[Jun-2026] Pass AAISM Exam in First Attempt Updated AAISM Exam Questions
Isaca Certification Dumps AAISM Exam for Full Questions - Exam Study Guide
ISACA AAISM Exam Syllabus Topics:
| Topic | Details |
|---|---|
| Topic 1 |
|
| Topic 2 |
|
| Topic 3 |
|
NEW QUESTION # 103
Which of the following is the BEST way to ensure role clarity and staff effectiveness when implementing AI- assisted security monitoring tools?
- A. Defer implementation until the security team can be expanded with data scientists.
- B. Increase training budgets for business staff to obtain vendor-neutral AI certifications.
- C. Transition responsibilities for AI tools to external consultants for improved scalability.
- D. Update the security program to include cross-functional AI-specific responsibilities.
Answer: D
Explanation:
AAISM directs that AI adoption in security operations be governed through explicit operating models and RACI-mapped responsibilities spanning security operations, data science/ML, platform engineering, privacy, and compliance. Role clarity comes from updating the security program to codify AI-specific responsibilities (model monitoring, incident handling for AI failures, data governance, change control for models, bias
/fairness reviews, supplier assurance) rather than deferring implementation, outsourcing core accountability, or relying on generic certifications. This ensures measurable accountability, reduces hand-off risk, and aligns day-to-day SOC practices with AI control objectives.
References:* AI Security Management™ (AAISM) Body of Knowledge: AI Governance Operating Model; Roles & Responsibilities; RACI for AI-enabled Security Operations* AAISM Study Guide: Program Governance, Control Objective Mapping to SOC Workflows; Cross-Functional Ownership for AI Controls
NEW QUESTION # 104
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. Third-party audit reports
- B. Internal red-team testing reports
- C. Industry benchmarking peer review
- D. General AI security whitepapers
Answer: A
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 # 105
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. Performing threat modeling and integrity checks
- B. Implementing unsupervised learning methods
- C. Disabling runtime logs during model training
- D. Leveraging open-source models and packages
Answer: A
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 # 106
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. Remote code execution
- B. Prompt injection
- C. Jailbreaking
- D. Evasion
Answer: B
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 # 107
Which of the following would BEST help mitigate vulnerabilities associated with hidden triggers in generative AI models?
- A. Monitoring model outputs and suspicious patterns to detect trigger activations
- B. Regularly retraining the model using a diverse data set
- C. Applying differential privacy and masking sensitive patterns in the training data
- D. Incorporating adversarial training to expose and neutralize potential triggers
Answer: D
Explanation:
Hidden triggers are adversarial backdoors planted in AI models, activated only by specific inputs. The AAISM materials specify that the best mitigation is to use adversarial training, which deliberately exposes the model to potential trigger inputs during training so it can learn to neutralize or resist them. Retraining with diverse data reduces bias but does not address hidden triggers. Differential privacy is focused on privacy preservation, not adversarial resilience. Monitoring outputs can help with detection but is reactive rather than preventative. The proactive solution highlighted in the study guide is adversarial training.
References:
AAISM Exam Content Outline - AI Risk Management (Backdoors and Hidden Triggers) AI Security Management Study Guide - Adversarial Training as a Mitigation Control
NEW QUESTION # 108
An organization is deploying a large language model (LLM) and is concerned that input manipulations may compromise its integrity. Which of the following is the MOST effective way to determine an acceptable risk threshold?
- A. Restrict all user inputs containing special characters
- B. Assess the business impact of known threats
- C. Deploy a real-time logging and monitoring system
- D. Implement a static risk threshold by limiting LLM outputs
Answer: B
Explanation:
AAISM requires that risk thresholds/tolerances be set by aligning threat likelihood and impact with the organization's business context and risk appetite. Determining "acceptable" risk starts with assessing business impact of credible threats (e.g., prompt injection leading to data exfiltration, policy evasion, or harmful actions), then translating this into control intensity and thresholds. Hard input restrictions (A) and static output caps (C) are blunt measures that may degrade utility without ensuring alignment to risk appetite. Monitoring (B) is essential for detection, but it does not, by itself, define what level of risk is acceptable.
References: AI Security Management™ (AAISM) Body of Knowledge - Risk Appetite and Tolerance for AI; Threat Modeling for LLMs; Business Impact Analysis and Risk Acceptance Criteria.
NEW QUESTION # 109
An organization plans to use AI to analyze the shopping patterns of its customers to predict interests and send targeted, customized marketing emails. Which of the following should be done FIRST?
- A. Obtain customer consent
- B. Verify customer email addresses
- C. Update the terms of service
- D. Train the marketing department
Answer: A
Explanation:
The first action, before any processing of personal data for AI-driven profiling and targeted communications, is to establish a lawful basis for processing. Under AAISM-aligned privacy governance, explicit and informed consent is prioritized for new or sensitive uses such as interest profiling and targeted marketing.
Consent ensures purpose limitation, transparency, and user control prior to model ingestion and campaign activation. Training teams, updating terms of service, or verifying contact details are important, but they do not provide legal authority to process data; therefore, they follow after consent is obtained.
References: AI Security Management (AAISM) Body of Knowledge - Privacy Governance and Lawful Basis; Purpose Limitation and Transparency; Consent Management in AI-enabled Marketing. AAISM Study Guide - Data Protection Controls for AI Profiling; Consent Capture and Record-Keeping.
NEW QUESTION # 110
When integrating AI for innovation, which of the following can BEST help an organization manage security risk?
- A. Adopting a phased approach
- B. Seeking third-party advice
- C. Evaluating compliance requirements
- D. Re-evaluating the risk appetite
Answer: A
Explanation:
AAISM emphasizes that when introducing innovative AI systems, organizations reduce security and compliance risk by following a phased adoption approach. This allows incremental deployment, controlled testing, and gradual scaling while monitoring risks in real time. Re-evaluating risk appetite and evaluating compliance are important governance steps but do not directly mitigate risks during implementation. Seeking third-party advice can add expertise but does not provide the structured control that phased integration offers.
The most effective risk management approach for AI innovation is to adopt a phased rollout strategy.
References:
AAISM Exam Content Outline - AI Risk Management (Innovation and Risk Control) AI Security Management Study Guide - Phased Implementation Strategies
NEW QUESTION # 111
A large corporation has received an influx of sophisticated credential-phishing emails and wants to leverage an AI solution to detect and quarantine these messages before they reach employees. Which of the following blue-team AI features is BEST suited to this task?
- A. Natural language processing (NLP)
- B. Retrieval-augmented generation (RAG)
- C. Large language model (LLM)
- D. Natural language generation (NLG)
Answer: A
Explanation:
For pre-delivery phishing detection and classification, the most appropriate capability is NLP- tokenization, feature extraction, semantic similarity, and supervised classifiers (e.g., transformer-based classifiers) tuned on phishing corpora and indicators. NLP models score messages and drive automated quarantine policies. An LLM (Option A) is a model type, not a specific blue-team feature; NLG (Option C) is for generation, not detection; RAG (Option D) augments responses with retrieved knowledge but does not by itself optimize classification and quarantine of phishing emails.
References:
AAISM Body of Knowledge: Defensive AI Use Cases; Text Classification Pipelines for Security Operations; Email Security and AI-Driven Triage.
AAISM Study Guide: NLP for Threat Detection; Model Evaluation for Precision/Recall in Security Classifiers; SOC Integration and Automated Containment.
NEW QUESTION # 112
Which of the following is the MOST critical success factor for an AI implementation project?
- A. Ensuring AI risk is captured in the risk register
- B. Mapping data throughout the life cycle
- C. Developing and using model cards
- D. Obtaining senior management buy-in
Answer: D
Explanation:
AAISM identifies executive sponsorship and senior management buy-in as the foremost success factor for AI initiatives. It secures resources, resolves cross-functional conflicts, sets risk appetite, and enforces adherence to governance and controls. Model cards (A), risk registers (B), and lifecycle data mapping (C) are vital practices within the program, but without top-level commitment, adoption, funding, and accountability often fail.
References: AI Security Management (AAISM) Body of Knowledge - AI Program Governance; Executive Sponsorship & Accountability; Strategy-to-Control Alignment for Successful AI Delivery.
NEW QUESTION # 113
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. Lack of sufficient computing resources for the AI system
- B. Excessive reliance on external consultants for model design
- C. Absence of metrics and dashboard for analysts
- D. Insufficient model validation and change control processes
Answer: D
Explanation:
AAISM requires formal model change governance: documented justification, risk assessment, validation
/verification (V&V), approvals, and post-deployment monitoring when altering features, thresholds, or signals. Disabling risk indicators to reduce false positives without rigorous validation and controlled rollout reflects a failure in model validation and change control, which AAISM treats as a core safeguard against unintended harms and regulatory breaches.
References: AI Security Management (AAISM) Body of Knowledge - Model Risk Governance; Change Management & Approvals; Validation/Verification Requirements. AAISM Study Guide - Control Gates for Feature/Threshold Changes; Post-Change Monitoring and Backout Criteria.
NEW QUESTION # 114
Which of the following is the GREATEST benefit of performing AI security risk assessments?
- A. Appropriate privacy risk controls are implemented for AI models
- B. The risk register is updated with the latest AI risk
- C. The appropriate level of funding is secured for AI security risk
- 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 # 115
Cybersecurity teams should FIRST be embedded in the:
- A. Model training phase
- B. Model deployment phase
- C. Model design phase
- D. Model testing phase
Answer: C
Explanation:
AAISM stresses that security must be embedded from the earliest phase-design, ensuring:
* threat modeling
* secure architecture
* data protection requirements
* system boundaries
* security-by-design principles
Introducing cybersecurity later increases unmitigated vulnerabilities.
Testing (A), training (C), and deployment (B) occur after foundational security decisions have already been made.
References: AAISM Study Guide - Secure AI Lifecycle; Early Security Involvement.
NEW QUESTION # 116
An organization decides to contract a vendor to implement a new set of AI libraries. Which of the following is MOST important to address in the master service agreement to protect data used during the AI training process?
- A. Continuous data monitoring
- B. Right to audit
- C. Data pseudonymization
- D. Independent certification
Answer: B
Explanation:
AAISM emphasizes that the right to audit is the most critical contractual safeguard when outsourcing AI services. This allows the contracting organization to independently verify that the vendor is applying appropriate protections to training data, meeting compliance obligations, and upholding privacy requirements.
Pseudonymization is a technical method, monitoring is operational, and certifications provide external assurance, but none give the direct, enforceable oversight that audit rights provide. In vendor contracts, the right to audit is the primary safeguard for data protection and governance.
References:
AAISM Study Guide - AI Governance and Program Management (Third-Party Contracts and Audit Rights) ISACA AI Security Management - Vendor Governance Controls
NEW QUESTION # 117
Which of the following should be the MOST important consideration when conducting an AI impact assessment?
- A. Achieve business objectives
- B. Effect on employee retention
- C. Reputation of the organization
- D. Security awareness training
Answer: A
Explanation:
An AI impact assessment prioritizes alignment with stated business objectives and intended purpose of the AI use case. Establishing whether the AI system measurably supports defined objectives (value, outcomes, success criteria) is the primary lens before assessing secondary concerns (workforce effects, awareness initiatives, reputational considerations). Governance requires confirming purpose limitation, success metrics, and risk-benefit trade-offs tied to organizational objectives; only then are downstream impacts evaluated.
References: AI Security Management (AAISM) Body of Knowledge - AI Governance & Impact Assessments; Purpose and Objective Definition; Risk-Benefit Evaluation. AAISM Study Guide - Business Alignment, Intended Use, and Success Criteria in AI Impact Assessments.
NEW QUESTION # 118
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. Postpone control selection until deployment and address risk through enhanced monitoring
- B. Apply AI-specific controls from external frameworks without customization and initiate monitoring to expedite compliance
- C. Rely primarily on vendor-provided security features and seek third-party certifications
- D. Map identified AI threats to enterprise control catalogs and integrate AI-specific safeguards where gaps exist
Answer: D
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 # 119
Which of the following information is MOST important to include in a centralized AI inventory?
- A. Training data sets
- B. AI model use cases
- C. Ownership and accountability of AI systems
- D. Foundation model and package registry
Answer: C
Explanation:
AAISM governance practices identify ownership and accountability as the most critical element in any centralized AI inventory. An AI inventory provides oversight by cataloging all AI assets within an organization, and assigning responsibility ensures that each system has clear governance, monitoring, and compliance coverage. While use cases, training data, and registries are valuable metadata, they do not guarantee accountability. Without defined ownership, no party is responsible for addressing risk, bias, or incidents. Therefore, the most important information to include is ownership and accountability details for each AI system.
References:
AAISM Exam Content Outline - AI Governance and Program Management (AI Inventories and Oversight) AI Security Management Study Guide - Ownership and Accountability Structures
NEW QUESTION # 120
Which of the following is the BEST way to ensure role clarity and staff effectiveness when implementing AI- assisted security monitoring tools?
- A. Defer implementation until the security team can be expanded with data scientists.
- B. Increase training budgets for business staff to obtain vendor-neutral AI certifications.
- C. Transition responsibilities for AI tools to external consultants for improved scalability.
- D. Update the security program to include cross-functional AI-specific responsibilities.
Answer: D
Explanation:
AAISM directs that AI adoption in security operations be governed through explicit operating models and RACI-mapped responsibilities spanning security operations, data science/ML, platform engineering, privacy, and compliance. Role clarity comes from updating the security program to codify AI-specific responsibilities (model monitoring, incident handling for AI failures, data governance, change control for models, bias/fairness reviews, supplier assurance) rather than deferring implementation, outsourcing core accountability, or relying on generic certifications. This ensures measurable accountability, reduces hand-off risk, and aligns day-to-day SOC practices with AI control objectives.
References:* AI Security Management (AAISM) Body of Knowledge: AI Governance Operating Model; Roles & Responsibilities; RACI for AI-enabled Security Operations* AAISM Study Guide: Program Governance, Control Objective Mapping to SOC Workflows; Cross-Functional Ownership for AI Controls
NEW QUESTION # 121
A data scientist creating categories and training the algorithm on large data sets is an example of which type of AI model learning technique?
- A. Unsupervised
- B. Supervised
- C. Reinforcement
- D. Machine learning (ML)
Answer: B
Explanation:
AAISM classifies learning paradigms by the presence of labeled targets. Creating categories (labels) and training on them is supervised learning, where input features are mapped to known outputs and optimization minimizes prediction error against ground truth. Unsupervised (B) discovers structure without labels; reinforcement (A) optimizes behavior via rewards; "machine learning" (C) is the broad field, not the specific technique.
References: AI Security Management (AAISM) Body of Knowledge - AI/ML Foundations; Learning Paradigms and Data Requirements. AAISM Study Guide - Supervised vs. Unsupervised vs. Reinforcement Learning; Label Quality and Model Performance Dependencies.
NEW QUESTION # 122
Which of the following should be included in an AI acceptable use policy?
- A. Data collection and storage processes
- B. Ethical and legal compliance standards
- C. AI training data requirements
- D. AI monitoring requirements
Answer: B
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 # 123
Which of the following datasets is used to tune hyperparameters?
- A. Configuration
- B. Test
- C. Validation
- D. Training
Answer: C
Explanation:
Per AAISM's ML lifecycle controls, hyperparameter tuning is performed on the validation set, reserving the test set strictly for final, unbiased performance estimation. The training set is used to fit parameters; the validation set guides model selection and hyperparameter optimization; the test set is untouched until the end to prevent leakage and optimistic bias. "Configuration" is not a dataset type in the lifecycle split.
References:* AI Security Management™ (AAISM) Body of Knowledge: Model Development Controls- Data Splitting and Evaluation Integrity* AAISM Study Guide: Overfitting Avoidance; Validation vs. Test Separation; Leakage Prevention* AAISM Mapping to Standards: Evaluation Integrity-Hold-out Protocols and Tuning Practices
NEW QUESTION # 124
Which BEST describes the role of model cards in AI solutions?
- A. They document training data and AI model use cases
- B. They automatically fine-tune AI models
- C. They help developers create synthetic data
- D. They visualize AI model performance
Answer: A
Explanation:
AAISM explains that model cards provide structured documentation about AI models, including:
* intended use cases
* training data characteristics
* ethical considerations
* known limitations
* risk factors
* performance benchmarks
They are not visualization tools (A), do not create synthetic data (C), and do not tune models (D).
References: AAISM Study Guide - AI Transparency & Model Cards.
NEW QUESTION # 125
Which of the following should be the PRIMARY objective of implementing differential privacy techniques in AI models leveraging fraud detection systems?
- A. Enhancing the accuracy of predictions to desired levels
- B. Increasing model training speed for an efficient launch
- C. Reducing computational resources required for the model training phase
- D. Protecting individual data contributions while allowing statistical analysis
Answer: D
Explanation:
Differential privacy aims to protect the privacy of any single individual's data contribution while still enabling useful aggregate learning and statistical analysis. Noise mechanisms are calibrated so that results remain informative for modeling (e.g., fraud patterns) without revealing whether any particular person's data was included or enabling inference about them. Accuracy, speed, and compute efficiency can be secondary considerations, but the primary objective is privacy protection with utility preserved.
References: AI Security Management (AAISM) Body of Knowledge: Privacy-Preserving ML; Differential Privacy Objectives and Mechanisms. AAISM Study Guide: Individual Contribution Protection; Utility- Privacy Trade-offs and Calibration in Applied Models.
NEW QUESTION # 126
......
Authentic Best resources for AAISM Online Practice Exam: https://www.exams4sures.com/ISACA/AAISM-practice-exam-dumps.html
Get the superior quality AAISM Dumps with explanations waiting just for you, get it now: https://drive.google.com/open?id=1sfUpFRwbj28mj5npSw01UqbB1B--7rJ3