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NEW QUESTION # 25
A retail organization implements an AI-driven recommendation system that utilizes customer purchase history. Which of the following is the BEST way for the organization to ensure privacy and comply with regulatory standards?
- A. Conducting quarterly retraining of the AI model to maintain the accuracy of recommendations
- B. Maintaining a register of legal and regulatory requirements for privacy
- C. Establishing a governance committee to oversee AI privacy practices
- D. Storing customer data indefinitely to ensure the AI model has a complete history
Answer: B
Explanation:
According to the AI Security Management™ (AAISM) study framework, compliance with privacy and regulatory standards must begin with a formalized process of identifying, documenting, and maintaining applicable obligations. The guidance explicitly notes that organizations should maintain a comprehensive register of legal and regulatory requirements to ensure accountability and alignment with privacy laws. This register serves as the foundation for all governance, risk, and control practices surrounding AI systems that handle personal data.
Maintaining such a register ensures that the recommendation system operates under the principles of privacy by design and privacy by default. It allows decision-makers and auditors to trace every AI data processing activity back to relevant compliance obligations, thereby demonstrating adherence to laws such as GDPR, CCPA, or other jurisdictional mandates.
Other measures listed in the options contribute to good practice but do not achieve the same direct compliance outcome. Retraining models improves technical accuracy but does not address legal obligations. Oversight committees are valuable but require the documented register as a baseline to oversee effectively. Indefinite storage of customer data contradicts regulatory requirements, particularly the principle of data minimization and storage limitation.
AAISM Domain Alignment:
This requirement falls under Domain 1 - AI Governance and Program Management, which emphasizes organizational accountability, policy creation, and maintaining compliance documentation as part of a structured governance program.
References from AAISM and ISACA materials:
AAISM Exam Content Outline - Domain 1: AI Governance and Program Management AI Security Management Study Guide - Privacy and Regulatory Compliance Controls ISACA AI Governance Guidance - Maintaining Registers of Applicable Legal Requirements
NEW QUESTION # 26
From a risk perspective, which of the following is the MOST important step when implementing an adoption strategy for AI systems?
- A. Establishing a comprehensive AI risk assessment framework
- B. Implementing a robust risk analysis methodology tailored to AI-specific tasks
- C. Conducting an AI risk assessment and updating the enterprise risk register
- D. Benchmarking against peer organizations' AI risk strategies
Answer: C
Explanation:
AAISM guidance states that when adopting AI, the most important step is to conduct a risk assessment and update the enterprise risk register. This ensures AI-specific risks are identified, documented, and integrated into the organization's existing governance structures. Benchmarking peers provides context but does not address internal risk. Implementing methodologies and frameworks are important, but they precede or follow the assessment process. The decisive step that connects adoption to enterprise risk governance is updating the risk register with AI-specific risks.
References:
AAISM Study Guide - AI Risk Management (Integration with Enterprise Risk Management) ISACA AI Security Management - Risk Assessment and Register Updates
NEW QUESTION # 27
A model producing contradictory outputs based on highly similar inputs MOST likely indicates the presence of:
- A. Poisoning attacks
- B. Model exfiltration
- C. Evasion attacks
- D. Membership inference
Answer: C
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.
References:
AAISM Study Guide - AI Technologies and Controls (Adversarial Machine Learning and Attack Types) ISACA AI Security Management - Inference-time Attack Scenarios
NEW QUESTION # 28
To ensure AI tools do not jeopardize ethical principles, it is MOST important to validate that:
- A. AI tools are evaluated by the privacy department before implementation
- B. Stakeholders have approved alignment with company values
- C. The organization has implemented a responsible development policy
- D. Outputs of AI tools do not perpetuate adverse biases
Answer: D
Explanation:
AAISM highlights that the core ethical risk in AI is the perpetuation of bias that results in unfair or discriminatory outcomes. Therefore, the most important validation step is ensuring that outputs of AI systems are free from adverse biases. A responsible development policy, stakeholder approvals, and privacy reviews all contribute to governance, but they do not directly ensure ethical outcomes. Validation of output fairness is the critical safeguard for ensuring AI does not violate ethical principles.
References:
AAISM Study Guide - AI Risk Management (Bias and Ethics Validation)
ISACA AI Security Management - Ethical AI Practices
NEW QUESTION # 29
Which of the following AI-driven systems should have the MOST stringent recovery time objective (RTO)?
- A. Health support system
- B. Car navigation system
- C. Industrial control system
- D. Credit risk modeling system
Answer: C
Explanation:
AAISM risk guidance notes that the most stringent recovery objectives apply to industrial control systems, as downtime can directly disrupt critical infrastructure, manufacturing, or safety operations. Health support systems also require high availability, but industrial control often underpins safety-critical and real-time environments where delays can result in catastrophic outcomes. Credit risk models and navigation systems are important but less critical in terms of immediate physical and operational impact. Thus, industrial control systems require the tightest RTO.
References:
AAISM Study Guide - AI Risk Management (Business Continuity in AI)
ISACA AI Security Management - RTO Priorities for AI Systems
NEW QUESTION # 30
In the context of generative AI, which of the following would be the MOST likely goal of penetration testing during a red-teaming exercise?
- A. Stress test the model's decision-making process
- B. Replace the model's outputs with entirely random content
- C. Degrade the model's performance for existing use cases
- D. Generate outputs that are unexpected using adversarial inputs
Answer: D
Explanation:
AAISM's risk management content describes red-teaming in generative AI as focused on deliberately crafting adversarial prompts to test whether the model produces unexpected or undesired outputs that violate safety, integrity, or compliance standards. The goal is not to stress system performance or randomly disrupt outputs, but rather to uncover vulnerabilities in how the model responds to manipulative inputs. This allows organizations to improve resilience against prompt injection, jailbreaking, or harmful content generation. The correct answer is therefore generate outputs that are unexpected using adversarial inputs.
References:
AAISM Exam Content Outline - AI Risk Management (Red-Team Testing and Adversarial Exercises) AI Security Management Study Guide - Penetration Testing in Generative AI Contexts
NEW QUESTION # 31
Which of the following types of testing can MOST effectively mitigate prompt hacking?
- A. Load
- B. Regression
- C. Input
- D. Adversarial
Answer: D
Explanation:
Prompt hacking manipulates large language models by injecting adversarial instructions into inputs to bypass or override safeguards. The AAISM framework identifies adversarial testing as the most effective way to simulate such manipulative attempts, expose vulnerabilities, and improve the resilience of controls. Load testing evaluates performance, input testing checks format validation, and regression testing validates functionality after changes. None of these directly address the manipulation of natural language inputs.
Adversarial testing is therefore the correct approach to mitigate prompt hacking risks.
References:
AAISM Exam Content Outline - AI Risk Management (Testing and Assurance Practices) AI Security Management Study Guide - Adversarial Testing Against Prompt Manipulation
NEW QUESTION # 32
Which of the following security framework elements BEST helps to safeguard the integrity of outputs generated by AI algorithms?
- A. Risk exposure due to bias in AI outputs is kept within an acceptable range
- B. Responsibility is defined for legal actions related to AI regulatory requirements
- C. Management is prepared to disclose AI system architecture to stakeholders
- D. Ethical standards are incorporated into security awareness programs
Answer: A
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 # 33
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 compliance with AI-related requirements
- B. Vendor results in compliance training programs
- C. Vendor reviews of external AI threat reports
- D. Vendor participation in industry AI research
Answer: A
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 # 34
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. Data poisoning
- B. Deepfake
- C. Availability attack
- D. Model inversion
Answer: C
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 # 35
Which of the following BEST enables an organization to maintain visibility to its AI usage?
- A. Measuring the impact of AI implementation using key performance indicators (KPIs)
- B. Ensuring the board approves the policies and standards that define corporate AI strategy
- C. Maintaining a comprehensive inventory of AI systems and business units that leverage them
- D. Maintaining a monthly dashboard that captures all AI vendors
Answer: C
Explanation:
The AAISM framework stresses that the most effective way to maintain oversight of organizational AI usage is by maintaining a comprehensive inventory of all AI systems and the business units using them. Such an inventory provides a centralized, transparent record of where AI is deployed, ensuring accountability, monitoring, and compliance. While board approval, dashboards, and KPIs are important governance tools, they do not provide holistic visibility across the enterprise. The inventory ensures traceability and governance alignment, making it the best method to maintain visibility of AI usage.
References:
AAISM Study Guide - AI Governance and Program Management (AI Inventories) ISACA AI Security Management - Centralized Oversight of AI Assets
NEW QUESTION # 36
A financial institution plans to deploy an AI system to provide credit risk assessments for loan applications.
Which of the following should be given the HIGHEST priority in the system's design to ensure ethical decision-making and prevent bias?
- A. Restrict the model's decision-making criteria to objective financial metrics only.
- B. Train the system to provide advisory outputs with final decisions made by human experts.
- C. Regularly update the model with new customer data to improve prediction accuracy.
- D. Integrate a mechanism for customers to appeal decisions directly within the system.
Answer: B
Explanation:
In AI governance frameworks, credit scoring is treated as a high-risk application. For such systems, the highest-priority safeguard is human oversight to ensure fairness, accountability, and prevention of bias in automated decisions.
The AI Security Management™ (AAISM) domain of AI Governance and Program Management emphasizes that high-impact AI systems require explicit governance structures and human accountability. Human-in-the- loop design ensures that final decisions remain the responsibility of human experts rather than being fully automated. This is particularly critical in financial contexts, where biased outputs can affect individuals' access to credit and create compliance risks.
Official ISACA AI governance guidance specifies:
High-risk AI systems must comply with strict requirements, including human oversight, transparency, and fairness.
The purpose of human oversight is to reduce risks to fundamental rights by ensuring humans can intervene or override an automated decision.
Bias controls are strengthened by requiring human review processes that can analyze outputs and prevent unfair discrimination.
Why other options are not the highest priority:
A). Regular updates improve accuracy but do not guarantee fairness or ethical decision-making. Model drift can introduce new bias if not governed properly.
B). Appeals mechanisms are important for accountability, but they operate after harm has occurred.
Governance frameworks emphasize prevention through human oversight in the decision loop.
D). Restricting criteria to "objective metrics" is insufficient, as even objective data can contain hidden proxies for protected attributes. Bias mitigation requires monitoring, testing, and human oversight, not only feature restriction.
AAISM Domain Alignment:
Domain 1 - AI Governance and Program Management: Ensures accountability, ethical oversight, and governance structures.
Domain 2 - AI Risk Management: Identifies and mitigates risks such as bias, discrimination, and lack of transparency.
Domain 3 - AI Technologies and Controls: Provides the technical enablers for implementing oversight mechanisms and bias detection tools.
References from AAISM and ISACA materials:
AAISM Exam Content Outline - Domain 1: AI Governance and Program Management (roles, responsibilities, oversight).
ISACA AI Governance Guidance (human oversight as mandatory in high-risk AI applications).
Bias and Fairness Controls in AI (human review and intervention as a primary safeguard).
NEW QUESTION # 37
Which of the following BEST ensures the integrity of data sets used to train AI models?
- A. Collection and retention of only necessary data sets
- B. Tracking and verification of data sets via cryptographic controls
- C. Appropriate storage of data sets according to documented classification processes
- D. Clear documentation of data sources, types used, and processing steps
Answer: B
Explanation:
AAISM defines cryptographic tracking and verification as the best control for ensuring the integrity of training data. By applying hashing and verification methods, organizations can confirm that datasets remain unaltered and authentic throughout collection, storage, and processing. Collecting only necessary data, proper storage, or clear documentation all support governance and compliance, but they do not guarantee that the data has not been tampered with. Integrity is specifically ensured by cryptographic verification techniques.
References:
AAISM Exam Content Outline - AI Risk Management (Data Integrity and Protection) AI Security Management Study Guide - Cryptographic Controls for Dataset Integrity
NEW QUESTION # 38
Which of the following MOST effectively minimizes the attack surface when securing AI agent components during their development and deployment?
- A. Implement compartmentalization with least privilege enforcement.
- B. Consolidate event logs for correlation and centralized analysis.
- C. Deploy pre-trained models directly into production.
- D. Schedule periodic manual code reviews.
Answer: A
Explanation:
The most effective strategy tominimize attack surfacesin AI agent security is to apply compartmentalization and least privilege enforcement.
AAISM control frameworks emphasize:
* Isolation of components (e.g., training, inference, data pipelines) to limit lateral movement.
* Principle ofleast privilegeto restrict access only to what is required for function.
* Hardening AI pipelines through segmentation rather than relying solely on manual reviews or monitoring.
Pre-trained models and log centralization are useful but do not directly reduce the attack surface.Manual code reviewsare important but insufficient against runtime exploitation.
Thus,compartmentalization with least privilege enforcementis the most effective technical safeguard.
NEW QUESTION # 39
Which of the following controls BEST mitigates the inherent limitations of generative AI models?
- A. Ensuring human oversight
- B. Reverse engineering the models
- C. Adopting AI-specific regulations
- D. Classifying and labeling AI systems
Answer: A
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 # 40
Which of the following controls BEST mitigates the risk of bias in AI models?
- A. Robust access control techniques
- B. Cryptographic hash functions
- C. Diverse data sourcing strategies
- D. Regular data reconciliation
Answer: C
Explanation:
Bias in AI models primarily stems from limitations or imbalances in training data. The AAISM study materials emphasize that the most effective way to mitigate this risk is through diverse data sourcing strategies that ensure coverage across demographics, scenarios, and contexts. Access controls protect data security, not fairness. Data reconciliation ensures accuracy but does not address representational imbalance.
Cryptographic hashing preserves integrity but has no impact on bias mitigation. To reduce systemic unfairness, the critical control is sourcing diverse and representative data.
References:
AAISM Exam Content Outline - AI Technologies and Controls (Bias and Fairness Management) AI Security Management Study Guide - Data Governance and Bias Reduction Strategies
NEW QUESTION # 41
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