AAISM Dumps PDF New [2026] Ultimate Study Guide
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ISACA AAISM Exam Syllabus Topics:
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NEW QUESTION # 118
When evaluating a new AI tool for intrusion prevention, which of the following is the MOST important consideration to ensure the tool fits within the existing program architecture?
- A. Ensure automated response orchestration.
- B. Select a tool that integrates with the existing SIEM.
- C. Prioritize a tool that offers real-time anomaly detection.
- D. Confirm tool capabilities align with the control objectives.
Answer: D
Explanation:
The highest-priority fit criterion for introducing a new AI security capability is alignment to the organization' s established control objectives and program architectures. Control objectives encode what must be achieved (e.g., detection coverage, response timeliness, accountability, auditability) and are the basis for requirements traceability across governance, risk, and technical controls. Ensuring the tool's capabilities directly satisfy those objectives provides architectural fit, policy conformance, and measurable assurance. While integration (e.g., SIEM), detection features (e.g., real-time anomaly detection), and orchestration are important, they are secondary to proving the tool maps to-and can be verified against-the control objectives that define the program's intended outcomes.
References:* AI Security Management (AAISM) Body of Knowledge: AI Governance and Program Management - Security program alignment, control objectives, and requirements traceability* AI Security Management Study Guide: Control objective mapping, architecture fit criteria, and solution selection governance
NEW QUESTION # 119
Which of the following BEST describes an adversarial attack on an AI model?
- A. Providing inputs that mislead the model into incorrect predictions
- B. Reverse-engineering the model using social engineering
- C. Conducting denial-of-service attacks on AI APIs
- D. Attacking underlying hardware
Answer: A
Explanation:
AAISM defines adversarial attacks as manipulations of input data (text, image, audio, numeric values) designed to cause the model to produce incorrect or harmful predictions.
Hardware attacks (A) are infrastructure threats. Social engineering (C) targets people, not models. DoS attacks (D) affect availability, not model decision pathways.
References: AAISM Study Guide - Adversarial Threats; Input Manipulation.
NEW QUESTION # 120
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. Control self-assessment (CSA)
- C. Explainable decision-making
- D. Model validation
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 # 121
When robust input controls are not practical on a large language model (LLM) to prevent prompt injection attacks from external threats, which of the following would be the BEST compensating control to address the risk?
- A. Fine-tune the system to validate the AI system's inputs
- B. Implement identity and access management (IAM)
- C. Conduct human reviews of the AI system's inputs
- D. Review and annotate the AI system's outputs
Answer: D
Explanation:
When preventive input hardening isn't feasible for LLMs, AAISM prescribes compensating detective and corrective controls-notably human review and annotation of outputs prior to downstream action-to reduce harm from prompt injection. Output-side review gates prevent untrusted instructions from propagating, enable rapid suppression/feedback loops, and provide labeled examples for subsequent model hardening. IAM (B) is necessary but does not mitigate injection in content; reviewing inputs (C) is less effective than auditing what the model is about to act on; fine-tuning for validation (D) is helpful long-term but is not an immediate compensating control when robust input validation is impractical.
References: AI Security Management (AAISM) Body of Knowledge - LLM Threats & Compensating Controls; Human Oversight & Output Review Gates; Post-incident Feedback and Labeling for Model Hardening.
NEW QUESTION # 122
Security and assurance requirements for AI systems 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 directs organizations to embed security, safety, and compliance controls at design time ("secure-by- design" and "shift-left"), ensuring requirements for robustness, privacy, and governance are defined as non- functional constraints on architecture, data sourcing, model choices, and evaluation criteria before any model is trained. Deferring these requirements to training, testing, or deployment increases residual risk and rework, and weakens traceability of control coverage.
References:* AI Security Management™ (AAISM) Body of Knowledge: Governance-Secure-by-Design; Policy-to-Control Traceability; Requirements Management* AAISM Study Guide: AI Program Lifecycle- Planning & Design Controls; Design-time Threat Modeling and Control Selection* AAISM Mapping to Standards: Design-phase Risk Identification and Requirements Engineering for AI
NEW QUESTION # 123
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. Model inversion
- B. Deepfake
- C. Data poisoning
- D. Availability attack
Answer: D
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 # 124
An organization is implementing AI agent development across engineering teams. What should AI-specific training focus on?
- A. Prompt injection, agent memory control, insecure tool execution
- B. API abuse, data leakage, third-party plug-in risk
- C. Dataset bias, explainability, fairness
- D. Output moderation, hallucination handling, policy alignment
Answer: A
Explanation:
AAISM states that AI agent security training should focus on the unique risks of agentic systems, which include:
* prompt injection
* memory control and context hijacking
* unsafe tool execution (agents triggering unauthorized actions)
These risks are specific to autonomous or semi-autonomous AI agents.
Bias, fairness (B) and output moderation (C) are important but not the most critical for agent security. API abuse and plug-in risk (D) matter but are secondary.
References: AAISM Study Guide - Agentic AI Security; Prompt Injection and Tool Execution Risks.
NEW QUESTION # 125
Personal data used to train AI systems can BEST be protected by:
- A. Erasing personal data after training
- B. Ensuring the quality of personal data
- C. Anonymizing personal data
- D. Hashing personal data
Answer: C
Explanation:
AAISM guidance on privacy-preserving AI highlights anonymization as the most effective means of protecting personal data used in training. By irreversibly removing or masking identifiable attributes, anonymization ensures that training data cannot be linked back to individuals, thereby meeting key privacy obligations under laws such as GDPR. Erasing data after training may limit exposure but does not protect it during the training process. Ensuring data quality improves accuracy but does not mitigate privacy risk.
Hashing protects data integrity but does not guarantee anonymity, as hashes can sometimes be reversed or correlated. Therefore, anonymization is the recommended control for protecting personal data in AI training.
References:
AAISM Study Guide - AI Technologies and Controls (Privacy-Preserving Methods) ISACA AI Security Management - Data Anonymization Practices
NEW QUESTION # 126
Which of the following controls BEST mitigates the risk of bias in AI models?
- A. Regular data reconciliation
- B. Cryptographic hash functions
- C. Robust access control techniques
- D. Diverse data sourcing strategies
Answer: D
Explanation:
Bias in AI models primarily stems from limitations or imbalances in training data. The AAISM study materials emphasize that the most effective way to mitigate this risk is through diverse data sourcing strategies that ensure coverage across demographics, scenarios, and contexts. Access controls protect data security, not fairness. Data reconciliation ensures accuracy but does not address representational imbalance.
Cryptographic hashing preserves integrity but has no impact on bias mitigation. To reduce systemic unfairness, the critical control is sourcing diverse and representative data.
References:
AAISM Exam Content Outline - AI Technologies and Controls (Bias and Fairness Management) AI Security Management Study Guide - Data Governance and Bias Reduction Strategies
NEW QUESTION # 127
Which of the following is MOST important to consider when validating a third-party AI tool?
- A. Roundtable testing
- B. Industry analysis and certifications
- C. Terms and conditions
- D. Right to audit
Answer: D
Explanation:
The AAISM framework specifies that when adopting third-party AI tools, the right to audit is the most critical contractual and governance safeguard. This ensures that the organization can independently verify compliance with security, privacy, and ethical requirements throughout the lifecycle of the tool. Terms and conditions provide general usage guidance but often limit liability rather than ensuring transparency. Industry certifications may indicate good practice but do not substitute for direct verification. Roundtable testing is useful for evaluation but lacks enforceability. Only the contractual right to audit provides formal assurance that the tool operates in accordance with organizational policies and external regulations.
References:
AAISM Exam Content Outline - AI Governance and Program Management (Third-Party Governance) AI Security Management Study Guide - Vendor Oversight and Audit Rights
NEW QUESTION # 128
A SaaS-based LLM system has risks including prompt injection, data poisoning, and model exfiltration. What is the BEST way to ensure consistent risk treatment?
- A. Focus on post-deployment red teaming
- B. Apply control baselines from a recognized industry standard
- C. Rely on vendor audit reports and SLAs
- D. Implement an AI threat control matrix mapping threats to controls and assurance
Answer: D
Explanation:
AAISM highlights the importance of an AI threat-control matrix that:
* maps threats (e.g., poisoning, exfiltration, injection)
* identifies required controls
* defines assurance/testing activities
* ensures consistent coverage across all AI components
Control baselines (A) are useful but not AI-specific. Red teaming (C) is reactive and not comprehensive.
Vendor reports (D) are supplemental, not sufficient.
References: AAISM Study Guide - AI Risk Treatment; Threat-Control Mapping.
NEW QUESTION # 129
An organization is looking to purchase an AI application from a vendor but is concerned about the security of its data. Which of the following is the MOST effective way to address this concern?
- A. Initiate discussions between the organization's and the vendor's legal teams
- B. Assess the vendor's publicly available AI usage policy
- C. Ensure vendors disclose how the application uses the organization's data
- D. Mandate an AI security audit by an external auditor before procurement
Answer: C
Explanation:
AAISM's approach to third-party and vendor risk for AI systems stresses data usage transparency as a primary control. The guidance explains that organizations must obtain clear documentation on "what data is collected, how it is processed, stored, retained, and whether it is reused for training or shared with other parties." Option C directly addresses this by requiring the vendor to disclose how the application uses organizational data, enabling appropriate risk assessment, contractual controls, and technical safeguards. An external audit (A) can be useful but may be costly and not always feasible pre-procurement. Legal discussions (B) are important but ineffective without clarity on data flows. Publicly available policies (D) are often high- level and marketing-oriented, lacking the specificity required for proper risk evaluation. Therefore, obtaining explicit data usage disclosures from the vendor is the most effective starting point.
References: AI Security Management™ (AAISM) Study Guide - Third-Party AI Risk and Data Sharing; Vendor Governance Requirements.
NEW QUESTION # 130
Which of the following strategies is the MOST effective way to protect against AI data poisoning?
- A. Increasing model complexity to better handle data variations
- B. Using robust data validation techniques and anomaly detection
- C. Ensuring the model is trained on diverse data sources
- D. Incorporating more features and data into model training
Answer: B
Explanation:
AAISM directs organizations to prevent training-time attacks by hard-gating data ingestion with provenance checks, schema and label validation, sanitization, and anomaly/outlier detection prior to model training. These controls most directly block poisoned records from entering the pipeline and are prioritized over architectural complexity or sheer data volume. Diversity of sources can improve representativeness but does not reliably stop adversarial contamination.
References: AI Security Management (AAISM) Body of Knowledge - Adversarial ML: Training-Time Threats; Secure Data Ingestion & Validation Controls; AI Risk Treatment and Assurance. AAISM Study Guide - Poisoning Prevention Gates; Provenance, Quality, and Anomaly Screening in ML Pipelines.
NEW QUESTION # 131
Which of the following technologies can be used to manage deepfake risk?
- A. Adaptive authentication
- B. Multi-factor authentication (MFA)
- C. Blockchain
- D. Systematic data tagging
Answer: C
Explanation:
The AAISM study material highlights blockchain as a control mechanism for managing deepfake risk because it provides immutable verification of digital media provenance. By anchoring original data signatures on a blockchain, organizations can verify authenticity and detect tampered or synthetic content. Data tagging helps organize but does not guarantee authenticity. MFA and adaptive authentication strengthen identity security but do not address content manipulation risks. Blockchain's immutability and traceability make it the recognized technology for mitigating deepfake challenges.
References:
AAISM Study Guide - AI Technologies and Controls (Emerging Controls for Content Authenticity) ISACA AI Governance Guidance - Blockchain for Data Integrity and Deepfake Mitigation
NEW QUESTION # 132
Which of the following types of testing can MOST effectively mitigate prompt hacking?
- A. Input
- B. Regression
- C. Load
- D. Adversarial
Answer: D
Explanation:
Prompt hacking manipulates large language models by injecting adversarial instructions into inputs to bypass or override safeguards. The AAISM framework identifies adversarial testing as the most effective way to simulate such manipulative attempts, expose vulnerabilities, and improve the resilience of controls. Load testing evaluates performance, input testing checks format validation, and regression testing validates functionality after changes. None of these directly address the manipulation of natural language inputs.
Adversarial testing is therefore the correct approach to mitigate prompt hacking risks.
References:
AAISM Exam Content Outline - AI Risk Management (Testing and Assurance Practices) AI Security Management Study Guide - Adversarial Testing Against Prompt Manipulation
NEW QUESTION # 133
Which of the following BEST reduces the risk of exposing sensitive data through the output of large language models (LLMs) in applications?
- A. Enforcing least privilege access
- B. Encrypting data in transit and at rest
- C. Conducting adversarial testing
- D. Implementing data sanitization techniques
Answer: D
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 # 134
Which of the following AI data management techniques involves creating validation and test data?
- A. Annotating
- B. Splitting
- C. Training
- D. Learning
Answer: B
Explanation:
Data splitting partitions a labeled dataset into training, validation, and test subsets to enable unbiased model tuning and evaluation. Training (A) consumes the training split; annotating (B) adds labels; learning (D) is a general term for model optimization, not a data management step.
References: AI Security Management™ (AAISM) Body of Knowledge - Data Lifecycle Controls; Dataset Partitioning for Validation and Testing. AAISM Study Guide - Train/Validation/Test Splits and Evaluation Integrity.
NEW QUESTION # 135
When using AI as part of incident response, which of the following BEST ensures the automation aligns with regulatory and governance obligations?
- A. Use deep learning models to autonomously classify all incidents
- B. Implement a tiered automation strategy where severity ratings inform the need for human oversight
- C. Train the AI incident response platform to mirror legacy response workflows and log containment
- D. Apply anomaly detection models to filter incoming threats and automate containment
Answer: B
Explanation:
AAISM prescribes risk-based, human-in-the-loop orchestration for safety-critical or regulated actions. A tiered automation strategy that gates autonomy by incident severity, data sensitivity, and regulatory requirements ensures accountability, auditability, and proportionality, satisfying governance obligations. Full autonomy (A) risks non-compliance; simply mirroring legacy workflows (B) may not meet current obligations; broad auto-containment (C) lacks necessary oversight controls.
References: AI Security Management (AAISM) Body of Knowledge - Governance of AI-Driven Security Automation; Human Oversight and Escalation; Risk-Based Orchestration. AAISM Study Guide - Incident Response with AI: Controls, Approvals, and Auditability.
NEW QUESTION # 136
Which defense is MOST effective against cyberattacks that alter input data to avoid detection?
- A. Conducting periodic monitoring of decisions
- B. Restricting access to internal model parameters
- C. Enhancing model robustness through adversarial training
- D. Applying differential privacy to training data
Answer: C
Explanation:
AAISM lists adversarial training as the strongest method to harden models against input manipulation attacks.
By exposing models to adversarial examples during training, the system learns to resist evasion techniques.
Access restriction (B) protects confidentiality, not detection evasion. Monitoring (C) is reactive, not preventive. Differential privacy (D) protects individual data, not adversarial inputs.
References: AAISM Study Guide - AI Evasion Attacks; Adversarial Training Mitigation.
NEW QUESTION # 137
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