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NEW QUESTION # 34
Which of the following recommendations would BEST help a service provider mitigate the risk of lawsuits arising from generative AI's access to and use of internet data?
- A. Disclose service provider policies to declare compliance with regulations
- B. Appoint a data steward specialized in AI to strengthen security governance
- C. Activate filtering logic to exclude intellectual property flags
- D. Review log information that records how data was collected
Answer: C
Explanation:
The AAISM materials highlight that one of the primary legal risks with generative AI systems is the unauthorized use of copyrighted or intellectual property-protected data drawn from internet sources. To mitigate lawsuits, the most effective recommendation is to implement filtering logic that actively excludes data flagged for intellectual property risks before ingestion or generation. While disclosing compliance policies, appointing governance roles, or reviewing logs are supportive measures, they do not directly prevent the core liability of using restricted content. The study guide explicitly emphasizes that proactive filtering and data governance controls are the most effective safeguards against legal disputes concerning content origin.
References:
AAISM Exam Content Outline - AI Risk Management (Legal and Intellectual Property Risks) AI Security Management Study Guide - Generative AI Data Governance
NEW QUESTION # 35
Personal data used to train AI systems can BEST be protected by:
- A. Ensuring the quality of personal data
- B. Anonymizing personal data
- C. Hashing personal data
- D. Erasing personal data after training
Answer: B
Explanation:
AAISM guidance on privacy-preserving AI highlights anonymization as the most effective means of protecting personal data used in training. By irreversibly removing or masking identifiable attributes, anonymization ensures that training data cannot be linked back to individuals, thereby meeting key privacy obligations under laws such as GDPR. Erasing data after training may limit exposure but does not protect it during the training process. Ensuring data quality improves accuracy but does not mitigate privacy risk.
Hashing protects data integrity but does not guarantee anonymity, as hashes can sometimes be reversed or correlated. Therefore, anonymization is the recommended control for protecting personal data in AI training.
References:
AAISM Study Guide - AI Technologies and Controls (Privacy-Preserving Methods) ISACA AI Security Management - Data Anonymization Practices
NEW QUESTION # 36
Which area of intellectual property law presents the GREATEST challenge in determining copyright protection for AI-generated content?
- A. Protecting trade secrets in AI technologies
- B. Determining the rightful ownership of AI-generated creations
- C. Enforcing trademark rights associated with AI systems
- D. Establishing licensing frameworks for AI-generated works
Answer: B
Explanation:
AAISM governance content highlights that the greatest intellectual property challenge in the context of AI- generated works is determining rightful ownership. Traditional copyright law requires human authorship, but AI-generated creations blur authorship and ownership boundaries, raising legal uncertainty about who can claim rights. Trademark enforcement, trade secret protection, and licensing frameworks are established areas of IP law but do not present the same fundamental challenge as ownership attribution. For AI-generated content, the central legal dilemma is ownership of the creation.
References:
AAISM Study Guide - AI Governance and Program Management (Intellectual Property and AI) ISACA AI Security Management - Copyright and Ownership Challenges
NEW QUESTION # 37
During the creation of a new large language model (LLM), an organization procured training data from multiple sources. Which of the following is MOST likely to address the CISO's security and privacy concerns?
- A. Data minimization
- B. Data discovery
- C. Data classification
- D. Data augmentation
Answer: A
Explanation:
AAISM guidance highlights data minimization as a critical practice for addressing both security and privacy concerns. By ensuring that only the minimum necessary data is collected and retained, the organization reduces the risk of sensitive information being exposed or misused during training. Data augmentation expands data but does not mitigate privacy risk. Classification organizes data but does not limit exposure.
Data discovery helps locate sources but does not directly reduce risks. The control that directly aligns with privacy-by-design principles is data minimization.
References:
AAISM Exam Content Outline - AI Risk Management (Data Privacy and Minimization) AI Security Management Study Guide - Privacy Safeguards in AI Training
NEW QUESTION # 38
Which of the following information is MOST important to include in a centralized AI inventory?
- A. AI model use cases
- B. Foundation model and package registry
- C. Ownership and accountability of AI systems
- D. Training data sets
Answer: C
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 # 39
Which of the following technologies can be used to manage deepfake risk?
- A. Adaptive authentication
- B. Systematic data tagging
- C. Multi-factor authentication (MFA)
- D. Blockchain
Answer: D
Explanation:
The AAISM study material highlights blockchain as a control mechanism for managing deepfake risk because it provides immutable verification of digital media provenance. By anchoring original data signatures on a blockchain, organizations can verify authenticity and detect tampered or synthetic content. Data tagging helps organize but does not guarantee authenticity. MFA and adaptive authentication strengthen identity security but do not address content manipulation risks. Blockchain's immutability and traceability make it the recognized technology for mitigating deepfake challenges.
References:
AAISM Study Guide - AI Technologies and Controls (Emerging Controls for Content Authenticity) ISACA AI Governance Guidance - Blockchain for Data Integrity and Deepfake Mitigation
NEW QUESTION # 40
When integrating AI for innovation, which of the following can BEST help an organization manage security risk?
- A. Re-evaluating the risk appetite
- B. Adopting a phased approach
- C. Evaluating compliance requirements
- D. Seeking third-party advice
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.
References:
AAISM Exam Content Outline - AI Risk Management (Innovation and Risk Control) AI Security Management Study Guide - Phased Implementation Strategies
NEW QUESTION # 41
Which of the following is the MOST important course of action prior to placing an in-house developed AI solution into production?
- A. Perform testing, evaluation, validation, and verification
- B. Perform a privacy, security, and compliance gap analysis
- C. Deploy a prototype of the solution
- D. Obtain senior management sign-off
Answer: A
Explanation:
AAISM lifecycle governance guidance specifies that before any AI solution is moved into production, it must undergo testing, evaluation, validation, and verification to ensure accuracy, resilience, security, and compliance with standards. These steps confirm that the solution performs as expected under varied conditions. Conducting gap analysis is part of compliance checks but comes earlier in design. Management sign-off provides approval but cannot substitute for assurance of technical reliability. Deploying prototypes is a testing method but not the final assurance step. The critical requirement is a complete cycle of testing, validation, and verification.
References:
AAISM Exam Content Outline - AI Risk Management (Lifecycle Testing and Validation) AI Security Management Study Guide - Production Readiness Checks
NEW QUESTION # 42
Which of the following is the MOST critical key risk indicator (KRI) for an AI system?
- A. The rate of drift in the model
- B. The response time of the model
- C. The amount of data in the model
- D. The accuracy rate of the model
Answer: A
Explanation:
AAISM highlights that while accuracy and performance metrics are important, the rate of drift is the most critical KRI for AI systems. Model drift occurs when input data or environmental conditions shift, causing the system to degrade and produce unreliable outputs. This risk indicator directly reflects whether the AI continues to function as intended over time. Accuracy rates and response times are performance metrics, not primary risk signals. The amount of data in the model does not reliably indicate exposure to risk. Therefore, the greatest KRI for ongoing assurance and governance is the rate of drift.
References:
AAISM Study Guide - AI Risk Management (Monitoring and Drift Detection) ISACA AI Security Management - Key Risk Indicators for AI Systems
NEW QUESTION # 43
Which of the following is MOST important to monitor in order to ensure the effectiveness of an organization' s AI vendor management program?
- A. Vendor participation in industry AI research
- B. Vendor reviews of external AI threat reports
- C. Vendor compliance with AI-related requirements
- D. Vendor results in compliance training programs
Answer: C
Explanation:
The AAISM framework specifies that the primary metric of effectiveness in vendor management is the vendor's compliance with AI-related requirements defined in contracts and governance frameworks. This provides measurable assurance that vendors adhere to agreed-upon privacy, security, and ethical standards.
Reviews of threat reports, training results, or research participation are supplemental and may support continuous improvement, but they do not establish compliance accountability. Governance requires a direct focus on whether contractual and regulatory obligations are being fulfilled. Therefore, vendor compliance with AI requirements is the most important monitoring focus.
References:
AAISM Study Guide - AI Risk Management (Third-Party Risk Oversight)
ISACA AI Security Management - Vendor Compliance Monitoring
NEW QUESTION # 44
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. Securing the model training data
- D. Utilizing unstructured data sets
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 # 45
An organization has requested a developer to apply AI algorithms to existing modules in order to improve customer service quality. At this stage, which of the following should be considered FIRST?
- A. The developer may need to be held accountable for business inquiries raised by customers
- B. Project sponsors may need to agree on a phased approach in order to ensure safe release
- C. IT management may need to revise the service agreement if AI behavior cannot be predefined
- D. The organization may need to explain the performance of the applied AI algorithm
Answer: C
Explanation:
According to AAISM governance principles, when AI functionality is added to existing services, the first consideration is contractual and service-level accountability. If AI outputs cannot be predefined, the existing service agreements may no longer reflect performance responsibilities or liability. Revising or updating the agreement ensures governance alignment, accountability, and risk management for AI-driven behavior.
Phased approaches and performance explanations are valuable but occur later in project management.
Developer accountability for customer inquiries is not a primary governance step. The most immediate consideration is revising service agreements when AI introduces new uncertainties.
References:
AAISM Exam Content Outline - AI Governance and Program Management (Policies and Service Agreements) AI Security Management Study Guide - Accountability in AI Deployments
NEW QUESTION # 46
Which of the following is the MOST effective use of AI in incident response?
- A. Automating incident response triage
- B. Streamlining incident response testing
- C. Ensuring chain of custody
- D. Improving incident response playbook
Answer: A
Explanation:
AAISM's risk management guidance notes that the most effective application of AI in incident response is in automating triage activities. AI systems can rapidly analyze logs, alerts, and telemetry to prioritize incidents, reducing response times and allowing human analysts to focus on critical issues. Streamlining testing and improving playbooks are valuable but secondary benefits. Ensuring chain of custody is critical for legal admissibility of evidence but is primarily a human and process-driven control, not AI's strength. The greatest efficiency and effectiveness comes from AI-driven triage automation.
References:
AAISM Exam Content Outline - AI Risk Management (AI for Incident Detection and Response) AI Security Management Study Guide - Automation in Security Operations
NEW QUESTION # 47
Which of the following is the MOST important course of action when implementing continuous monitoring and reporting for AI-based systems?
- A. Develop standardized risk reporting templates for different stakeholder groups
- B. Establish an automated alert system for threshold breaches in risk metrics
- C. Implement a risk dashboard for visualizing and tracking AI-related risk over time
- D. Implement real-time monitoring of key risk indicators (KRIs) for AI systems
Answer: D
Explanation:
The AAISM governance framework specifies that the foundation of continuous monitoring is real-time tracking of key risk indicators. This ensures immediate detection of deviations, model drift, and operational anomalies. Automated alerts, dashboards, and reporting templates all support monitoring, but they rely on the presence of accurate, real-time KRI measurement as their source. Without live monitoring, the other controls are reactive rather than proactive. The most important course of action in establishing effective continuous monitoring is therefore real-time KRI tracking.
References:
AAISM Study Guide - AI Governance and Program Management (Continuous Monitoring and Assurance) ISACA AI Risk Guidance - Monitoring Key Risk Indicators
NEW QUESTION # 48
Which of the following security framework elements BEST helps to safeguard the integrity of outputs generated by AI algorithms?
- A. Management is prepared to disclose AI system architecture to stakeholders
- B. Ethical standards are incorporated into security awareness programs
- C. Responsibility is defined for legal actions related to AI regulatory requirements
- D. Risk exposure due to bias in AI outputs is kept within an acceptable range
Answer: D
Explanation:
According to AAISM technical controls, the element of security frameworks that directly safeguards output integrity is ensuring that bias-related risks are maintained within acceptable ranges. This ensures that AI results remain consistent, fair, and reliable, preserving trust in outputs. Ethical awareness, architectural disclosure, and legal responsibilities are governance practices but do not directly secure output integrity.
Output integrity is primarily protected through bias management and ongoing evaluation of fairness and accuracy.
References:
AAISM Exam Content Outline - AI Technologies and Controls (Integrity of AI Outputs) AI Security Management Study Guide - Bias and Output Integrity Controls
NEW QUESTION # 49
Which of the following controls BEST mitigates the risk of data poisoning?
- A. Intrusion detection
- B. Data set restoration
- C. Data validation
- D. Digital watermarking
Answer: C
Explanation:
The AAISM technical controls framework emphasizes data validation as the primary safeguard against data poisoning attacks. Poisoning occurs when attackers insert malicious or corrupted data into training sets.
Validation techniques verify the quality, authenticity, and consistency of input data before training, preventing compromised samples from corrupting the model. Restoration helps after compromise, watermarking protects ownership, and intrusion detection monitors networks rather than data quality. The most effective preventive measure is data validation.
References:
AAISM Study Guide - AI Technologies and Controls (Data Poisoning Mitigation) ISACA AI Security Management - Data Validation and Quality Controls
NEW QUESTION # 50
Which of the following is the BEST mitigation control for membership inference attacks on AI systems?
- A. AI threat modeling
- B. Differential privacy
- C. Model ensemble techniques
- D. Cybersecurity-oriented red teaming
Answer: B
Explanation:
Membership inference attacks attempt to determine whether a particular data point was part of a model's training set, which risks violating privacy. The AAISM study guide highlights differential privacy as the most effective mitigation because it introduces mathematical noise that obscures individual contributions without significantly degrading model performance. Ensemble methods improve robustness but do not specifically protect privacy. Threat modeling and red teaming help identify risks but are not direct controls. The explicit mitigation control aligned with privacy preservation for membership inference is differential privacy.
References:
AAISM Study Guide - AI Technologies and Controls (Privacy-Preserving Techniques) ISACA AI Security Management - Membership Inference Mitigations
NEW QUESTION # 51
Which of the following metrics BEST evaluates the ability of a model to correctly identify all true positive instances?
- A. F1 score
- B. Precision
- C. Specificity
- D. Recall
Answer: D
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.
References:
AAISM Study Guide - AI Technologies and Controls (Evaluation Metrics and Model Performance) ISACA AI Security Management - Model Accuracy and Completeness Assessments
NEW QUESTION # 52
Which of the following factors is MOST important for preserving user confidence and trust in generative AI systems?
- A. Data anonymization
- B. Bias minimization
- C. Transparent disclosure and informed consent
- D. Access controls and secure storage solutions
Answer: C
Explanation:
AAISM risk guidance underscores that transparent disclosure and informed consent are the most important factors in maintaining user trust in generative AI. Users must clearly understand how outputs are created, what data sources are used, and how risks such as bias or misinformation are managed. While bias minimization, access controls, and anonymization contribute to technical or ethical robustness, they are not sufficient to preserve user trust. Trust requires openness and consent, which align with governance expectations for transparency and accountability.
References:
AAISM Exam Content Outline - AI Risk Management (Transparency and Trust) AI Security Management Study Guide - User Confidence in Generative AI
NEW QUESTION # 53
Which of the following is MOST important for an organization to consider when implementing a preventive security safeguard into a new AI product?
- A. Model output monitoring
- B. Differential privacy
- C. Input sanitization
- D. Penetration testing
Answer: C
Explanation:
AAISM materials emphasize that the most effective preventive safeguard is to ensure input sanitization.
Preventive controls stop malicious or malformed inputs from reaching the model in the first place, thereby reducing the likelihood of prompt injection, evasion, or poisoning at inference time. Model output monitoring is a detective control, not preventive. Penetration testing is an assessment technique rather than a safeguard.
Differential privacy protects data privacy but does not prevent adversarial input manipulation. Therefore, the most important preventive safeguard in a new AI product is robust input sanitization.
References:
AAISM Study Guide - AI Technologies and Controls (Preventive vs. Detective Safeguards) ISACA AI Security Management - Input Validation in AI Systems
NEW QUESTION # 54
Which of the following controls BEST mitigates the inherent limitations of generative AI models?
- A. Classifying and labeling AI systems
- B. Adopting AI-specific regulations
- C. Ensuring human oversight
- D. Reverse engineering the models
Answer: C
Explanation:
The AAISM governance framework emphasizes that the inherent limitations of generative AI-including hallucinations, bias, and unpredictability-are best mitigated by human oversight. Human-in-the-loop review ensures that outputs are validated before being used in sensitive or high-risk contexts. Regulatory adoption, system classification, and reverse engineering all play supporting roles but do not directly safeguard against the model's inherent unpredictability. Governance best practices highlight human oversight as the critical safeguard.
References:
AAISM Exam Content Outline - AI Governance and Program Management (Human Oversight and Accountability) AI Security Management Study Guide - Mitigating Generative AI Limitations
NEW QUESTION # 55
An organization uses an AI tool to scan social media for product reviews. Fraudulent social media accounts begin posting negative reviews attacking the organization's product. Which type of AI attack is MOST likely to have occurred?
- A. Model inversion
- B. Deepfake
- C. Data poisoning
- D. Availability attack
Answer: D
Explanation:
The AAISM materials classify availability attacks as attempts to disrupt or degrade the functioning of an AI system so that its outputs become unreliable or unusable. In this scenario, the fraudulent social media accounts are deliberately overwhelming the AI tool with misleading negative reviews, undermining its ability to deliver accurate sentiment analysis. This aligns directly with the concept of an availability attack. Model inversion relates to reconstructing training data from outputs, deepfakes involve synthetic content generation, and data poisoning corrupts the training set rather than manipulating inputs at runtime. Therefore, the fraudulent review campaign is most accurately identified as an availability attack.
References:
AAISM Study Guide - AI Risk Management (Adversarial Threats and Availability Risks) ISACA AI Security Management - Attack Classifications
NEW QUESTION # 56
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