(Sep-2026) Latest AAISM Dumps for Success in Actual ISACA Certified
Changing the Concept of AAISM Exam Preparation 2026
ISACA AAISM Exam Syllabus Topics:
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NEW QUESTION # 163
An organization deploying an LLM is concerned input manipulations could compromise security. What is the MOST effective way to determine an acceptable risk threshold?
- A. Deploy real-time logging and monitoring
- B. Implement a static threshold limiting LLM outputs
- C. Restrict all inputs containing special characters
- D. Assess the business impact of known threats
Answer: D
Explanation:
AAISM instructs that acceptable risk thresholds must be determined using business impact analysis. This aligns with the broader enterprise risk management principle of defining tolerances based on:
* potential harm
* regulatory exposure
* financial impact
* operational disruption
Monitoring (A) detects attacks but does not set thresholds. Blocking special characters (B) is unrealistic and overly restrictive. Static thresholds (D) ignore business context and practicality.
References: AAISM Study Guide - AI Risk Appetite and Threshold Determination.
NEW QUESTION # 164
From a risk perspective, which of the following is the MOST important step when implementing an adoption strategy for AI systems?
- A. Benchmarking against peer organizations' AI risk strategies
- B. Conducting an AI risk assessment and updating the enterprise risk register
- C. Establishing a comprehensive AI risk assessment framework
- D. Implementing a robust risk analysis methodology tailored to AI-specific tasks
Answer: B
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 # 165
An organization is implementing an AI-based credit assessment engine using internal and third-party customer data. Which of the following BEST aligns with data management controls for the AI life cycle?
- A. Use of hashed identifiers to anonymize datasets used for model validation and internal analytics
- B. Limitation of model training to structured data from vetted sources to minimize ingestion risk
- C. Documented procedures for data sourcing, lineage tracking, and quality validation
- D. Encrypted isolation and dynamic access controls on training data pipelines
Answer: C
Explanation:
The strongest alignment with AI life-cycle data management controls is establishing documented procedures for data sourcing, lineage tracking, and quality validation. Governance requires end-to-end data controls- provenance, consent, sourcing approvals, lineage, quality checks, labeling standards, retention, and auditability-so models are trained and validated on trustworthy, compliant data. Hashing alone is pseudo- anonymization and does not deliver full governance. Restricting to structured data is an implementation constraint, not a control set. Encryption and access control protect confidentiality/availability but do not ensure lineage or quality.
References: AI Security Management (AAISM) Body of Knowledge: Data Governance & Stewardship; AI Data Lifecycle Controls; Data Provenance, Lineage, and Quality Assurance. AAISM Study Guide: Data Intake Procedures; Lineage and Audit Evidence; Quality Gates and Approval Workflows.
NEW QUESTION # 166
Which of the following is MOST important to ensure security throughout the AI data life cycle?
- A. Maintaining a complete inventory with data lineage records
- B. Leveraging selected open-source models
- C. Restricting use of data in third-party models
- D. Conducting periodic data reviews
Answer: A
Explanation:
AAISM emphasizes data lineage, provenance tracking, and inventory completeness as essential controls to ensure data security and accountability across all AI data life-cycle phases. This enables detection of unauthorized modifications, improper use, and compliance violations.
NEW QUESTION # 167
An organization is evaluating a SaaS-based HR system that uses AI for resume vetting. Which control is MOST important?
- A. Encryption and isolation of customer data
- B. Inclusion of diverse and representative training data
- C. Availability of backups
- D. Vendor conformity assessments
Answer: B
Explanation:
AAISM states that HR systems performing candidate evaluation must prioritize training data fairness, representativeness, and bias mitigation because biased HR decisions carry regulatory, ethical, and litigation risks.
Backups (B) and encryption (D) relate to availability and confidentiality, not fairness. Conformity assessments (C) are helpful but secondary.
References: AAISM Study Guide - AI Bias and Fairness; High-Risk HR Use Cases.
NEW QUESTION # 168
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. Project sponsors may need to agree on a phased approach in order to ensure safe release
- B. The organization may need to explain the performance of the applied AI algorithm
- C. IT management may need to revise the service agreement if AI behavior cannot be predefined
- D. The developer may need to be held accountable for business inquiries raised by customers
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 # 169
After implementing a third-party generative AI tool, an organization learns about new regulations related to how organizations use AI. Which of the following would be the BEST justification for the organization to decide not to comply?
- A. The AI tool is regularly audited
- B. The AI tool is widely used within the industry
- C. The cost of noncompliance was not determined
- D. The risk is within the organization's risk appetite
Answer: D
Explanation:
The AAISM framework clarifies that compliance decisions must always be tied to an organization's risk appetite and tolerance. When new regulations emerge, management may choose not to comply if the associated risk remains within the documented and approved risk appetite, provided that accountability is established and governance structures support this decision. Other options such as widespread industry use, third-party audits, or lack of cost assessment do not justify noncompliance under the governance principles.
The risk appetite framework is the only recognized justification under AI governance principles.
References:
AAISM Study Guide - AI Governance and Program Management
ISACA AI Risk Guidance - Risk Appetite and Compliance Decisions
NEW QUESTION # 170
Which of the following is the MOST important course of action when implementing continuous monitoring and reporting for AI-based systems?
- A. Establish an automated alert system for threshold breaches in risk metrics
- B. Implement real-time monitoring of key risk indicators (KRIs) for AI systems
- C. Develop standardized risk reporting templates for different stakeholder groups
- D. Implement a risk dashboard for visualizing and tracking AI-related risk over time
Answer: B
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 # 171
An organization is deploying a large language model (LLM) and is concerned that input manipulations may compromise its integrity. Which of the following is the MOST effective way to determine an acceptable risk threshold?
- A. Restrict all user inputs containing special characters
- B. Deploy a real-time logging and monitoring system
- C. Implement a static risk threshold by limiting LLM outputs
- D. Assess the business impact of known threats
Answer: D
Explanation:
AAISM requires that risk thresholds/tolerances be set by aligning threat likelihood and impact with the organization's business context and risk appetite. Determining "acceptable" risk starts with assessing business impact of credible threats (e.g., prompt injection leading to data exfiltration, policy evasion, or harmful actions), then translating this into control intensity and thresholds. Hard input restrictions (A) and static output caps (C) are blunt measures that may degrade utility without ensuring alignment to risk appetite.
Monitoring (B) is essential for detection, but it does not, by itself, define what level of risk is acceptable.
References: AI Security Management (AAISM) Body of Knowledge - Risk Appetite and Tolerance for AI; Threat Modeling for LLMs; Business Impact Analysis and Risk Acceptance Criteria.
NEW QUESTION # 172
Which of the following would BEST help an organization align its AI initiatives with business objectives?
- A. Establishing an AI governance committee
- B. Complying with applicable AI-related regulations
- C. Protecting enterprise information used by AI projects
- D. Ensuring ethical use of AI technologies in projects
Answer: A
Explanation:
An AI governance committee provides cross-functional oversight to align AI strategy, investment, and risk appetite with business goals. It sets policies, prioritizes portfolios, ensures accountability, and integrates compliance, ethics, and security into decision-making. While compliance, ethics, and information protection are essential, governance is the primary mechanism that systematically connects AI initiatives to enterprise objectives.
NEW QUESTION # 173
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 # 174
An organization is looking to purchase an AI application from a vendor but is concerned about the security of its data. Which of the following is the MOST effective way to address this concern?
- A. Initiate discussions between the organization's and the vendor's legal teams
- B. Ensure vendors disclose how the application uses the organization's data
- C. Mandate an AI security audit by an external auditor before procurement
- D. Assess the vendor's publicly available AI usage policy
Answer: B
Explanation:
AAISM's approach to third-party and vendor risk for AI systems stresses data usage transparency as a primary control. The guidance explains that organizations must obtain clear documentation on "what data is collected, how it is processed, stored, retained, and whether it is reused for training or shared with other parties." Option C directly addresses this by requiring the vendor to disclose how the application uses organizational data, enabling appropriate risk assessment, contractual controls, and technical safeguards.
NEW QUESTION # 175
The PRIMARY objective of performing tests to detect membership inference in machine learning (ML) models is to determine whether the model:
- A. is susceptible to attacks that alter predictions through adversarial inputs.
- B. can reveal details about the data it was trained on.
- C. can handle changes in the input data without affecting its accuracy.
- D. does not exhibit any biases toward certain groups of data.
Answer: B
Explanation:
Membership inference testing evaluates whether a model can disclose whether specific data points were part of its training dataset, indicating potential leakage of sensitive information about the data it was trained on.
NEW QUESTION # 176
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 employee awareness training on recognizing deepfake videos and audio
- B. Engage with brand monitoring services to track social media activity
- C. Provide clarifying information in a pre-approved public statement
- D. Conduct a detailed forensic analysis to identify the source of the deepfake
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 # 177
A security assessment revealed that attackers could access sensitive company data through chat interface injection. What is the BEST mitigation?
- A. Conducting regular security audits
- B. Implementing input validation and templates
- C. Manually reviewing AI model outputs
- D. Ensuring continuous monitoring and tagging
Answer: B
Explanation:
AAISM explains that prompt injection attacks are best mitigated by:
- strict input validation
- templated prompts
- controlled context windows
- guardrail enforcement
These prevent malicious instructions from overriding system prompts.
NEW QUESTION # 178
A retailer's AI-driven dynamic pricing system failed after its feature store was corrupted, resulting in all prices being set to zero. The data source has been isolated, and management demands immediate recovery. Which of the following is the MOST appropriate course of action to restore operations while minimizing impact?
- A. Require customer service to manually price and approve orders until the AI system is fully restored.
- B. Revert to the previous AI model and validate backup of the feature store.
- C. Keep the system offline and conduct a full forensic investigation.
- D. Failover to a secondary environment and retrain the model with recent data.
Answer: B
Explanation:
Reverting to a previously stable model and validating a clean backup of the feature store enables rapid restoration of normal operations while ensuring data integrity. This minimizes business disruption and avoids propagating corrupted data into the system.
NEW QUESTION # 179
An AI application development team has been given access to user information and now must format it to be readable by the AI model. During which phase of the data life cycle would this MOST likely occur?
- A. Data minimization
- B. Data collection
- C. Data preparation
- D. Data normalization
Answer: C
Explanation:
In the AI data lifecycle, converting raw user information into model-readable form (e.g., cleaning, parsing, feature engineering, encoding, normalization/standardization, and schema alignment) is performed during data preparation. This phase establishes input quality and structure prior to training and inference and may include normalization as one of several preparation steps. Data collection acquires data, minimization limits data to what's necessary, and normalization is a specific technique-not the overarching phase.
References: AI Security Management (AAISM) Body of Knowledge: Data Lifecycle-Preparation and Ingestion Controls; AAISM Study Guide: Data Preparation Activities (cleaning, labeling, feature engineering, encoding) and Quality Gates.
NEW QUESTION # 180
A bank is training an AI credit scoring model on a vast amount of sensitive customer data. Which of the following is the MOST critical security risk in this training process?
- A. Algorithmic bias leading to discriminatory lending
- B. Model inversion or membership inference attacks
- C. Resources needed to retrain and validate the AI model
- D. Data poisoning to corrupt training outcomes
Answer: D
NEW QUESTION # 181
A SaaS-based LLM system has risks including prompt injection, data poisoning, and model exfiltration. What is the BEST way to ensure consistent risk treatment?
- A. Implement an AI threat control matrix mapping threats to controls and assurance
- B. Apply control baselines from a recognized industry standard
- C. Focus on post-deployment red teaming
- D. Rely on vendor audit reports and SLAs
Answer: A
Explanation:
AAISM highlights the importance of an AI threat-control matrix that:
* maps threats (e.g., poisoning, exfiltration, injection)
* identifies required controls
* defines assurance/testing activities
* ensures consistent coverage across all AI components
Control baselines (A) are useful but not AI-specific. Red teaming (C) is reactive and not comprehensive.
Vendor reports (D) are supplemental, not sufficient.
References: AAISM Study Guide - AI Risk Treatment; Threat-Control Mapping.
NEW QUESTION # 182
When preparing for an AI incident, which of the following should be done FIRST?
- A. Implement a clear communication channel to report AI incidents
- B. Create containment and eradication procedures for AI-related incidents
- C. Establish recovery processes for AI system models and datasets
- 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 # 183
When evaluating a third-party AI service provider, which master services agreement (MSA) provision is MOST critical for managing security risk?
- A. Restricting query volume thresholds
- B. Sharing real-time log information
- C. Prohibiting the use of customer data for model training
- D. Guaranteeing unlimited model retraining requests
Answer: C
Explanation:
AAISM emphasizes strong contractual restrictions on how vendors use customer data, especially prohibiting vendors from using customer inputs to train or fine-tune shared models.
This protects against:
* data leakage
* intellectual property exposure
* regulatory violations
* shadow training of external models
Log sharing (B) and query limits (D) are operational controls but do not directly prevent data misuse.
Unlimited retraining (A) has no relevance to security.
References: AAISM Study Guide - Vendor Risk Management; Data Usage Restrictions in Contracts.
NEW QUESTION # 184
A global financial organization wants to deploy an AI solution for credit scoring. Which of the following should the organization do FIRST?
- A. Perform bias assessments based on cultural differences and demographic landscapes.
- B. Verify that the AI solution meets compliance requirements for local laws and regulations.
- C. Maintain thorough documentation of the validation process, data used, and results.
- D. Create a test, evaluation, verification and validation (TEVV) strategy for AI solutions.
Answer: B
Explanation:
Ensuring the solution complies with applicable laws and regulations is the foundational step before any deployment activities. Regulatory compliance defines permissible data use, model behavior, and governance requirements, which must be established before conducting assessments, testing strategies, or documentation.
NEW QUESTION # 185
Which of the following is the MOST effective use of AI in incident response?
- A. Automating incident response triage
- B. Improving incident response playbook
- C. Streamlining incident response testing
- D. Ensuring chain of custody
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 # 186
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