ISACA Advanced in AI Security Management (AAISM) Exam Practice Tests 2026 | Pass AAISM with confidence!
Practice Isaca Certification AAISM exam. Online Exam Practice Tests with detailed explanations!
ISACA AAISM Exam Syllabus Topics:
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NEW QUESTION # 28
An organization plans to apply an AI system to its business, but developers find it difficult to predict system results due to lack of visibility to the inner workings of the AI model. Which of the following is the GREATEST challenge associated with this situation?
- A. Assigning a risk owner who is responsible for system uptime and performance
- B. Continuing operations to meet expected AI security requirements
- C. Gaining the trust of end users through explainability and transparency
- D. Determining average turnaround time for AI transaction completion
Answer: C
Explanation:
AAISM materials identify explainability and transparency as the greatest challenge when models operate as
"black boxes" where inner logic is opaque. Inability to interpret how results are produced undermines the trust of business users, customers, regulators, and auditors. Explainability is emphasized as a critical governance requirement, because without it, ethical validation, accountability, and regulatory compliance are at risk.
Assigning risk owners or measuring transaction times are operational concerns, but they do not address the core trust deficit caused by lack of visibility. The greatest challenge in this situation is therefore the loss of end-user trust due to insufficient explainability.
References:
AAISM Study Guide - AI Governance and Program Management (Transparency and Explainability) ISACA AI Security Management - Ethical and Trust Considerations
NEW QUESTION # 29
Which of the following is the MOST effective way to prevent a model inversion attack?
- A. Ensure data minimization
- B. Monitor model output for anomalies
- C. Implement differential privacy during model training
- D. Utilize data pseudonymization
Answer: C
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 # 30
An aerospace manufacturer prioritizing accuracy and security wants to use generative AI. Which LLM adoption plan BEST aligns with its risk appetite?
- A. Developing a private LLM to automate non-critical functions
- B. Developing a public LLM to automate critical functions
- C. Purchasing an LLM dataset on the open market
- D. Contracting LLM access from a reputable third-party provider
Answer: A
Explanation:
AAISM notes that high-security industries (e.g., aerospace) should prefer private, controlled environments with restricted data exposure. Developing a private LLM for non-critical workloads minimizes operational and security risk while enabling innovation.
Public LLMs for critical functions (C) violate safety expectations. Purchased datasets (D) introduce unknown provenance. Outsourcing (B) increases third-party risk.
References: AAISM Study Guide - AI Governance; Safe LLM Adoption Strategies.
NEW QUESTION # 31
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. Activate filtering logic to exclude intellectual property flags
- B. Review log information that records how data was collected
- C. Disclose service provider policies to declare compliance with regulations
- D. Appoint a data steward specialized in AI to strengthen security governance
Answer: A
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 # 32
Which of the following is the PRIMARY purpose of a dedicated AI system policy?
- A. Providing a framework to set AI objectives
- B. Optimizing AI accuracy
- C. Complying with external regulations
- D. Ensuring environmental impact is minimized
Answer: A
Explanation:
Per AAISM, an AI policy is a governance instrument that defines objectives, principles, roles, responsibilities, accountability, and control requirements for AI systems across their lifecycle. It establishes the framework within which performance, compliance, ethics, risk appetite, security, privacy, and sustainability objectives are set and operationalized. Environmental considerations (A), accuracy optimization (B), and regulatory compliance (D) are important outcomes addressed under the policy, but the primary purpose is to provide the overarching framework for objectives and controls.
References: AI Security Management (AAISM) Body of Knowledge - AI Governance Frameworks; Policies, Standards, and Procedures; Roles and Accountability in AI Programs.
NEW QUESTION # 33
Which of the following is the BEST way to ensure an organization remains compliant with industry regulations when decommissioning an AI system used to record patient data?
- A. Ensure the certificate of destruction is received and archived in line with data retention policies
- B. Update governance policies based on lessons learned and ensure a feedback loop exists
- C. Perform a post-destruction risk assessment to verify that there is no residual exposure of data
- D. Ensure backups are tested and access controls are recorded and audited to ensure compliance
Answer: A
Explanation:
For regulated data such as patient information, AAISM requires provable data lifecycle closure at decommissioning. The authoritative evidence is a certificate of destruction (covering primary, replicas, backups, and caches) retained per the organization's records retention policy. While testing backups and auditing access (A), updating policies (B), and doing post-destruction risk assessment (C) are valuable practices, documented destruction attestation is the primary compliance proof point that the data was disposed of in accordance with regulatory and contractual obligations.
References: AI Security Management (AAISM) Body of Knowledge - Data Lifecycle Governance; Decommissioning & Secure Disposal; Records Retention and Evidence of Destruction.
NEW QUESTION # 34
When evaluating a third-party AI service provider, which of the following master services agreement provisions is MOST critical for managing security risk?
- A. Restricting query volume thresholds
- B. Prohibiting the use of customer data for model training
- C. Guaranteeing unlimited model retraining requests
- D. Sharing real-time log information
Answer: B
Explanation:
The most material contractual control for reducing security and privacy risk in outsourced AI services is a data-use restriction that prohibits the provider from using customer data for model training (and from derivative model improvements) unless explicitly authorized. This prevents unintended secondary processing, model inversion exposure of proprietary data, unauthorized profiling, and downstream data proliferation across multi-tenant systems. AAISM positions third-party risk controls to prioritize data minimization, purpose limitation, confidentiality, and downstream controls; among common MSA provisions, data-use limitations directly constrain the provider's technical and organizational handling of sensitive inputs, making it the highest-impact risk-reducing clause. Query throttling (B) and logging (C) are useful operational controls but are secondary to legal/processing authority. Unlimited retraining (D) increases attack surface and cost without addressing the core risk of misuse of customer data.
References: AI Security Management™ (AAISM) Body of Knowledge - Third-Party & Supply-Chain Governance; Contractual Controls for AI Services; Data Minimization and Purpose Limitation. AAISM Study Guide - Procurement & MSA/DPA Clauses for AI; Provider Model Training and Data-Use Restrictions; Privacy & Confidentiality Safeguards in Outsourced AI.
NEW QUESTION # 35
What is the GREATEST benefit of performing AI security risk assessments?
- A. Enabling risk prioritization
- B. Implementing privacy controls
- C. Securing appropriate funding
- D. Updating the risk register
Answer: A
Explanation:
AAISM emphasizes that the primary value of AI security risk assessments is prioritizing risks based on likelihood, impact, and business relevance.
Updating the register (A) is administrative. Privacy controls (B) are one category of mitigation. Funding (D) is possible but not the primary purpose.
References: AAISM Study Guide - AI Risk Assessment Objectives; Prioritization and Governance.
NEW QUESTION # 36
Which approach should an organization prioritize to effectively verify the security of its AI models?
- A. Using standard penetration testing methods
- B. Automating vulnerability identification
- C. Testing team competencies in IT threat mitigation
- D. Developing a testing strategy including AI-specific threat modeling and adversarial attack simulations
Answer: D
Explanation:
The AAISM standard explicitly states that traditional penetration tests alone are insufficient for AI systems.
Effective AI security testing requires:
* AI-specific threat modeling (e.g., data poisoning, prompt injection, model theft)
* Adversarial attack simulations (white-box, black-box, gradient-based attacks)
* Evaluation of robustness and manipulation resistance
Option B captures these requirements precisely.
Options A, C, and D do not address AI-specific attack vectors.
References: AAISM Study Guide - AI Security Testing and Adversarial Evaluation.
NEW QUESTION # 37
Which of the following datasets is used to tune hyperparameters?
- A. Validation
- B. Training
- C. Test
- D. Configuration
Answer: A
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 # 38
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. Model hallucinations
- B. Policy violations
- 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.
References:* AI Security Management (AAISM) Body of Knowledge: Generative AI governance; human- in-the-loop risks; data loss and exfiltration vectors in prompts; sanctioned vs. unsanctioned AI usage.* AI Security Management Study Guide: Immediate risk triage for shadow AI; DLP and input-control safeguards; confidentiality-first posture for generative AI adoption.
NEW QUESTION # 39
Which of the following approaches BEST helps to reduce model bias?
- A. Increasing the number of labels per instance
- B. Decreasing the frequency of model updates
- C. Utilizing a more complex model architecture
- D. Ensuring diversity in training data sources
Answer: D
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 # 40
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. Duplicating AI microservices across multiple availability zones
- B. Using explainable AI to document decision paths
- C. Including AI model supporting infrastructure in disaster recovery scenarios
- D. Periodic retraining using pre-labeled data
Answer: C
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 # 41
Which of the following strategies is the MOST effective way to protect against AI data poisoning?
- A. Using robust data validation techniques and anomaly detection
- B. Increasing model complexity to better handle data variations
- C. Ensuring the model is trained on diverse data sources
- D. Incorporating more features and data into model training
Answer: A
Explanation:
AAISM directs organizations to prevent training-time attacks by hard-gating data ingestion with provenance checks, schema and label validation, sanitization, and anomaly/outlier detection prior to model training. These controls most directly block poisoned records from entering the pipeline and are prioritized over architectural complexity or sheer data volume. Diversity of sources can improve representativeness but does not reliably stop adversarial contamination.
References: AI Security Management (AAISM) Body of Knowledge - Adversarial ML: Training-Time Threats; Secure Data Ingestion & Validation Controls; AI Risk Treatment and Assurance. AAISM Study Guide - Poisoning Prevention Gates; Provenance, Quality, and Anomaly Screening in ML Pipelines.
NEW QUESTION # 42
Which of the following controls BEST mitigates the risk of bias in AI models?
- A. Cryptographic hash functions
- B. Diverse data sourcing strategies
- C. Robust access control techniques
- D. Regular data reconciliation
Answer: B
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 # 43
Implementing which of the following would MOST effectively address bias in generative AI models?
- A. Adversarial training
- B. Data minimization
- C. Fairness constraints
- D. Data augmentation
Answer: C
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 # 44
Which strategy is MOST effective for penetration testers assessing an AI model against membership inference attacks?
- A. Disabling model logging
- B. Measuring accuracy on the test set
- C. Generating synthetic training data
- 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 # 45
Which of the following BEST ensures AI components are validated as part of disaster recovery testing?
- A. Running simulated data loss scenarios by erasing test records from the AI system's feature store
- B. Disconnecting primary model training clusters to test retraining workflow during extended outages
- C. Monitoring model performance metrics during failover and recovery to assess system stability
- D. Simulating denial of service (DoS) attacks against AI APIs to evaluate detection capabilities
Answer: C
Explanation:
Business continuity and disaster recovery (BC/DR) exercises for AI must validate that critical AI components (feature stores, model registries, inference services, pipelines) operate within agreed recovery objectives during failover and restoration. Monitoring and evaluating model performance and stability during DR tests provides objective evidence that AI services remain functional, accurate, and reliable under contingency conditions, thereby validating the AI stack end-to-end.
Option A focuses on retraining during outages (a niche scenario) rather than validating service continuity for production inference. Option B is security testing, not BC/DR validation. Option C tests data loss handling but does not comprehensively validate AI service behavior across failover and recovery.
References: AI Security Management™ (AAISM) Body of Knowledge: "Operational Resilience-BC/DR for AI Systems," "Validation and Evidence of Continuity"; AAISM Study Guide: "AI DR Test Planning- Metrics, Model Performance Validation, and Recovery Readiness."
NEW QUESTION # 46
Which of the following types of testing can MOST effectively mitigate prompt hacking?
- A. Input
- B. Load
- C. Adversarial
- D. Regression
Answer: C
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 # 47
Which testing technique is BEST for determining how an AI model makes decisions?
- A. Blue team
- B. White box
- C. Black box
- D. Red team
Answer: B
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 # 48
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. Prohibiting the use of customer data for model training
- C. Guaranteeing unlimited model retraining requests
- D. Sharing real-time log information
Answer: B
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 # 49
What is the GREATEST concern when a vendor enables generative AI features for an organization's critical system?
- A. Bias and ethical practices
- B. Proposed regulatory enhancements
- C. Access to the model
- D. Security monitoring and alerting
Answer: C
Explanation:
AAISM highlights that uncontrolled access to generative AI in critical systems introduces the highest level of risk, as such models can:
* expose sensitive data
* execute unintended actions
* be manipulated through injected prompts
* cause operational instability
Monitoring (A) is important but not the core risk. Bias (B) is significant but secondary in critical systems.
Regulatory enhancements (C) are indirect.
References: AAISM Study Guide - Generative AI Operational Risk; Access Control Priority.
NEW QUESTION # 50
Security and assurance requirements for AI systems should FIRST be embedded in the:
- A. Model testing phase
- B. Model deployment phase
- C. Model design phase
- D. Model training phase
Answer: C
Explanation:
AAISM directs organizations to embed security, safety, and compliance controls at design time ("secure-by- design" and "shift-left"), ensuring requirements for robustness, privacy, and governance are defined as non- functional constraints on architecture, data sourcing, model choices, and evaluation criteria before any model is trained. Deferring these requirements to training, testing, or deployment increases residual risk and rework, and weakens traceability of control coverage.
References:* AI Security Management™ (AAISM) Body of Knowledge: Governance-Secure-by-Design; Policy-to-Control Traceability; Requirements Management* AAISM Study Guide: AI Program Lifecycle- Planning & Design Controls; Design-time Threat Modeling and Control Selection* AAISM Mapping to Standards: Design-phase Risk Identification and Requirements Engineering for AI
NEW QUESTION # 51
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