(2026) PASS AAISM exam with ISACA AAISM Real Exam Questions
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ISACA AAISM Exam Syllabus Topics:
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NEW QUESTION # 81
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. Ensure backups are tested and access controls are recorded and audited to ensure compliance
- D. Perform a post-destruction risk assessment to verify that there is no residual exposure of data
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 # 82
Which of the following technologies can be used to manage deepfake risk?
- A. Blockchain
- B. Multi-factor authentication (MFA)
- C. Systematic data tagging
- D. Adaptive authentication
Answer: A
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 # 83
Which of the following should be included in an AI acceptable use policy?
- A. Ethical and legal compliance standards
- B. AI training data requirements
- C. Data collection and storage processes
- D. AI monitoring requirements
Answer: A
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 # 84
Which of the following controls would BEST help to prevent data poisoning in AI models?
- A. Regularly updating the foundational model
- B. Increasing the size of the training data set
- C. Establishing continuous monitoring
- D. Implementing a strict data validation mechanism
Answer: D
Explanation:
The most direct preventative control against data poisoning is robust data validation/ingestion gating:
provenance checks, schema and constraint validation, anomaly/outlier screening, label consistency tests, and whitelist/blacklist source controls before data reaches training pipelines. Larger datasets (A) don't inherently prevent poisoning; monitoring (C) is detective; updating a foundation model (D) does not address tainted inputs entering the pipeline.
References: AI Security Management (AAISM) Body of Knowledge - Adversarial ML Threats and Training-Time Attacks; Secure Data Ingestion and Validation Controls. AAISM Study Guide - Poisoning Prevention: Provenance, Validation, and Sanitization Gates.
NEW QUESTION # 85
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. Continuing operations to meet expected AI security requirements
- B. Gaining the trust of end users through explainability and transparency
- C. Determining average turnaround time for AI transaction completion
- D. Assigning a risk owner who is responsible for system uptime and performance
Answer: B
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 # 86
A post-incident investigation finds that an AI-powered anti-money laundering system inadvertently allowed suspicious transactions because certain risk signals were disabled to reduce false positives. Which of the following governance failures does this BEST demonstrate?
- A. Absence of metrics and dashboards for analysts
- B. Excessive reliance on external consultants for model design
- C. Insufficient model validation and change control processes
- D. Lack of sufficient computing resources for the AI system
Answer: C
Explanation:
AAISM states that AI risk signals, thresholds, and model logic must be governed through strict validation and change control processes. Disabling key risk indicators without formal review or testing directly reflects a failure in:
* AI model validation
* Change management
* Governance oversight
This aligns precisely with option D.
Lack of dashboards (C) affects monitoring but does not explain disabled risk signals. Computing resources (A) would not cause intentional disabling. Reliance on consultants (B) is not connected to improper internal model changes.
References: AAISM Study Guide - AI Governance; Model Validation and Change Control Failures.
NEW QUESTION # 87
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. Data leakage
- D. Lack of monitoring
Answer: C
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 # 88
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. Restrict all inputs containing special characters
- B. Assess the business impact of known threats
- C. Deploy real-time logging and monitoring
- D. Implement a static threshold limiting LLM outputs
Answer: B
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 # 89
An organization is planning to commission a third-party AI system to make decisions using sensitive data.
Which of the following metrics is MOST important for the organization to consider?
- A. Accuracy thresholds
- B. Accessibility rating
- C. Model response time
- D. Service availability
Answer: A
Explanation:
When AI systems make consequential decisions over sensitive data, AAISM requires explicit performance thresholds tied to decision quality-i.e., accuracy (and related error/false-rate limits) aligned to business risk appetite and regulatory expectations. Availability and latency are important service metrics, but decision integrity and error bounds are primary risk drivers in sensitive contexts. Establishing, monitoring, and enforcing minimum accuracy thresholds (with subgroup performance checks) is essential to reduce harm, ensure fairness/compliance, and support auditability.
References:* AI Security Management (AAISM) Body of Knowledge: Risk-aligned performance metrics; decision quality thresholds; harm and error-rate governance in sensitive processing.* AI Security Management Study Guide: Metric selection for high-risk AI; accuracy, false positive/negative limits, and acceptance criteria tied to business controls.
NEW QUESTION # 90
AI developers often find deep learning systems difficult to explain PRIMARILY because:
- A. Training data is spread across public domains
- B. Neural network architectures include statistical methods not fully understood
- C. Knowledge dynamically changes without logs
- D. Algorithms rely on probability theories
Answer: B
Explanation:
AAISM notes that deep learning systems lack transparency due to complex neural architectures, where internal representations are statistical, nonlinear, and not directly interpretable.
While probability (C) and data sourcing (D) contribute to opacity, the root cause is the intrinsic complexity and opacity of deep neural networks.
References: AAISM Study Guide - Explainability Challenges in Deep Learning.
NEW QUESTION # 91
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 # 92
AI developers often find it difficult to explain the processes inside deep learning systems PRIMARILY because:
- A. Neural network architectures can include statistical methods that are not fully understood
- B. Applied algorithms are based on probability theories to improve system performance
- C. Training data input for learning is spread throughout the public domain and continues to change
- D. Generated knowledge dynamically changes in memory without being tracked by change history logs
Answer: A
Explanation:
Deep learning models learn high-dimensional, non-linear representations through layered parameterization that resists simple causal narratives. The internal mechanisms (e.g., distributed feature representations and complex statistical transformations) are difficult to map to human-interpretable rules, making explanation challenging. This is the primary reason for explainability difficulty in deep learning.
Option A addresses data origin/volatility, not explainability. Option B mischaracterizes model behavior as mutable "knowledge" without logs. Option C notes probabilistic foundations but that alone does not make systems inexplicable.
References: AI Security Management™ (AAISM) Body of Knowledge: "Explainability and Interpretability- Complexity in Deep Learning," "Model Behavior, Surrogates, and Post-hoc Explanations"; AAISM Study Guide: "Interpreting High-Dimensional Representations," "Limits of Transparency in Neural Architectures."
NEW QUESTION # 93
An organization has implemented a natural language processing model to respond to customer questions when personnel are not available. A pre-implementation security assessment revealed attackers could access sensitive company data through a chat interface injection attack. Which of the following is the BEST way to prevent this attack?
- A. Manually reviewing AI model outputs
- B. Conducting regular information security audits
- C. Ensuring continuous monitoring and data tagging
- D. Implementing input validation and templates
Answer: D
Explanation:
To prevent prompt/interface injection, AAISM prioritizes preventive technical controls at the boundary: input validation/sanitization, structured templates/system prompts, allow/deny lists, and context isolation. These measures constrain user-supplied content and block adversarial instructions from being interpreted as system directives. Monitoring (A) and audits (D) are detective/assurance activities; manual output review (B) is compensating but less scalable and does not prevent injection.
References: AI Security Management™ (AAISM) Body of Knowledge - Secure Prompting & Input Controls; Interface Injection Mitigations; Context and Instruction Isolation Patterns.
NEW QUESTION # 94
Which AI data management technique involves creating validation and test data?
- A. Annotating
- B. Training
- C. Splitting
- D. Learning
Answer: C
Explanation:
AAISM describes data splitting as the process of dividing datasets into:
* training
* validation
* test sets
This is essential for reducing overfitting and ensuring robust evaluation.
Learning (A) refers to model training. Annotating (D) labels data. Training (C) does not create validation/test data.
References: AAISM Study Guide - AI Data Preparation & Dataset Splitting.
NEW QUESTION # 95
An organization plans to leverage AI in the software development process to speed up coding. Which of the following should the information security manager do FIRST?
- A. Update the security policy to include AI controls
- B. Conduct an impact assessment
- C. Perform a cost-benefit analysis
- D. Train developers to verify AI output
Answer: B
Explanation:
AAISM guidance specifies that before introducing AI into any business or technical workflow, an AI Impact Assessment must be conducted early to determine potential risks, privacy implications, misuse scenarios, governance gaps, and required security controls. This aligns with the principle that AI adoption must begin with governance and risk identification, not training or policy modification.
Training developers (B) is important but occurs after identifying risks. Updating policies (C) is also downstream of the assessment. Cost-benefit analysis (D) supports business justification but does not address security.
References: AAISM Study Guide - AI Governance; Impact Assessment Required Before Deployment.
NEW QUESTION # 96
Which attack type is MOST likely to cause model drift?
- A. Model stealing
- B. Membership inference
- C. Data poisoning
- D. Perfect knowledge
Answer: C
Explanation:
AAISM defines data poisoning as directly capable of causing model drift because corrupted training data shifts the statistical distribution, leading to degraded or unsafe performance.
Model stealing (A) extracts model behavior but does not cause drift. Perfect knowledge (B) is an attacker capability, not an attack causing drift. Membership inference (D) attacks privacy, not performance.
References: AAISM Study Guide - Model Drift Causes; Data Poisoning Impact.
NEW QUESTION # 97
Which of the following BEST describes the role of transparency in AI?
- A. Publishing AI mechanisms, data sources, and decision-making processes while making them openly available
- B. Explaining the AI system in an understandable and logical way so reasons for decisions can be given
- C. Talking through a decision tree to better understand how the algorithm made each of its choices
- D. Persuading someone that the AI tool in use is beneficial and operates as expected
Answer: B
Explanation:
Transparency in AI is a governance principle requiring that systems be explainable to stakeholders in ways that are understandable and meaningful, enabling clear articulation of how decisions were reached and why.
Within an AI program, transparency supports accountability, auditability, and trust by ensuring that reasons for decisions can be communicated and scrutinized. Option C reflects this definition by focusing on intelligible, logical explanations of system behavior and decision rationale.
Option A is a narrow technique (model-specific interpretability for decision trees) and does not capture transparency as a broad governance requirement. Option B conflates transparency with full public disclosure; transparency does not require making all artifacts openly available. Option D is persuasion/advocacy, not transparency.
References: AI Security Management™ (AAISM) Body of Knowledge: "AI Governance-Transparency and Explainability," "Accountability and Assurance"; AAISM Study Guide: "Explainability Objectives and Stakeholder Communication," "Documentation for Decision Rationale."
NEW QUESTION # 98
An organization needs large data sets to perform application testing. Which of the following would BEST fulfill this need?
- A. Performing AI data augmentation
- B. Incorporating data from search content
- C. Using open-source data repositories
- D. Reviewing AI model cards
Answer: C
Explanation:
According to AAISM study guidance, the most direct and effective way to obtain large volumes of diverse data for application testing is through open-source data repositories. These repositories provide freely available, well-documented, and often standardized data that supports testing and benchmarking in a compliant manner. Model cards document AI behavior but do not provide data. Incorporating search content may introduce legal, privacy, and quality risks. Data augmentation is useful for expanding existing sets but does not provide the breadth or size required when starting with insufficient data. The recommended best practice for sourcing large testing datasets is therefore the use of open-source repositories.
References:
AAISM Study Guide - AI Technologies and Controls (Data Sources and Testing Practices) ISACA AI Security Management - Data Governance and Compliance in AI Testing
NEW QUESTION # 99
Which of the following BEST ensures AI components are validated as part of disaster recovery testing?
- A. Monitoring model performance metrics during failover and recovery to assess system stability
- B. Simulating denial of service (DoS) attacks against AI APIs to evaluate detection capabilities
- C. Disconnecting primary model training clusters to test retraining workflow during extended outages
- D. Running simulated data loss scenarios by erasing test records from the AI system's feature store
Answer: A
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 # 100
The PRIMARY reason to conduct a privacy impact assessment (PIA) on an AI system is to:
- A. Determine whether personal data is poisoned
- B. Build customer confidence
- C. Identify applicable regulations
- D. Analyze how personal data is handled
Answer: D
Explanation:
According to AAISM privacy governance guidance, the primary reason for conducting a PIA is to analyze how personal data is collected, processed, shared, and retained by an AI system. This analysis ensures compliance with privacy laws, mitigates risks to individuals, and informs necessary safeguards. Identifying regulations is part of compliance but is secondary to analyzing actual data handling. Building customer confidence is an outcome, not the main purpose. Checking for poisoned data relates to data quality, not privacy assessment. The fundamental purpose of a PIA is therefore to analyze the handling of personal data.
References:
AAISM Study Guide - AI Governance and Program Management (Privacy Impact Assessments) ISACA AI Security Management - Data Handling and Privacy Risk
NEW QUESTION # 101
An organization is updating its vendor arrangements to facilitate the safe adoption of AI technologies. Which of the following would be the PRIMARY challenge in delivering this initiative?
- A. Inability to sufficiently identify shadow AI within the organization
- B. Unwillingness of large AI companies to accept updated terms
- C. Failure to adequately assess AI risk
- D. Insufficient legal team experience with AI
Answer: B
Explanation:
In the AAISM guidance, vendor management for AI adoption highlights that large AI providers often resist contractual changes, particularly when customers seek to impose stricter security, transparency, or ethical obligations. The official study materials emphasize that while organizations must evaluate AI risk and build internal expertise, the primary challenge lies in negotiating acceptable contractual terms with dominant AI vendors who may not be willing to adjust their standardized agreements. This resistance limits the ability of organizations to enforce oversight, bias controls, and compliance requirements contractually.
References:
AAISM Exam Content Outline - AI Risk Management
AI Security Management Study Guide - Third-Party and Vendor Risk
NEW QUESTION # 102
Which of the following is MOST important to ensure security throughout the AI data life cycle?
- A. Conducting periodic data reviews
- B. Maintaining a complete inventory with data lineage records
- C. Restricting use of data in third-party models
- D. Leveraging selected open-source models
Answer: B
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.
Periodic reviews (B) are necessary but insufficient without lineage. Restricting third-party use (C) is one control but not comprehensive. Open-source model choice (A) does not secure data.
References: AAISM Study Guide - AI Data Governance; Lineage and Traceability.
NEW QUESTION # 103
A data scientist creating categories and training an algorithm on large data sets is performing which learning technique?
- A. Reinforcement
- B. Supervised
- C. Machine learning (ML)
- D. Unsupervised
Answer: B
Explanation:
AAISM identifies supervised learning as involving:
* labeled categories
* ground-truth datasets
* model training with known outcomes
This aligns exactly with categorizing data and training on labeled datasets.
Reinforcement (B) involves reward feedback loops. Unsupervised (C) uses unlabeled data. "Machine learning" (D) is too broad and not a specific technique.
References: AAISM Study Guide - AI Learning Types; Supervised Learning Definition.
NEW QUESTION # 104
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