Current AAISM Exam Dumps [2025] Complete ISACA Exam Smoothly
AAISM Premium PDF & Test Engine Files with 257 Questions & Answers
ISACA AAISM Exam Syllabus Topics:
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NEW QUESTION # 92
What BEST ensures a proper business continuity plan (BCP) for an AI solution?
- A. Enhancing monitoring for model failure
- B. Increasing backup restoration detail
- C. Implementing access controls
- D. Testing AI infrastructure failover mechanisms
Answer: D
Explanation:
AAISM defines an AI BCP as requiring validated failover and recovery testing of AI components, including:
* model hosting environments
* model-serving APIs
* feature stores
* inference pipelines
Only Option B tests actual continuity of AI functionality.
Monitoring (A) detects issues but does not ensure continuity. Access controls (C) relate to security, not continuity. Backup detail (D) is insufficient without recovery testing.
References: AAISM Study Guide - AI Resilience and Disaster Recovery Validation.
NEW QUESTION # 93
Which of the following is the MAIN objective of the operational phase of AI life cycle management?
- A. Align the model to business needs
- B. Optimize the model's algorithms
- C. Obtain end-user feedback
- D. Monitor model performance
Answer: D
Explanation:
AAISM identifies the operational phase as the stage where the AI system is actively deployed and requires continuous monitoring of performance, drift, security, and reliability.
Business alignment (B) belongs to planning and design. Optimization (A) and user feedback (D) are part of development or improvement cycles, not primary operational objectives.
References: AAISM Study Guide - AI Life Cycle Management; Operational Monitoring Requirements.
NEW QUESTION # 94
Which of the following is the MOST important consideration when deciding how to compose an AI red team?
- A. Time-to-market constraints
- B. Resource availability
- C. Compliance requirements
- D. AI use cases
Answer: D
Explanation:
AAISM materials specify that the composition of an AI red team must be tailored to the organization's AI use cases. The purpose of red-teaming is to simulate realistic adversarial conditions aligned with the actual applications of AI. For example, testing a generative model requires different expertise than testing a fraud detection system. While resource availability, compliance requirements, and time-to-market pressures are practical considerations, they are secondary to aligning team expertise with use case scenarios. The most important factor is therefore the AI use cases themselves.
References:
AAISM Exam Content Outline - AI Risk Management (Red Teaming Considerations) AI Security Management Study Guide - Tailoring Adversarial Testing to Use Cases
NEW QUESTION # 95
An organization is designing an AI-based credit risk assessment system integrating sensitive financial data.
Which option BEST supports security-by-design?
- A. Segmenting AI services across containers
- B. Restricting access to AI models using IP allow lists
- C. Integrating differential privacy mechanisms into model training
- D. Applying threat modeling specific to AI components before deployment
Answer: D
Explanation:
AAISM identifies AI-specific threat modeling as an essential early-stage control in security-by-design, particularly for high-risk systems like credit scoring. It systematically identifies:
* data poisoning
* bias vulnerabilities
* model evasion
* model extraction
* misuse scenarios
Differential privacy (A) is powerful but is a mitigation, not the overarching design control. Segmentation (C) and IP allow lists (D) are supporting controls but not the foundational step in secure design.
References: AAISM Study Guide - Security-by-Design; AI Threat Modeling.
NEW QUESTION # 96
An attack has occurred on an AI system that has been in use for two years. Which of the following would BEST mitigate the impact of the attack?
- A. Monitoring AI systems for suspicious activities
- B. Replacing the AI model with a new model that hides confidence levels
- C. Implementing strict access controls to the model's architecture
- D. Updating deployed training data with new adversarial data
Answer: D
Explanation:
When an AI system experiences an attack after being in production for an extended period, the most effective mitigation strategy is to update the deployed training data with new adversarial data. This process strengthens the model's resilience by retraining it to recognize and resist attack vectors that were previously unknown or unaccounted for. According to the AI Security Management™ (AAISM) framework, risk mitigation for AI systems must address model robustness through adversarial retraining, data quality improvement, and model lifecycle hardening rather than relying solely on reactive measures.
Why Option B is Correct:
* Incorporating adversarial examples into the training set enhances the system's ability to correctly classify and withstand malicious inputs.
* This approach directly mitigates the vulnerability exploited in the attack and supports a proactive, continuous risk management cycle.
Why Other Options Are Incorrect:
* Option A: Monitoring helps detect suspicious activity but does not resolve the underlying vulnerability.
* Option C: Concealing confidence scores may reduce model transparency but does not address the attack mechanism or its root cause.
* Option D: Implementing access controls protects the model's architecture but does not improve model robustness against input manipulation attacks.
Exact Extract from Official AAISM Study Guide:
"AI risk management requires continuous improvement following incidents. After an adversarial or data poisoning event, the preferred risk treatment involves retraining the model using adversarial data and updated datasets to enhance robustness. This ensures the AI model adapts to evolving threat landscapes rather than merely restricting access or obscuring outputs." References:
AI Security Management™ (AAISM) Body of Knowledge: AI Risk Treatment and Mitigation Strategies, Adversarial Robustness and Resilience Engineering.
AI Security Management™ Study Guide: Model Lifecycle Security, Continuous Risk Treatment through Adversarial Retraining.
ISO/IEC 23894:2023, Clause 8.3.2 - Risk treatment through robustness improvement and adversarial data inclusion.
NEW QUESTION # 97
An organization is adopting an agentic AI solution from an external vendor to support its internal IT operations. To evaluate the security posture of this system, which of the following provides the MOST reliable and independently verifiable evidence of implemented security controls?
- A. Third-party audit reports
- B. Internal red team testing reports
- C. Industry benchmarking peer review
- D. General AI security whitepapers
Answer: A
Explanation:
Third-party audit reports provide independent assurance that the vendor's stated controls are designed and operating effectively against recognized criteria. Such attestations (e.g., audit/assurance frameworks) are traceable, repeatable, and verifiable, and they support supply-chain risk reviews and contractual assurance.
Internal red-team reports are not independent, industry "peer reviews" are not control attestations, and whitepapers are marketing/educational materials without evidence of control operation.
References: AI Security Management (AAISM) Body of Knowledge: Third-Party & Supply-Chain Assurance; Independent Assessment and Audit Evidence; Vendor Risk Management. AAISM Study Guide:
Assurance Artifacts for AI Services; Evaluating External AI Providers.
NEW QUESTION # 98
Which of the following BEST ensures the integrity of data sets used to train AI models?
- A. Appropriate storage of data sets according to documented classification processes
- B. Tracking and verification of data sets via cryptographic controls
- C. Collection and retention of only necessary data sets
- D. Clear documentation of data sources, types used, and processing steps
Answer: B
Explanation:
AAISM defines cryptographic tracking and verification as the best control for ensuring the integrity of training data. By applying hashing and verification methods, organizations can confirm that datasets remain unaltered and authentic throughout collection, storage, and processing. Collecting only necessary data, proper storage, or clear documentation all support governance and compliance, but they do not guarantee that the data has not been tampered with. Integrity is specifically ensured by cryptographic verification techniques.
References:
AAISM Exam Content Outline - AI Risk Management (Data Integrity and Protection) AI Security Management Study Guide - Cryptographic Controls for Dataset Integrity
NEW QUESTION # 99
During the deployment of a generative AI platform, a risk assessment highlighted threats such as data leakage and prompt manipulation. Which of the following is the BEST way to ensure appropriate control selection?
- A. Apply AI-specific controls from external frameworks without customization and initiate monitoring to expedite compliance
- B. Postpone control selection until deployment and address risk through enhanced monitoring
- C. Map identified AI threats to enterprise control catalogs and integrate AI-specific safeguards where gaps exist
- D. Rely primarily on vendor-provided security features and seek third-party certifications
Answer: C
Explanation:
AAISM requires that control selection be threat-led and context-specific, aligning AI threats to the organization's existing enterprise control catalogs (security, privacy, resilience) and augmenting them with AI- specific safeguards where coverage is insufficient. This ensures consistency with the risk appetite, removes duplication, and closes AI-unique gaps (e.g., prompt injection, data leakage from context windows, model misuse). Generic reliance on vendors or uncustomized external frameworks does not ensure fit-for-purpose coverage, and deferring control selection to post-deployment contradicts proactive risk treatment.
References: AI Security Management™ (AAISM) Body of Knowledge - Governance & Program Controls; Control Selection and Tailoring; Threat-to-Control Mapping for AI Systems; Risk Appetite & Control Assurance Alignment.
NEW QUESTION # 100
A health services organization is developing a proprietary generative AI chatbot to assist patients with medical devices. Which of the following should be the organization's HIGHEST priority?
- A. Maximizing the amount of training data
- B. Maximizing neural network size
- C. Tuning algorithms used in the AI model
- D. Selecting the appropriate training data
Answer: D
Explanation:
AAISM prioritizes training data suitability-lawful sourcing, provenance, quality, representativeness, and safety-especially in health-related applications. The correctness and appropriateness of training data determine clinical safety, reduction of harmful outputs, and compliance with data protection/sector obligations. Larger models or more data do not compensate for inappropriate or low-quality datasets; tuning is secondary to ensuring the right data with rigorous curation, labeling quality, and guardrails aligned to patient safety requirements.
References:* AI Security Management™ (AAISM) Body of Knowledge: Data Governance & Quality; High- Risk/Health Context Controls; Safety & Harm Minimization* AAISM Study Guide: Data Provenance & Suitability, Domain-Specific Dataset Controls; Compliance-by-Design for Sensitive Sectors
NEW QUESTION # 101
When evaluating a third-party AI service provider, which master services agreement (MSA) provision is MOST critical for managing security risk?
- A. Sharing real-time log information
- B. Restricting query volume thresholds
- 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 # 102
A preliminary risk assessment of a SaaS-based large language model (LLM) business support system has identified prompt injection, data poisoning, and model exfiltration as material threats. Which of the following is the BEST approach to ensure risks are treated consistently?
- A. Applying control baselines from a recognized industry standard to AI components
- B. Implementing an AI threat control matrix that maps threats to specific controls and assurance activities
- C. Focusing resources on post-deployment red teaming and deferring control selection until post go-live feedback is received
- D. Relying on vendor independent audit reports and service level agreements (SLAs) as evidence of AI risk coverage
Answer: B
Explanation:
AAISM prescribes building and maintaining an AI Threat-Control Matrix to ensure consistent, repeatable risk treatment. The matrix traces each material threat (e.g., prompt injection, poisoning, exfiltration) to named controls, test/evidence procedures, and assurance owners across the lifecycle. Baselines and vendor attestations can support assurance but are insufficient alone; deferring control selection until after deployment conflicts with AAISM's proactive treatment principle.
References: AI Security Management (AAISM) Body of Knowledge - AI Risk Treatment Planning; Threat-Control Traceability; Assurance & Evidence Management for AI Systems.
NEW QUESTION # 103
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 cost of noncompliance was not determined
- B. The AI tool is widely used within the industry
- C. The AI tool is regularly audited
- 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 # 104
Which of the following BEST ensures AI components are validated as part of disaster recovery testing?
- A. Disconnecting primary model training clusters to test retraining workflow during extended outages
- B. Simulating denial of service (DoS) attacks against AI APIs to evaluate detection capabilities
- C. Running simulated data loss scenarios by erasing test records from the AI system's feature store
- D. Monitoring model performance metrics during failover and recovery to assess system stability
Answer: D
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 # 105
A military contractor discovered that its large language model (LLM) is at high risk of being targeted by advanced persistent threat (APT) actors seeking to exploit the model to access confidential information.
Which of the following attacks is the HIGHEST priority to protect against?
- A. Model inversion
- B. Model distillation
- C. Unauthorized tuning
- D. Data poisoning
Answer: A
Explanation:
AAISM classifies model inversion as a privacy/information-leakage threat where adversaries infer or reconstruct sensitive training data or attributes from model outputs-directly jeopardizing confidential information targeted by APTs. While data poisoning, unauthorized tuning, and model distillation present material risks (integrity, governance/IP theft), the scenario's stated objective-accessing confidential information-most directly maps to inversion. Accordingly, AAISM prioritizes defenses such as output regularization, confidence suppression/calibration, overfitting controls, privacy-preserving techniques, and strict access/telemetry on inference interfaces.
References:* AI Security Management (AAISM) Body of Knowledge: Model Security-Inference-Time Threats (Inversion, Membership Inference) and Confidentiality Risks* AAISM Study Guide: Leakage Mitigations-Regularization, Output Minimization/Calibration, Access Controls & Monitoring on Model Interfaces
NEW QUESTION # 106
Which of the following is the MOST effective way to mitigate the risk of deepfake attacks?
- A. Using a general-purpose large language model (LLM) to detect fraud
- B. Relying on human judgment for oversight
- C. Validating the provenance of the data source
- D. Limiting employee access to AI tools
Answer: C
Explanation:
AAISM study content identifies validating the provenance of data sources as the most effective way to counter deepfake risks. Provenance validation ensures that content is authentic, verifiable, and traceable, preventing malicious synthetic media from being trusted as legitimate. Human oversight helps but cannot reliably detect sophisticated fakes. Limiting tool access reduces exposure but does not prevent external attacks. General-purpose LLMs are not optimized for fraud detection. The strongest control is verifying the origin and authenticity of data before acceptance.
References:
AAISM Study Guide - AI Risk Management (Deepfake and Content Integrity Risks) ISACA AI Security Management - Provenance Validation as a Defense
NEW QUESTION # 107
Which of the following methods provides the MOST effective protection against model inversion attacks?
- A. Implementing regularization output
- B. Reducing the model's complexity
- C. Increasing the number of training iterations
- D. Using adversarial training
Answer: A
Explanation:
AAISM classifies model inversion as a privacy leakage threat where adversaries infer sensitive attributes or training records from model outputs. The recommended technical risk treatments emphasize reducing overfitting and information leakage via regularization and output-side constraints. Regularization (e.g., stronger penalties, output smoothing, confidence calibration, temperature limiting, and related techniques) reduces the model's tendency to memorize training data and curtails exploitable signal in outputs.
* A (adversarial training) targets perturbation robustness, not primary for inversion.
* B (reducing complexity) can help but is a coarse control with limited assurance versus explicit anti-leakage regularization.
* D (more iterations) typically increases overfitting and leakage risk.
AAISM further notes that privacy-preserving training and output minimization are preferred where feasible; among the listed options, regularization most directly addresses inversion risk.
References:* AI Security Management™ (AAISM) Body of Knowledge: Model Security-Privacy leakage threats (membership inference, inversion) and mitigation via regularization and output minimization.* AI Security Management™ Study Guide: Overfitting controls, calibration and confidence suppression as defenses against inference attacks.
NEW QUESTION # 108
Which of the following is a key risk indicator (KRI) for an AI system used for threat detection?
- A. Number of layers in the neural network
- B. Number of system overrides by cyber analysts
- C. Training time of the model
- D. Number of training epochs
Answer: B
Explanation:
AAISM materials emphasize that in operational AI systems, key risk indicators (KRIs) must reflect risks to performance and reliability rather than technical design factors alone. In the case of threat detection, the most relevant KRI is the frequency of system overrides by human analysts, as this indicates a lack of trust, frequent false positives, or poor detection accuracy. Training epochs, model depth, and training time are technical metrics but do not directly measure operational risk. Analyst overrides represent a practical measure of system effectiveness and risk.
References:
AAISM Study Guide - AI Risk Management (Operational KRIs for AI Systems) ISACA AI Security Management - Monitoring AI Effectiveness
NEW QUESTION # 109
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