Sep-2025 Realistic AAISM Exam Dumps with Accurate & Updated Questions
AAISM Exam Dumps - PDF Questions and Testing Engine
NEW QUESTION # 35
Which of the following metrics BEST evaluates the ability of a model to correctly identify all true positive instances?
- A. Recall
- B. Precision
- C. F1 score
- D. Specificity
Answer: A
Explanation:
AAISM technical coverage identifies recall as the metric that specifically measures a model's ability to capture all true positive cases out of the total actual positives. A high recall means the system minimizes false negatives, ensuring that relevant instances are not overlooked. Precision instead measures correctness among predicted positives, specificity focuses on true negatives, and the F1 score balances precision and recall but does not by itself indicate the completeness of capturing positives. The official study guide defines recall as the most direct metric for evaluating how well a model identifies all relevant positive cases, making it the correct answer.
References:
AAISM Study Guide - AI Technologies and Controls (Evaluation Metrics and Model Performance) ISACA AI Security Management - Model Accuracy and Completeness Assessments
NEW QUESTION # 36
Which of the following is the BEST reason to immediately disable an AI system?
- A. Excessive model drift
- B. Insufficient model training
- C. Overly detailed model outputs
- D. Slow model performance
Answer: A
Explanation:
According to AAISM lifecycle management guidance, the best justification for disabling an AI system immediately is the detection of excessive model drift. Drift results in outputs that are no longer reliable, accurate, or aligned with intended purpose, creating significant risks. Performance slowness and overly detailed outputs are operational inefficiencies but not critical shutdown triggers. Insufficient training should be addressed before deployment rather than after. The trigger for immediate deactivation in production is excessive drift compromising reliability.
References:
AAISM Exam Content Outline - AI Governance and Program Management (Model Drift Management) AI Security Management Study Guide - Disabling AI Systems
NEW QUESTION # 37
An attacker crafts inputs to a large language model (LLM) to exploit output integrity controls. Which of the following types of attacks is this an example of?
- A. Evasion
- B. Jailbreaking
- C. Remote code execution
- D. Prompt injection
Answer: D
Explanation:
According to the AAISM framework, prompt injection is the act of deliberately crafting malicious or manipulative inputs to override, bypass, or exploit the model's intended controls. In this case, the attacker is targeting the integrity of the model's outputs by exploiting weaknesses in how it interprets and processes prompts. Jailbreaking is a subtype of prompt injection specifically designed to override safety restrictions, while evasion attacks target classification boundaries in other ML contexts, and remote code execution refers to system-level exploitation outside of the AI inference context. The most accurate classification of this attack is prompt injection.
References:
AAISM Exam Content Outline - AI Technologies and Controls (Prompt Security and Input Manipulation) AI Security Management Study Guide - Threats to Output Integrity
NEW QUESTION # 38
Which of the following AI system vulnerabilities is MOST easily exploited by adversaries?
- A. Lack of protection against denial of service (DoS) attacks
- B. Inability to detect input modifications causing inappropriate AI outputs
- C. Inaccurate generalizations from new data by the AI model
- D. Weak controls for access to the AI model
Answer: D
Explanation:
AAISM study materials stress that weak access controls are the most easily exploited vulnerability in AI systems. Without strong access restrictions, adversaries can directly query, extract, manipulate, or overload models, leading to data leakage or compromised outputs. While inaccurate generalizations, DoS vulnerabilities, or susceptibility to input manipulation are serious, they typically require more effort or specific conditions. Weak access control provides the most direct and immediate entry point for attackers. As such, it is identified as the most easily exploited vulnerability.
References:
AAISM Exam Content Outline - AI Risk Management (Access and Authentication Vulnerabilities) AI Security Management Study Guide - Exploitable Weaknesses in AI Systems
NEW QUESTION # 39
An organization uses an AI tool to scan social media for product reviews. Fraudulent social media accounts begin posting negative reviews attacking the organization's product. Which type of AI attack is MOST likely to have occurred?
- A. Availability attack
- B. Data poisoning
- C. Deepfake
- D. Model inversion
Answer: A
Explanation:
The AAISM materials classify availability attacks as attempts to disrupt or degrade the functioning of an AI system so that its outputs become unreliable or unusable. In this scenario, the fraudulent social media accounts are deliberately overwhelming the AI tool with misleading negative reviews, undermining its ability to deliver accurate sentiment analysis. This aligns directly with the concept of an availability attack. Model inversion relates to reconstructing training data from outputs, deepfakes involve synthetic content generation, and data poisoning corrupts the training set rather than manipulating inputs at runtime. Therefore, the fraudulent review campaign is most accurately identified as an availability attack.
References:
AAISM Study Guide - AI Risk Management (Adversarial Threats and Availability Risks) ISACA AI Security Management - Attack Classifications
NEW QUESTION # 40
An organization concerned about the ethical and responsible use of a newly developed AI product should consider implementing:
- A. Security by design
- B. An accountability model
- C. Model cards
- D. Vendor monitoring
Answer: B
Explanation:
The AAISM framework highlights that organizations adopting AI must ensure accountability structures are in place to govern ethical and responsible use. An accountability model assigns clear responsibility for decisions, outputs, and risks related to AI systems. While model cards provide transparency about a model's design and performance, they are primarily documentation tools. Vendor monitoring focuses on third-party oversight, not internal accountability. Security by design improves resilience but does not by itself address ethical use. The governance approach that most directly supports responsible and ethical AI deployment is an accountability model.
References:
AAISM Study Guide - AI Governance and Program Management (Ethical AI and Accountability) ISACA AI Security Management - Responsible AI Practices
NEW QUESTION # 41
Which of the following is the MOST effective use of AI-enabled tools in a security operations center (SOC)?
- A. Employing AI-enabled tools to reduce false negatives by detecting subtle attack patterns
- B. Using AI-enabled tools exclusively to classify all types of security incidents
- C. Assigning AI-enabled tools to triage non-critical alerts to preserve SOC resources
- D. Replacing human analysis with automated AI decision-making processes
Answer: A
Explanation:
Themost effective SOC applicationof AI is indetecting subtle, hard-to-find attack patternsthat reduce false negatives.
AAISM technical control guidance notes that AI in SOCs is best applied to:
* Enhance detection accuracy and sensitivity to anomalies.
* Assist analysts in identifying hidden patterns that traditional rule-based systems miss.
* Augment-not replace-human decision-making for high-confidence outcomes.
Options B and C incorrectly shift responsibility entirely to AI, which contradicts governance principles requiringhuman oversight. Option D is useful for efficiency, but theprimary effectivenesscomes from improving detection quality.
Therefore, the most effective use is toreduce false negatives and detect subtle attacks.
NEW QUESTION # 42
Which of the following would BEST help to prevent the compromise of a facial recognition AI system through the use of alterations in facial appearance?
- A. Monitoring the system for misuse cases
- B. Fine-tuning the AI model to decrease hallucinations
- C. Enhancing training data to increase variance
- D. Implementing a secondary AI system to confirm images
Answer: C
Explanation:
AAISM materials note that adversaries may attempt to bypass facial recognition by disguising or altering appearance. The most effective mitigation is to enhance training data with a wide range of variances in facial features, lighting, and disguises so the system can robustly detect authentic users despite adversarial attempts.
Monitoring and secondary confirmation are supportive controls but are reactive. Fine-tuning to reduce hallucinations is irrelevant in this context, as hallucinations apply more to generative AI. The best preventive measure is strengthening the model with diverse, variance-rich training data.
References:
AAISM Study Guide - AI Technologies and Controls (Robust Training Data Strategies) ISACA AI Security Management - Biometric AI Security Risks
NEW QUESTION # 43
An organization develops and implements an AI-based plug-in for users that summarizes their individual emails. Which of the following is the GREATEST risk associated with this application?
- A. Inadequate controls over parameters
- B. Insufficient rate limiting for APIs
- C. Data format incompatibility
- D. Lack of application vulnerability scanning
Answer: A
Explanation:
According to AAISM risk management guidance, the greatest risk in AI applications handling personal communication data is inadequate parameter controls, which may allow unintended access, manipulation, or leakage of sensitive information. Plug-ins that interact with emails must enforce strict parameter validation and security restrictions to prevent unauthorized or manipulated inputs. While vulnerability scanning, format incompatibility, and API rate limiting are valid concerns, they are secondary. The primary risk is a lack of strong parameter controls that could expose sensitive content.
References:
AAISM Exam Content Outline - AI Risk Management (Application Security Risks) AI Security Management Study Guide - Plug-in and API Security Risks
NEW QUESTION # 44
An organization using an AI model for financial forecasting identifies inaccuracies caused by missing data.
Which of the following is the MOST effective data cleaning technique to improve model performance?
- A. Tuning model hyperparameters to increase performance and accuracy
- B. Deleting outlier data points to prevent unusual values impacting the model
- C. Applying statistical methods to address missing data and reduce bias
- D. Increasing the frequency of model retraining with the existing data set
Answer: C
Explanation:
The AAISM study content emphasizes that data quality management is a central pillar of AI risk reduction.
Missing data introduces bias and undermines predictive accuracy if not addressed systematically. The most effective remediation is to apply statistical imputation and related methods to fill in or adjust for missing values in a way that minimizes bias and preserves data integrity. Retraining on flawed data does not solve the underlying issue. Deleting outliers may harm model robustness, and hyperparameter tuning optimizes model mechanics but cannot resolve missing information. Therefore, the proper corrective technique for missing data is the application of statistical methods to reduce bias.
References:
AAISM Study Guide - AI Risk Management (Data Integrity and Quality Controls) ISACA AI Governance Guidance - Data Preparation and Bias Mitigation
NEW QUESTION # 45
A financial institution plans to deploy an AI system to provide credit risk assessments for loan applications.
Which of the following should be given the HIGHEST priority in the system's design to ensure ethical decision-making and prevent bias?
- A. Train the system to provide advisory outputs with final decisions made by human experts.
- B. Regularly update the model with new customer data to improve prediction accuracy.
- C. Restrict the model's decision-making criteria to objective financial metrics only.
- D. Integrate a mechanism for customers to appeal decisions directly within the system.
Answer: A
Explanation:
In AI governance frameworks, credit scoring is treated as a high-risk application. For such systems, the highest-priority safeguard is human oversight to ensure fairness, accountability, and prevention of bias in automated decisions.
The AI Security Management™ (AAISM) domain of AI Governance and Program Management emphasizes that high-impact AI systems require explicit governance structures and human accountability. Human-in-the- loop design ensures that final decisions remain the responsibility of human experts rather than being fully automated. This is particularly critical in financial contexts, where biased outputs can affect individuals' access to credit and create compliance risks.
Official ISACA AI governance guidance specifies:
High-risk AI systems must comply with strict requirements, including human oversight, transparency, and fairness.
The purpose of human oversight is to reduce risks to fundamental rights by ensuring humans can intervene or override an automated decision.
Bias controls are strengthened by requiring human review processes that can analyze outputs and prevent unfair discrimination.
Why other options are not the highest priority:
A). Regular updates improve accuracy but do not guarantee fairness or ethical decision-making. Model drift can introduce new bias if not governed properly.
B). Appeals mechanisms are important for accountability, but they operate after harm has occurred.
Governance frameworks emphasize prevention through human oversight in the decision loop.
D). Restricting criteria to "objective metrics" is insufficient, as even objective data can contain hidden proxies for protected attributes. Bias mitigation requires monitoring, testing, and human oversight, not only feature restriction.
AAISM Domain Alignment:
Domain 1 - AI Governance and Program Management: Ensures accountability, ethical oversight, and governance structures.
Domain 2 - AI Risk Management: Identifies and mitigates risks such as bias, discrimination, and lack of transparency.
Domain 3 - AI Technologies and Controls: Provides the technical enablers for implementing oversight mechanisms and bias detection tools.
References from AAISM and ISACA materials:
AAISM Exam Content Outline - Domain 1: AI Governance and Program Management (roles, responsibilities, oversight).
ISACA AI Governance Guidance (human oversight as mandatory in high-risk AI applications).
Bias and Fairness Controls in AI (human review and intervention as a primary safeguard).
NEW QUESTION # 46
Which of the following controls BEST mitigates the inherent limitations of generative AI models?
- A. Classifying and labeling AI systems
- B. Ensuring human oversight
- C. Reverse engineering the models
- D. Adopting AI-specific regulations
Answer: B
Explanation:
The AAISM governance framework emphasizes that the inherent limitations of generative AI-including hallucinations, bias, and unpredictability-are best mitigated by human oversight. Human-in-the-loop review ensures that outputs are validated before being used in sensitive or high-risk contexts. Regulatory adoption, system classification, and reverse engineering all play supporting roles but do not directly safeguard against the model's inherent unpredictability. Governance best practices highlight human oversight as the critical safeguard.
References:
AAISM Exam Content Outline - AI Governance and Program Management (Human Oversight and Accountability) AI Security Management Study Guide - Mitigating Generative AI Limitations
NEW QUESTION # 47
An organization utilizes AI-enabled mapping software to plan routes for delivery drivers. A driver following the AI route drives the wrong way down a one-way street, despite numerous signs. Which of the following biases does this scenario demonstrate?
- A. Automation
- B. Confirmation
- C. Reporting
- D. Selection
Answer: A
Explanation:
AAISM defines automation bias as the tendency of individuals to over-rely on AI-generated outputs even when contradictory real-world evidence is available. In this scenario, the driver ignores traffic signs and follows the AI's instructions, showing blind reliance on automation. Selection bias relates to data sampling, reporting bias refers to misrepresentation of results, and confirmation bias involves interpreting information to fit pre-existing beliefs. The most accurate description is automation bias.
References:
AAISM Exam Content Outline - AI Risk Management (Bias Types in AI)
AI Security Management Study Guide - Automation Bias in AI Use
NEW QUESTION # 48
Which of the following should be a PRIMARY consideration when defining recovery point objectives (RPOs) and recovery time objectives (RTOs) for generative AI solutions?
- A. Preserving the most recent versions of data models to avoid inaccuracies in functionality
- B. Ensuring the backup system can restore training data sets within the defined RTO window
- C. Maintaining consistent hardware configurations to prevent discrepancies during model restoration
- D. Prioritizing computational efficiency over data integrity to minimize downtime
Answer: B
Explanation:
When setting RPOs and RTOs for AI systems, especiallygenerative AI, thecritical factor is the restoration of training data and model artifacts within the recovery window. Without this, restored systems may function inaccurately or incompletely, undermining business continuity.
AAISM risk management principles emphasize:
* Recovery objectives must align withdata protection requirementsfor both training and inference data.
* The ability to restorelarge-scale training datasetsis primary, since downtime without them leads to operational and compliance risks.
* Computational efficiency and hardware consistency are secondary considerations, but not the primary drivers of RPO/RTO definitions.
Thus, ensuring backup and restore capabilities of training datasets directly within RTO is theprimary requirement.
NEW QUESTION # 49
Which of the following is the MOST important factor to consider when selecting industry frameworks to align organizational AI governance with business objectives?
- A. Risk appetite
- B. Risk tolerance
- C. Risk register
- D. Risk threshold
Answer: A
Explanation:
According to AAISM governance principles, the risk appetite of the organization is the most important factor in selecting appropriate frameworks for AI governance. Risk appetite defines the level of risk an organization is willing to accept in pursuit of its objectives, ensuring frameworks are aligned with strategic goals. Risk tolerance and thresholds are operational measures derived from appetite, and the risk register is a documentation tool. The foundational consideration for framework alignment is the organization's risk appetite.
References:
AAISM Exam Content Outline - AI Governance and Program Management (Risk Appetite in Governance Alignment) AI Security Management Study Guide - Framework Selection and Business Strategy
NEW QUESTION # 50
A model producing contradictory outputs based on highly similar inputs MOST likely indicates the presence of:
- A. Evasion attacks
- B. Membership inference
- C. Model exfiltration
- D. Poisoning attacks
Answer: A
Explanation:
The AAISM study framework describes evasion attacks as attempts to manipulate or probe a trained model during inference by using crafted inputs that appear normal but cause the system to generate inconsistent or erroneous outputs. Contradictory results from nearly identical queries are a typical symptom of evasion, as the attacker is probing decision boundaries to find weaknesses. Poisoning attacks occur during training, not inference, while membership inference relates to exposing whether data was part of the training set, and model exfiltration involves extracting proprietary parameters or architecture. The clearest indication of contradictory outputs from similar queries therefore aligns directly with the definition of evasion attacks in AAISM materials.
References:
AAISM Study Guide - AI Technologies and Controls (Adversarial Machine Learning and Attack Types) ISACA AI Security Management - Inference-time Attack Scenarios
NEW QUESTION # 51
Which of the following AI-driven systems should have the MOST stringent recovery time objective (RTO)?
- A. Industrial control system
- B. Credit risk modeling system
- C. Health support system
- D. Car navigation system
Answer: A
Explanation:
AAISM risk guidance notes that the most stringent recovery objectives apply to industrial control systems, as downtime can directly disrupt critical infrastructure, manufacturing, or safety operations. Health support systems also require high availability, but industrial control often underpins safety-critical and real-time environments where delays can result in catastrophic outcomes. Credit risk models and navigation systems are important but less critical in terms of immediate physical and operational impact. Thus, industrial control systems require the tightest RTO.
References:
AAISM Study Guide - AI Risk Management (Business Continuity in AI)
ISACA AI Security Management - RTO Priorities for AI Systems
NEW QUESTION # 52
Which of the following employee awareness topics would MOST likely be revised to account for AI-enabled cyber risk?
- A. Authentication controls
- B. Clean desk policy
- C. Social engineering
- D. Malicious insider threats
Answer: C
Explanation:
AAISM training guidance specifies that social engineering is the awareness topic most impacted by AI- enabled risks. With generative AI and deepfake technologies, attackers can create highly convincing phishing messages, synthetic voices, or fake executive requests, increasing the sophistication of social engineering attacks. Clean desk policies, insider threat awareness, and authentication procedures remain relevant but are not directly altered by AI advancements. The most likely revision to employee awareness programs in the AI era is therefore enhanced social engineering awareness.
References:
AAISM Exam Content Outline - AI Risk Management (Human Factors and Awareness) AI Security Management Study Guide - Social Engineering Risks with AI
NEW QUESTION # 53
Which of the following MOST effectively minimizes the attack surface when securing AI agent components during their development and deployment?
- A. Consolidate event logs for correlation and centralized analysis.
- B. Deploy pre-trained models directly into production.
- C. Schedule periodic manual code reviews.
- D. Implement compartmentalization with least privilege enforcement.
Answer: D
Explanation:
The most effective strategy tominimize attack surfacesin AI agent security is to apply compartmentalization and least privilege enforcement.
AAISM control frameworks emphasize:
* Isolation of components (e.g., training, inference, data pipelines) to limit lateral movement.
* Principle ofleast privilegeto restrict access only to what is required for function.
* Hardening AI pipelines through segmentation rather than relying solely on manual reviews or monitoring.
Pre-trained models and log centralization are useful but do not directly reduce the attack surface.Manual code reviewsare important but insufficient against runtime exploitation.
Thus,compartmentalization with least privilege enforcementis the most effective technical safeguard.
NEW QUESTION # 54
Which of the following is the BEST approach for minimizing risk when integrating acceptable use policies for AI foundation models into business operations?
- A. Limit model usage to predefined scenarios specified by the developer
- B. Establish AI model life cycle policy and procedures
- C. Implement responsible development training and awareness
- D. Rely on the developer's enforcement mechanisms
Answer: B
Explanation:
The AAISM guidance defines risk minimization for AI deployment as requiring a formalized AI model life cycle policy and associated procedures. This ensures oversight from design to deployment, covering data handling, bias testing, monitoring, retraining, decommissioning, and acceptable use. Limiting usage to developer-defined scenarios or relying on vendor mechanisms transfers responsibility away from the organization and fails to meet governance expectations. Training and awareness support cultural alignment but cannot substitute for structured lifecycle controls. Therefore, the establishment of a documented lifecycle policy and procedures is the most comprehensive way to minimize operational, compliance, and ethical risks in integrating foundation models.
References:
AAISM Study Guide - AI Governance and Program Management (Model Lifecycle Governance) ISACA AI Security Guidance - Policies and Lifecycle Management
NEW QUESTION # 55
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