasing, and Evaluation Protocols.
NEW QUESTION # 163
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