[Apr 10, 2026] AAISM Exam Dumps, AAISM Practice Test Questions
Free AAISM Study Guides Exam Questions and Answer
ISACA AAISM Exam Syllabus Topics:
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NEW QUESTION # 101
Which of the following reviews MUST be conducted as part of an AI impact assessment?
- A. Evaluation of model reproducibility
- B. Testing, evaluation, validation, and verification
- C. Identification of environmental and societal consequences
- D. Security control self-assessment (CSA)
Answer: C
Explanation:
An AI impact assessment is a governance instrument that must address potential impacts on people and society, including environmental and societal consequences. This review determines downstream effects (e.g., fairness, safety, rights, sustainability) before and during deployment, supporting accountability and compliance. While TEVV activities (testing, evaluation, validation, verification) and security control self- assessments are integral to assurance and security management, the defining obligation of an impact assessment is to evaluate potential societal and environmental outcomes and associated mitigations. Model reproducibility is a technical quality attribute but is not the mandatory core of an impact assessment.
References:* AI Security Management (AAISM) Body of Knowledge: Impact Assessment - governance requirements for societal, environmental, and stakeholder impact review* AI Security Management Study Guide: AI impact assessment scope, stakeholder impact analysis, and documentation of societal and environmental consequences
NEW QUESTION # 102
Which of the following is the MOST likely cause of model drift?
- A. Membership inference
- B. Model stealing
- C. Perfect knowledge
- D. Data poisoning
Answer: D
Explanation:
Model drift occurs when the statistical properties of input data and/or the relationship between features and outcomes change over time, causing degraded model performance. The AAISM guidance classifies data- centric causes (distribution shift, concept drift, and contamination) as the primary drivers and highlights that malicious contamination of training or incremental learning data (data poisoning) is a direct, high- likelihood driver of observable drift in production because it changes the effective data-generating process the model learns from. In contrast:
* Perfect knowledge is an attacker capability descriptor, not a drift cause.
* Membership inference targets privacy of the training set and does not inherently shift data distributions.
* Model stealing targets IP/confidentiality; it does not change the victim model's data distribution or decision boundary in situ.
References:* AI Security Management (AAISM) Body of Knowledge: Model Risk & Drift; Data Integrity Risks; Adversarial ML-Poisoning vs. Evasion* AAISM Study Guide: Production Monitoring & Drift Management; Risk Scenarios-Data Poisoning Impacts and Controls* AAISM Mapping to Standards:
Lifecycle Risk Treatment-Robustness to Data Contamination; Continuous Monitoring and Feedback
NEW QUESTION # 103
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. Ensure vendors disclose how the application uses the organization's data
- B. Assess the vendor's publicly available AI usage policy
- C. Initiate discussions between the organization's and the vendor's legal teams
- D. Mandate an AI security audit by an external auditor before procurement
Answer: A
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. An external audit (A) can be useful but may be costly and not always feasible pre-procurement. Legal discussions (B) are important but ineffective without clarity on data flows. Publicly available policies (D) are often high- level and marketing-oriented, lacking the specificity required for proper risk evaluation. Therefore, obtaining explicit data usage disclosures from the vendor is the most effective starting point.
References: AI Security Management™ (AAISM) Study Guide - Third-Party AI Risk and Data Sharing; Vendor Governance Requirements.
NEW QUESTION # 104
Which of the following is the MOST important consideration for an organization that has decided to adopt AI to leverage its competitive advantage?
- A. Develop a business case for the procurement of AI monitoring tools
- B. Develop internal training programs on AI governance, risk, and compliance (GRC)
- C. Develop a comprehensive risk management process to address AI-related issues
- D. Develop a comprehensive strategic roadmap for AI integration
Answer: D
Explanation:
AAISM's governance guidance emphasizes that adopting AI for competitive advantage must begin with a comprehensive strategic roadmap for integration. This roadmap aligns AI adoption with business objectives, sets priorities, defines milestones, and ensures coordination across functions. Risk management, training, and tool procurement are essential, but they are tactical steps that follow once the strategic direction is defined.
Without a roadmap, adoption becomes fragmented and risks misalignment with business strategy. The most important consideration at the adoption stage is therefore creating a strategic integration roadmap.
References:
AAISM Exam Content Outline - AI Governance and Program Management (Strategy and Roadmapping) AI Security Management Study Guide - Business Alignment of AI Initiatives
NEW QUESTION # 105
When using AI as part of incident response, which of the following BEST ensures the automation aligns with regulatory and governance obligations?
- A. Apply anomaly detection models to filter incoming threats and automate containment
- B. Implement a tiered automation strategy where severity ratings inform the need for human oversight
- C. Use deep learning models to autonomously classify all incidents
- D. Train the AI incident response platform to mirror legacy response workflows and log containment
Answer: B
Explanation:
AAISM prescribes risk-based, human-in-the-loop orchestration for safety-critical or regulated actions. A tiered automation strategy that gates autonomy by incident severity, data sensitivity, and regulatory requirements ensures accountability, auditability, and proportionality, satisfying governance obligations. Full autonomy (A) risks non-compliance; simply mirroring legacy workflows (B) may not meet current obligations; broad auto-containment (C) lacks necessary oversight controls.
References: AI Security Management™ (AAISM) Body of Knowledge - Governance of AI-Driven Security Automation; Human Oversight and Escalation; Risk-Based Orchestration. AAISM Study Guide - Incident Response with AI: Controls, Approvals, and Auditability.
NEW QUESTION # 106
Which of the following mitigation control strategies would BEST reduce the risk of introducing hidden backdoors during model fine-tuning via third-party components?
- A. Leveraging open-source models and packages
- B. Disabling runtime logs during model training
- C. Implementing unsupervised learning methods
- D. Performing threat modeling and integrity checks
Answer: D
Explanation:
The most effective way to reduce the risk of hidden backdoors entering during fine-tuning via third-party components is to apply supply-chain aware threat modeling and integrity verification across data, code, models, and dependencies. This includes SBOM/MBOM review, cryptographic signing and hash verification, controlled provenance of datasets and model weights, dependency pinning, secure artifact repositories, and pre-deployment security testing (including backdoor scans and evals). Merely preferring open-source (Option B) does not guarantee integrity; learning paradigm changes (Option C) are unrelated to supply-chain risk; and disabling logs (Option D) reduces forensic visibility and increases risk.
References:
AAISM Body of Knowledge: Secure AI Supply Chain; Model Provenance, Integrity and SBOM/MBOM Controls; Pre-deployment Security Testing and Backdoor/Poisoning Evals.
AAISM Study Guide: AI Threat Modeling (Attack Surfaces in Training/Fine-tuning); Third-Party/Vendor Component Assurance; Cryptographic Integrity and Artifact Governance.
NEW QUESTION # 107
When deriving statistical information from AI systems, which source of risk is MOST important to address?
- A. Incomplete outputs
- B. Lack of data normalization
- C. Presence of hallucinations
- D. Systemic bias in data sets
Answer: D
Explanation:
AAISM emphasizes systemic or structural bias as a high-impact risk because biased data leads directly to discriminatory insights or decisions when used for analytics or reporting. This risk affects fairness, compliance, and organizational reputation.
Hallucinations (A) relate more to generative AI. Incomplete outputs (B) affect accuracy but not structural fairness. Lack of normalization (C) affects performance but is not the dominant risk.
References: AAISM Study Guide - AI Bias, Fairness, and Ethical Risk.
NEW QUESTION # 108
Which of the following controls BEST mitigates the risk of bias in AI models?
- A. Regular data reconciliation
- B. Diverse data sourcing strategies
- C. Cryptographic hash functions
- D. Robust access control techniques
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 # 109
A school district contracts a third-party provider for AI-based curriculum recommendations. Which of the following is the BEST way to ensure the vendor uses AI responsibly?
- A. Ensuring the vendor offers 24/7 technical support
- B. Confirming the AI solution supports single sign-on (SSO)
- C. Requiring the vendor to provide the model card
- D. Verifying the vendor has updated terms of service
Answer: C
Explanation:
AAISM emphasizes transparency artifacts from vendors to enable due diligence and assurance. A model card documents intended use, data sources, limitations, performance across subgroups, known risks, and evaluation procedures-information necessary to assess safety, fairness, and compliance for sensitive contexts like education. SSO and support are useful operational features; generic ToS updates are insufficient without model-specific disclosures.
References: AI Security Management™ (AAISM) Body of Knowledge - Third-Party & Supply Chain Governance; Transparency Artifacts (Model Cards, Datasheets). AAISM Study Guide - Vendor Due Diligence Requirements; Documentation for Risk, Fairness, and Intended Use.
NEW QUESTION # 110
Which of the following is the GREATEST benefit of implementing an AI tool to safeguard sensitive data and prevent unauthorized access?
- A. Reduced need for data classification
- B. Reduced number of false positives
- C. Timely analysis of endpoint activities
- D. Timely initiation of incident response
Answer: B
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 # 111
A global organization has experienced multiple incidents of staff copying confidential data into public chatbots and acting on the model outputs. Which of the following is MOST important to reduce short-term risk when launching an AI security awareness initiative?
- A. Publishing an AI acceptable use policy and collecting e-signatures of employees
- B. Blocking access to public large language models (LLMs) at the network perimeter
- C. Requiring employees to complete an annual generic phishing and deepfake awareness module
- D. Delivering role-based and scenario-driven AI security training mapped to policy and job functions
Answer: D
Explanation:
AAISM prescribes targeted, role-based, scenario-driven training aligned to policy and job tasks as the highest-impact near-term intervention for human-factor AI risks. By mapping concrete "do/don't" behaviors (e.g., what data may/may not be pasted into public chatbots, required redaction steps, approved tools, verification of outputs) to specific roles, organizations rapidly reduce incident likelihood and harmful actions.
* A (blocking) is a technical containment option but is not an awareness-initiative control and may cause workarounds; AAISM treats it as complementary, not a substitute for behavior change.
* B generic modules fail to address the specific misuse pattern.
* D signatures provide attestations without ensuring comprehension or changed behavior.
References:* AI Security Management (AAISM) Body of Knowledge: Human-centric Controls-Role- based training, policy-to-practice mapping, and scenario exercises for rapid risk reduction.* AI Security Management Study Guide: Awareness program design for generative AI misuse; behavior-anchored training outcomes.
NEW QUESTION # 112
An organization's CIO provided the AI steering committee with a list of AI technologies in use and tasked them with categorizing the technologies by risk. Which of the following should the committee do FIRST?
- A. Ensure the AI technologies are included in the asset inventory
- B. Identify vulnerabilities related to the technologies in use
- C. Begin grouping similar AI products and solutions together
- D. Assess risk levels based on risk appetite and regulatory requirements
Answer: A
Explanation:
AAISM governance practices state that before categorizing technologies by risk, the first step is to ensure that all AI systems are documented in the organizational asset inventory. A complete inventory provides the foundation for subsequent risk analysis, accountability, and governance. Grouping solutions, identifying vulnerabilities, and assessing risk levels come afterward, once inventory accuracy is established. Without confirming that the technologies are recorded in the inventory, risk categorization may miss critical assets.
References:
AAISM Study Guide - AI Governance and Program Management (AI Inventories as a Prerequisite to Risk Analysis) ISACA AI Security Management - Asset Visibility and Risk Categorization
NEW QUESTION # 113
When evaluating a third-party AI service provider, which of the following master services agreement provisions is MOST critical for managing security risk?
- A. Sharing real-time log information
- B. Restricting query volume thresholds
- C. Guaranteeing unlimited model retraining requests
- D. Prohibiting the use of customer data for model training
Answer: D
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 # 114
Which AI model is BEST suited to ensure explainability in an HR department's pre-screening tool for candidate resumes?
- A. Support vector machine
- B. Neural network
- C. Decision tree
- D. Gradient boosting machine
Answer: C
Explanation:
According to AAISM, decision trees provide the highest explainability because their structure clearly shows how inputs map to decisions. This is essential in HR applications subject to fairness, bias, and compliance requirements.
SVMs (A) and gradient boosting (D) are less interpretable. Neural networks (B) are explicitly listed as low- explainability models.
References: AAISM Study Guide - Explainability and Transparency Requirements; Interpretable ML Models.
NEW QUESTION # 115
Which of the following is the MOST important consideration when an organization is adopting generative AI for personalized advertising?
- A. Regulatory risk
- B. Fraud risk
- C. Commercial risk
- D. Reputational risk
Answer: A
Explanation:
In AAISM, usage of AI for activities involving personal data and profiling, such as personalized advertising, is explicitly mapped to stringent regulatory and compliance requirements (e.g., data protection, consent, profiling limitations, fairness obligations). The material notes that these activities may trigger "heightened regulatory scrutiny, mandatory impact assessments, and potential penalties for non-compliance." While reputational (B), fraud (A), and commercial (C) risks are all relevant, the primary, non-optional constraint is compliance with applicable regulations governing personal data, automated profiling, and targeted content.
Failure in this area can lead not only to reputational harm but also to legal sanctions, enforced remediation, and operational restrictions. Therefore, regulatory risk is identified as the most important consideration when deploying generative AI for personalized advertising.
References: AI Security Management™ (AAISM) Study Guide - AI, Privacy, and Regulatory Compliance; High-Risk Use Cases and Profiling.
NEW QUESTION # 116
Within an incident handling process, which of the following would BEST help restore end user trust with an AI system?
- A. The AI model prioritizes incidents based on business impact
- B. Remediation of the AI system based on lessons learned
- C. AI is being used to monitor incident detection and alerts
- D. The AI model's outputs are validated by team members
Answer: D
Explanation:
Restoring end user trust during incident handling requires visible, immediate assurance that system outcomes are safe and appropriate. AAISM prescribes human oversight and approval gates for high-risk AI decisions, with human validation of outputs before use as a primary control to maintain trust while technical remediation is underway. Prioritization (A) and monitoring (B) aid operations but do not directly rebuild user confidence in outcomes. Post-incident improvements (D) are essential for long-term assurance but do not provide the immediate trust restoration that supervised, human-validated outputs deliver.
References: AI Security Management™ (AAISM) Body of Knowledge - Incident Handling & Communications; Human Oversight and Approval Gates; Trust Restoration During AI Incidents.
NEW QUESTION # 117
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