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Free Isaca Certification AAISM Official Cert Guide PDF Download
ISACA AAISM Exam Syllabus Topics:
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NEW QUESTION # 37
When documenting information about machine learning (ML) models, which of the following artifacts BEST helps enhance stakeholder trust?
- A. Hyperparameters
- B. Model card
- C. Data quality controls
- D. Model prototyping
Answer: B
Explanation:
Themodel cardis a governance artifact that communicatesintended use, performance characteristics, limitations, fairness considerations, and ethical notesof an ML model.
AAISM governance materials highlight thatstakeholder trustcomes from transparency and explainability.
While hyperparameters and data quality controls are technical details, they lack stakeholder-facing clarity.
Model prototyping is part of development but not a governance record.
Model cards are explicitly recommended to:
* Provideexplainabilityand context for decision-making.
* Demonstrate governance, transparency, and compliance.
* Enable stakeholders (regulators, auditors, business leaders) to trust the system.
Therefore, themodel cardis the artifact that best enhances trust.
NEW QUESTION # 38
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 # 39
Secure aggregation enhances federated learning security by:
- A. Processing client updates in isolation
- B. Encrypting individual model updates so only the server can access them
- C. Applying differential privacy to training data
- D. Ensuring client contributions remain confidential even if the server is compromised
Answer: D
Explanation:
AAISM explains that secure aggregation ensures the server only sees aggregated model updates-not individual client contributions-so privacy is preserved even if the server is breached.
Encryption (A) is semi-correct but still allows the server to decrypt. Differential privacy (B) is separate.
Isolation (D) does not guarantee confidentiality.
References: AAISM Study Guide - Federated Learning Security; Secure Aggregation.
NEW QUESTION # 40
Which of the following is the GREATEST benefit of performing AI security risk assessments?
- A. The risk register is updated with the latest AI risk
- B. The appropriate level of funding is secured for AI security risk
- C. Appropriate privacy risk controls are implemented for AI models
- D. Risk prioritization decisions are made for AI security
Answer: D
Explanation:
AAISM emphasizes that the core outcome of AI risk assessments is prioritization: mapping threat likelihood and business impact to determine which risks to treat first, at what strength, and with which controls.
Implementing privacy controls (A), funding alignment (B), and updating registers (C) are important outputs, but the greatest benefit is making defensible, prioritized decisions that align with risk appetite and optimize control selection and resource allocation.
References: AI Security Management™ (AAISM) Body of Knowledge - AI Risk Assessment & Treatment; Risk Appetite, Tolerance, and Prioritization; Governance of Risk Decisions and Tracking.
NEW QUESTION # 41
When robust input controls cannot prevent prompt injections in an LLM, what is the BEST compensating control?
- A. Fine-tune the system to validate inputs
- B. Review and annotate the AI system's outputs
- C. Conduct human reviews of AI system inputs
- D. Implement identity and access management (IAM)
Answer: B
Explanation:
AAISM identifies output review and annotation as the most practical compensating control when robust input validation cannot be applied.
Output moderation detects:
* maliciously influenced responses
* unsafe outputs
* security-policy violations
IAM (B) does not mitigate prompt injection itself. Human review of inputs (C) is unrealistic at scale. Fine- tuning (A) cannot guarantee full prevention.
References: AAISM Study Guide - Generative AI Safeguards; Output Moderation Controls.
NEW QUESTION # 42
An organization is designing an AI-based credit risk assessment system that will integrate with sensitive financial datasets. Which of the following would BEST support the implementation of security-by-design principles in the AI system's architecture?
- A. Segmenting AI services across containers to manage resource constraints
- B. Integrating differential privacy mechanisms into model training to limit data leakage
- C. Applying threat modeling specific to AI components before deployment
- D. Restricting access to AI models using IP allow lists to reduce public exposure
Answer: C
Explanation:
Security by design in AI requires establishing risk-informed requirements at the earliest stages of the lifecycle and systematically translating them into architectural controls. Conducting AI-specific threat modeling before deployment is the highest-leverage action because it identifies assets (data, models, pipelines), trust boundaries (feature stores, training/inference services), threat events (poisoning, evasion, model extraction), and attack paths unique to ML systems. The outputs (abuse/misuse cases, control objectives, verification plans) then drive selection and prioritization of controls such as privacy-enhancing techniques, access controls, isolation, monitoring, and assurance testing. While differential privacy (C) is a strong control for leakage risk, it is one control choice among many and should be selected as a result of threat modeling. IP allow lists (B) and container segmentation (A) are valuable hardening measures but are narrower and do not replace the lifecycle-wide governance and design traceability that threat modeling enables.
References: AI Security Management™ (AAISM) Body of Knowledge - Secure AI SDLC; AI Threat Modeling and Abuse Case Development; Architecture & Control Selection; Risk-Based Design Assurance.
AAISM Study Guide - Security-by-Design for AI; Model/System Asset Mapping; Control Objectives from Threat Models.
NEW QUESTION # 43
A PRIMARY objective of responsibly providing AI services is to:
- A. Enable AI models to operate autonomously
- B. Build trust for decisions and predictions made by AI models
- C. Ensure the confidentiality and integrity of data processed by AI models
- D. Improve the ability of AI models to learn from new data
Answer: B
Explanation:
AAISM emphasizes that the primary objective of responsible AI is to establish and maintain trust in AI-driven decisions and predictions. Trust is achieved through transparency, accountability, fairness, and governance.
While confidentiality and integrity are critical technical objectives, they are not the overarching purpose of responsible AI service provision. Autonomy and learning ability are features of AI, but without trust, adoption and compliance falter. The correct answer is that responsible AI services must focus on building trust in AI outcomes.
References:
AAISM Exam Content Outline - AI Governance and Program Management (Responsible AI Principles) AI Security Management Study Guide - Trust and Ethical AI Adoption
NEW QUESTION # 44
Which of the following should be the PRIMARY consideration for an organization concerned about liabilities associated with unforeseen behavior from agentic AI systems?
- A. Acceptable risk level
- B. Model dependencies
- C. Accountability model
- D. Approved base models
Answer: C
Explanation:
AAISM governance guidance stresses that when dealing with agentic AI systems capable of autonomous decision-making, the primary consideration is accountability. Without clear accountability structures, unforeseen or harmful outcomes may result in unmitigated liability for the organization. While dependencies, base models, and defined risk levels are important, they do not directly address who is responsible when systems act unpredictably. The key governance safeguard is the implementation of an accountability model that ensures liability and oversight are properly assigned.
References:
AAISM Exam Content Outline - AI Governance and Program Management (Accountability and Liability Management) AI Security Management Study Guide - Responsible Oversight of Agentic AI
NEW QUESTION # 45
Which of the following BEST reduces the risk of exposing sensitive data through the output of large language models (LLMs) in applications?
- A. Conducting adversarial testing
- B. Implementing data sanitization techniques
- C. Encrypting data in transit and at rest
- D. Enforcing least privilege access
Answer: B
Explanation:
AAISM materials make clear that the best safeguard against sensitive information being leaked through the outputs of LLMs is data sanitization. This involves filtering, redacting, or masking sensitive content before the model can use it, thereby preventing unintended disclosure in outputs. Encryption protects confidentiality in storage and transmission but does not stop output leaks. Adversarial testing helps identify vulnerabilities but does not prevent exposure by itself. Least privilege access restricts who can interact with the model but does not sanitize the content of its outputs. The control most directly tied to preventing leakage is implementing data sanitization techniques.
References:
AAISM Exam Content Outline - AI Technologies and Controls (Data Leakage Prevention) AI Security Management Study Guide - Sensitive Data Controls in Generative AI
NEW QUESTION # 46
A financial organization uses AI to detect potential fraudulent activities but is concerned about the impact of potential data poisoning. Which of the following controls would BEST mitigate this risk?
- A. Using training data from multiple sources
- B. Delivering AI-specific security awareness training
- C. Being transparent with customers about the data sources
- D. Implementing an updated and tested break-glass policy
Answer: A
Explanation:
AAISM identifies training-data diversity and provenance assurance as primary treatments against data poisoning. Sourcing data from multiple, independently governed providers, combined with ingestion validation and anomaly screening, reduces the chance that a single compromised source can skew model behavior and improves cross-source consistency checks. Transparency, break-glass, and awareness are valuable but do not directly reduce poisoning exposure at the training boundary.
References: AI Security Management™ (AAISM) Body of Knowledge - Data Governance & Integrity for AI; Adversarial ML: Poisoning Threats and Mitigations; Supplier and Source Diversification Controls.
NEW QUESTION # 47
An organization is reviewing an AI application to determine whether it is still needed. Engineers have been asked to analyze the number of incorrect predictions against the total number of predictions made. Which of the following is this an example of?
- A. Key performance indicator (KPI)
- B. Model validation
- C. Explainable decision-making
- D. Control self-assessment (CSA)
Answer: A
Explanation:
AAISM guidance identifies metrics like error rate versus total predictions as a key performance indicator (KPI) for evaluating AI model effectiveness. KPIs provide measurable values to assess performance against objectives. Model validation is broader and occurs prior to production use, testing the model against predefined standards. Control self-assessment relates to governance processes, not predictive accuracy.
Explainable decision-making refers to interpretability, not error-rate evaluation. Thus, analyzing incorrect predictions against total predictions is a performance measure, making it a KPI.
References:
AAISM Exam Content Outline - AI Governance and Program Management (Performance Metrics and KPIs) AI Security Management Study Guide - Accuracy and Error Metrics
NEW QUESTION # 48
An organization plans to implement a new AI system. Which of the following is the MOST important factor in determining the level of risk monitoring activities required?
- A. The organization's number of AI system users
- B. The organization's risk tolerance
- C. The organization's risk appetite
- D. The organization's compensating controls
Answer: B
Explanation:
AAISM risk management guidance clarifies that the organization's risk tolerance is the most important factor in determining how much monitoring is needed. Risk tolerance specifies the amount of risk the organization is willing to accept and defines the threshold for triggering monitoring or mitigation activities. Risk appetite is broader and strategic, while tolerance sets the operational limits. The number of users may influence scale, and compensating controls may affect resilience, but neither dictates monitoring intensity as directly as risk tolerance.
References:
AAISM Study Guide - AI Risk Management (Risk Appetite vs. Tolerance)
ISACA AI Security Management - Monitoring Based on Risk Tolerance
NEW QUESTION # 49
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. Prompt injection
- B. Jailbreaking
- C. Evasion
- D. Remote code execution
Answer: A
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 # 50
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. Data poisoning
- B. Model inversion
- C. Unauthorized tuning
- D. Model distillation
Answer: B
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 # 51
An organization is adopting an agentic AI solution from an external vendor to support internal IT operations.
Which of the following provides the MOST reliable and independently verifiable evidence of implemented security controls?
- A. General AI security whitepapers
- B. Third-party audit reports
- C. Industry benchmarking peer review
- D. Internal red-team testing reports
Answer: B
Explanation:
AAISM states that when evaluating external AI vendors, independently issued third-party audit reports (SOC, ISO, AI assurance assessments) provide the strongest evidence of implemented controls because they are objective, repeatable, and externally verified.
Peer reviews (A) lack formality, internal red-team reports (C) are non-independent, and whitepapers (D) are marketing documents without assurance value.
References: AAISM Study Guide - Third-Party AI Risk Management; Independent Assurance and Audit Requirements.
NEW QUESTION # 52
A model producing contradictory outputs based on highly similar inputs MOST likely indicates the presence of:
- A. Membership inference
- B. Model exfiltration
- C. Poisoning attacks
- D. Evasion attacks
Answer: D
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 # 53
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. Focusing resources on post-deployment red teaming and deferring control selection until post go-live feedback is received
- B. Implementing an AI threat control matrix that maps threats to specific controls and assurance activities
- C. Relying on vendor independent audit reports and service level agreements (SLAs) as evidence of AI risk coverage
- D. Applying control baselines from a recognized industry standard to AI components
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 # 54
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