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NEW QUESTION # 174
An organization has discovered that employees have started regularly utilizing open-source generative AI without formal guidance. Which of the following should be the CISO's GREATEST concern?
- A. Policy violations
- B. Model hallucinations
- C. Lack of monitoring
- D. Data leakage
Answer: D
Explanation:
The greatest immediate risk from unsanctioned use of public or open-source generative AI tools is data leakage--employees may paste confidential or regulated information into third-party systems, resulting in loss of confidentiality, regulatory exposure, and loss of intellectual property.
AAISM emphasizes that when AI use occurs outside approved channels, the top control priority is preventing exfiltration of sensitive data via prompts, attachments, and context sharing. Monitoring and policy are necessary enablers, but leakage is the highest-impact failure mode in the short term; hallucinations primarily affect accuracy, not confidentiality.
NEW QUESTION # 175
An AI fraud detection system in a bank uses transaction data that includes customer names and account numbers. Which of the following BEST helps the bank comply with privacy regulations and data classification policies?
- A. Masking or tokenizing customer-related data in use
- B. Conducting awareness training for AI developers
- C. Security training to avoid leakage of personal data
- D. Performing periodic audits of the AI model for compliance
Answer: A
Explanation:
Masking or tokenizing customer names and account numbers directly protects sensitive and regulated data while allowing the AI system to continue processing transactions. This is the most effective control for supporting compliance with privacy regulations and data classification requirements.
NEW QUESTION # 176
Which of the following BEST supports the evaluation of AI system outputs?
- A. Input controls
- B. Manual review
- C. Model card assessment
- D. Scenario analysis
Answer: B
Explanation:
Manual review allows direct assessment of AI outputs for accuracy, relevance, and potential issues in real-world contexts, making it the most effective method for evaluating the quality and reliability of outputs.
NEW QUESTION # 177
Which testing technique is BEST for determining how an AI model makes decisions?
- A. Red team
- B. Black box
- C. Blue team
- D. White box
Answer: D
Explanation:
AAISM indicates that white-box testing allows evaluators full visibility into:
* internal logic
* weights
* decision pathways
* model architecture
This makes it ideal for understanding how decisions are made.
Black box (B) provides no internal visibility. Red/blue team tests (A, D) focus on security, not decision mechanics.
References: AAISM Study Guide - AI Testing; Explainability Through White-Box Analysis.
NEW QUESTION # 178
An organization is adopting an agentic AI solution from an external vendor to support internal IT operations. 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:
AAISM states that when evaluating external AI vendors, independently issued third-party audit reports (SOC, ISO, AI assurance assessments) provide the strongest evidence of implemented controls because they are objective, repeatable, and externally verified.
NEW QUESTION # 179
A pharmaceutical organization uses an AI solution from a third-party vendor to analyze confidential research and development data. Which of the following controls is MOST essential to ensure the vendor's AI solution complies with the organization's security requirements?
- A. Review the vendor's independent security audit report.
- B. Communicate security requirements to the vendor.
- C. Minimize sensitive data being used by the vendor's AI solution.
- D. Implement a private instance of the solution.
Answer: A
Explanation:
Reviewing the vendor's independent security audit report provides objective assurance that the vendor's AI solution and related controls have been evaluated against recognized security standards and requirements. This is essential for validating compliance with the organization's security expectations when handling confidential research data.
NEW QUESTION # 180
Which of the following is the GREATEST benefit of implementing an AI tool to safeguard sensitive data and prevent unauthorized access?
- A. Reduced number of false positives
- B. Timely initiation of incident response
- C. Timely analysis of endpoint activities
- D. Reduced need for data classification
Answer: A
Explanation:
The AAISM study materials highlight that AI-powered security tools provide the greatest benefit by reducing false positives in monitoring and access control systems. This improves efficiency, prevents alert fatigue, and enables security teams to focus on true threats. While timely analysis and incident response are benefits, they are not unique to AI-based tools and can be achieved with traditional methods. AI also does not remove the need for data classification, as classification underpins governance and compliance. The standout advantage is the improved accuracy and reduced false positives provided by AI.
References:
AAISM Study Guide - AI Technologies and Controls (Security Tools and Access Management) ISACA AI Security Management - Benefits of AI-Enabled Security
NEW QUESTION # 181
An organization is designing an AI-based credit risk assessment system that will integrate with sensitive financial datasets. Which of the following would BEST support the implementation of security-by-design principles in the AI system's architecture?
- A. Segmenting AI services across containers to manage resource constraints
- B. Applying threat modeling specific to AI components before deployment
- C. Restricting access to AI models using IP allow lists to reduce public exposure
- D. Integrating differential privacy mechanisms into model training to limit data leakage
Answer: B
Explanation:
Security by design in AI requires establishing risk-informed requirements at the earliest stages of the lifecycle and systematically translating them into architectural controls. Conducting AI-specific threat modeling before deployment is the highest-leverage action because it identifies assets (data, models, pipelines), trust boundaries (feature stores, training/inference services), threat events (poisoning, evasion, model extraction), and attack paths unique to ML systems. The outputs (abuse/misuse cases, control objectives, verification plans) then drive selection and prioritization of controls such as privacy-enhancing techniques, access controls, isolation, monitoring, and assurance testing. While differential privacy (C) is a strong control for leakage risk, it is one control choice among many and should be selected as a result of threat modeling. IP allow lists (B) and container segmentation (A) are valuable hardening measures but are narrower and do not replace the lifecycle-wide governance and design traceability that threat modeling enables.
References: AI Security Management (AAISM) Body of Knowledge - Secure AI SDLC; AI Threat Modeling and Abuse Case Development; Architecture & Control Selection; Risk-Based Design Assurance.
AAISM Study Guide - Security-by-Design for AI; Model/System Asset Mapping; Control Objectives from Threat Models.
NEW QUESTION # 182
A programmer suspects an AI system is inferring sensitive user information. What is the BEST action?
- A. Inform the governance panel
- B. Alert the CIO
- C. Suggest fine-tuning
- D. Conduct a code review
Answer: A
Explanation:
AAISM directs that potential privacy, ethical, or compliance risks must be escalated to the AI Governance Panel, the body responsible for oversight, risk approval, and corrective action.
NEW QUESTION # 183
Which of the following approaches BEST helps to reduce model bias?
- A. Ensuring diversity in training data sources
- B. Utilizing a more complex model architecture
- C. Increasing the number of labels per instance
- D. Decreasing the frequency of model updates
Answer: A
Explanation:
AAISM frames bias risk primarily as a data problem. The most impactful mitigation is to ensure diversity and representativeness of training data sources, thereby reducing sampling bias and improving fairness across subpopulations. More labels per instance (A) does not correct coverage gaps; reducing update cadence (B) can entrench existing bias; and higher model complexity (C) may overfit or obscure bias without addressing root causes. Diverse, representative datasets-paired with fairness testing-are the recommended first-line control.
References: AI Security Management™ (AAISM) Body of Knowledge - Bias Identification and Mitigation; Data Quality and Representativeness. AAISM Study Guide - Fairness Risk Controls; Dataset Curation and Coverage Analysis.
NEW QUESTION # 184
Which of the following AI data life cycle phases presents the GREATEST inherent risk?
- A. Preparation
- B. Training
- C. Maintenance
- D. Monitoring
Answer: A
Explanation:
The data Preparation phase-covering sourcing, collection, labeling, cleansing, and provenance-presents the greatest inherent risk because it is where privacy, consent, representativeness, bias, quality, lineage, and legality must be established. Decisions and defects here propagate into training and downstream use, amplifying ethical, regulatory, and accuracy risks.
While Training can introduce additional risks (e.g., poisoning, leakage), these are frequently mitigated by controls that depend on having trustworthy prepared data. Monitoring and Maintenance are later-life-cycle phases oriented toward detection and correction; they are critical but inherently rely on the foundation set during Preparation.
References: AI Security Management™ (AAISM) Body of Knowledge: "AI Data Lifecycle Risks-Sourcing, Consent, Provenance, and Bias," "Risk Treatment Priorities Across the Lifecycle"; AAISM Study Guide:
"Data Preparation Controls and Quality Gates," "Bias and Privacy Risk Controls at Ingestion."
NEW QUESTION # 185
An organization is implementing a large language model (LLM) to answer questions for both employees and customers. The CIO wants the LLM to provide employees with additional internal data that should not be shared with customers. Which of the following is the MOST secure approach?
- A. Allow list employee access
- B. Model fine tuning with increased use of function calls
- C. Prompt sanitization
- D. Retrieval-augmented generation (RAG) with access control lists (ACLs)
Answer: D
Explanation:
Retrieval-augmented generation (RAG) combined with access control lists (ACLs) ensures that the LLM retrieves and exposes only the data each user is authorized to access. This approach provides granular, role-based protection of internal information while supporting different data visibility requirements for employees and customers.
NEW QUESTION # 186
Which of the following BEST describes how supervised learning models help reduce false positives in cybersecurity threat detection?
- A. They analyze patterns in data to group legitimate activity from actual threats
- B. They dynamically generate new labeled data sets
- C. They use real-time feature engineering to automatically adjust decision boundaries
- D. They learn from historical labeled data
Answer: D
Explanation:
According to AAISM technical content, supervised learning models reduce false positives by learning from historical labeled data that distinguishes between legitimate activity and actual threats. This training enables the model to recognize patterns and improve its discrimination ability over time. Grouping patterns (A) describes clustering, an unsupervised method. Real-time feature engineering (B) and generating new labeled data (D) are advanced techniques but not the fundamental supervised learning approach. The essence of supervised learning is leveraging labeled data to minimize misclassification, including false positives.
References:
AAISM Exam Content Outline - AI Technologies and Controls (Machine Learning Approaches) AI Security Management Study Guide - Supervised Learning for Threat Detection
NEW QUESTION # 187
Which of the following key risk indicators (KRIs) BEST indicates the malicious manipulation of an AI model?
- A. Instances of unauthorized data disclosure by employees using the platform
- B. Elevated frequency of detected adversarial input attempts
- C. Total uptime percentage of AI infrastructure over time
- D. Increased length and detail of model outputs
Answer: B
Explanation:
An elevated frequency of detected adversarial input attempts is a strong indicator of malicious efforts to manipulate the AI model's behavior or outputs. This directly reflects attempts to exploit vulnerabilities through adversarial attacks, prompt manipulation, or model evasion techniques.
NEW QUESTION # 188
Which of the following should be included in an AI acceptable use policy?
- A. AI training data requirements
- B. Ethical and legal compliance standards
- C. Data collection and storage processes
- D. AI monitoring requirements
Answer: B
Explanation:
An AI acceptable use policy (AUP) sets the organizational expectations and boundaries for how AI systems may be used by employees and third parties. AAISM guidance places emphasis on ethical and legal compliance standards as core elements of an AUP to govern responsible behavior, prevent misuse, and align with regulatory and organizational principles. While data requirements, collection/storage processes, and monitoring may be covered in adjacent standards and procedures (e.g., data management policies, SOPs, and operational runbooks), the AUP's essential function is to codify permissible use anchored to ethics, legality, and organizational values.
References: AI Security Management (AAISM) Body of Knowledge - AI Governance Policies and Codes of Conduct; Responsible Use Principles. AAISM Study Guide - Policy Hierarchy and Control Mapping; Acceptable Use and Staff Obligations.
NEW QUESTION # 189
Which of the following processes involves tagging and labeling raw datasets to create training data sets?
- A. Annotation
- B. Classification
- C. Transformation
- D. Categorization
Answer: A
Explanation:
Annotation is the process of tagging, labeling, or marking raw data so it can be used as structured training data for machine learning and AI models. This enables the model to learn patterns, categories, or relationships from the labeled examples.
NEW QUESTION # 190
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 monthly dashboard that captures all AI vendors
- D. Maintaining a comprehensive inventory of AI systems and business units that leverage them
Answer: D
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.
NEW QUESTION # 191
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.
NEW QUESTION # 192
An organization is implementing AI agent development across multiple engineering teams. Which of the following is the MOST important focus of AI-specific security training for developers?
- A. Dataset bias, explainability, and fairness in model decisions
- B. API abuse, data leakage, and third-party plug-in risk
- C. Output moderation, hallucination handling, and policy alignment
- D. Prompt injection, agent memory control, and insecure tool execution
Answer: D
Explanation:
For developer-facing, near-term hardening of AI agents, AAISM prioritizes secure agent design and runtime controls against prompt injection, unsafe memory/tool use, and tool-execution compromise. These are primary exploitation paths for agents that read external content, persist memory, and call tools with elevated privileges. Training must center on: guarding tool invocation, constraining memory scope, sanitizing
/validating inputs, and isolating high-risk actions. Topics like bias/fairness (B) and policy/hallucinations (C) are important but are governance/assurance concerns; API abuse and plug-in risk (D) matter, yet the core, developer-controlled attack surface for agents is injection and unsafe tool/memory design.
References:* AI Security Management (AAISM) Body of Knowledge: Agent Security-prompt injection defenses, tool execution constraints, memory governance* AAISM Study Guide: Developer controls for agent frameworks; input validation, sandboxing, scoped permissions, guardrail patterns
NEW QUESTION # 193
Which of the following is an information security manager's BEST recommendation when the new release of a proprietary AI model is generating inconsistent output?
- A. Check the model scorecard to confirm data sources.
- B. Revert to a prior version of the model.
- C. Retrain the model with more datasets.
- D. Report the issue to the AI governance committee.
Answer: B
Explanation:
Reverting to a prior stable version is the best immediate recommendation because inconsistent output from a new model release indicates potential instability or unintended behavior. Restoring the previously validated version minimizes operational and security risk while the issue is investigated and corrected.
NEW QUESTION # 194
The PRIMARY goal of data poisoning attacks is to:
- A. undermine the integrity of the AI system's outputs
- B. compromise the confidentiality of model input data
- C. compromise the confidentiality of output data from the model
- D. manipulate the behavior of the model during development
Answer: A
Explanation:
AAISM defines data poisoning as the insertion of malicious or corrupted data into training (or fine-tuning) pipelines to degrade or bias model behavior, thereby compromising output integrity in production. While poisoning occurs during development/training (C), its primary objective is the downstream integrity impact on predictions/outputs (D). Options A and B relate to confidentiality threats (e.g., inversion or leakage), not poisoning.
References:* AI Security Management™ (AAISM) Body of Knowledge: Model Integrity Threats-data poisoning aims and effects; supply-side data controls* AAISM Study Guide: Risk scenarios for poisoning; mitigations via data provenance checks, robust training, and anomaly detection
NEW QUESTION # 195
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