[Oct 09, 2026] New Real AAISM Exam Dumps Questions [Q216-Q232]

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[Oct 09, 2026] New Real AAISM Exam Dumps Questions

Pass Your AAISM Exam Easily with Accurate ISACA Advanced in AI Security Management (AAISM) Exam PDF Questions

ISACA AAISM Exam Syllabus Topics:

Topic	Details
Topic 1	AI Governance and Program Management: This section of the exam measures the abilities of AI Security Governance Professionals and focuses on advising stakeholders in implementing AI security through governance frameworks, policy creation, data lifecycle management, program development, and incident response protocols.
Topic 2	AI Risk Management: This section of the exam measures the skills of AI Risk Managers and covers assessing enterprise threats, vulnerabilities, and supply chain risk associated with AI adoption, including risk treatment plans and vendor oversight.
Topic 3	AI Technologies and Controls: This section of the exam measures the expertise of AI Security Architects and assesses knowledge in designing secure AI architecture and controls. It addresses privacy, ethical, and trust concerns, data management controls, monitoring mechanisms, and security control implementation tailored to AI systems.

NEW QUESTION 216
Which of the following is the GREATEST contributor to end user security risk associated with generative AI?

Insufficient awareness training

Lack of data classification

Focus on unintentional errors

Lack of insider threat assessments

End users are a primary interface with generative AI, and without sufficient awareness training they may unknowingly misuse the technology, expose sensitive data, or fall victim to AI-enabled threats, making this the largest contributor to security risk.

NEW QUESTION 217
A health organization wants to use an AI tool for diagnostic decisions. Which of the following would BEST help the organization comply with privacy regulations?

De-identifying training data

Sanitizing training data

Conducting a privacy impact assessment (PIA)

Pseudonymizing AI model output data

Conducting a privacy impact assessment (PIA) is the best way to help ensure compliance with privacy regulations because it systematically identifies, evaluates, and mitigates privacy risks associated with the collection, processing, and use of sensitive health data within the AI diagnostic system.

NEW QUESTION 218
A viral video shows a blurry person making claims about a product safety issue. The video has random low- quality sections. This MOST likely represents what threat?

Hallucinations

Model drift

Data poisoning

Deepfake

AAISM defines deepfakes as manipulated media where individuals appear in synthetic or altered video/audio.
Indicators include:
* blurred or inconsistent facial rendering
* mismatched frames
* low-quality or distorted transitions
These characteristics match the scenario provided.
Hallucinations (A) relate to model outputs, not video manipulation. Drift (B) affects model performance.
Poisoning (C) affects training data, not video content.
References: AAISM Study Guide – AI-Generated Media Threats; Deepfake Identification.

NEW QUESTION 219
Which of the following reviews MUST be conducted as part of an AI impact assessment?

Testing, evaluation, validation, and verification

Evaluation of model reproducibility

Security control self-assessment (CSA)

Identification of environmental and societal consequences

The AAISM material frames an AI impact assessment as broader than a technical model review. It emphasizes that governance of AI includes assessing societal, environmental, and human-rights impacts in addition to organizational risk. The official guidance notes that high-level impact assessments must explicitly consider “potential consequences for individuals, communities, and the environment” as part of responsible AI governance. Activities such as testing/validation (A) and reproducibility (B) are critical in the development lifecycle but are not, by themselves, sufficient to constitute an impact assessment. A CSA (C) is focused on security controls, which is narrower than overall impact. The mandatory element that differentiates an AI impact assessment is the assessment of environmental and societal consequences, making option D the correct answer in alignment with governance expectations.
References: AI Security Management™ (AAISM) Study Guide – AI Governance and Impact Assessments; Responsible and Trustworthy AI Section.

NEW QUESTION 220
Which of the following is the MOST effective way to mitigate the effects of outdated data in a large language model (LLM)?

Replacing the model with a more complex model in the solution

Adding a new model trained with new information to the solution

Deploying retrieval-augmented generation (RAG) data to the model

Retraining the model periodically with an updated training dataset

Retrieval-augmented generation (RAG) enables the LLM to access current external information at query time instead of relying solely on static training data. This is the most effective way to mitigate outdated knowledge because it provides dynamically updated and context-relevant information without requiring full model retraining.

NEW QUESTION 221
An organization has discovered that employees have started regularly utilizing open-source generative AI without formal guidance. Which of the following should be the CISO’s GREATEST concern?

Lack of monitoring

Policy violations

Data leakage

Model hallucinations

The greatest immediate risk from unsanctioned use of public or open-source generative AI tools is data leakage-employees may paste confidential or regulated information into third-party systems, resulting in loss of confidentiality, regulatory exposure, and loss of intellectual property. AAISM emphasizes that when AI use occurs outside approved channels, the top control priority is preventing exfiltration of sensitive data via prompts, attachments, and context sharing. Monitoring and policy are necessary enablers, but leakage is the highest-impact failure mode in the short term; hallucinations primarily affect accuracy, not confidentiality.
References:* AI Security Management (AAISM) Body of Knowledge: Generative AI governance; human- in-the-loop risks; data loss and exfiltration vectors in prompts; sanctioned vs. unsanctioned AI usage.* AI Security Management Study Guide: Immediate risk triage for shadow AI; DLP and input-control safeguards; confidentiality-first posture for generative AI adoption.

NEW QUESTION 222
Which of the following should be included in an AI acceptable use policy?

AI training data requirements

Data collection and storage processes

Ethical and legal compliance standards

AI monitoring requirements

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.

NEW QUESTION 223
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?

Industry benchmarking peer review

Third-party audit reports

Internal red-team testing reports

General AI security whitepapers

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 224
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?

Rely primarily on vendor-provided security features and seek third-party certifications

Map identified AI threats to enterprise control catalogs and integrate AI-specific safeguards where gaps exist

Apply AI-specific controls from external frameworks without customization and initiate monitoring to expedite compliance

Postpone control selection until deployment and address risk through enhanced monitoring

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 225
When designing self-driving vehicles, conformity assessment is PRIMARILY needed to:

assess compliance against AI regulations and frameworks.

verify that the AI system operates reliably.

test for security flaws before the AI system goes live.

protect against unfair decisions.

Conformity assessment is primarily conducted to evaluate whether an AI system complies with applicable regulations, standards, and governance frameworks. For self-driving vehicles, this is critical because they are high-risk AI systems subject to strict regulatory and safety requirements.

NEW QUESTION 226
When integrating AI for innovation, which of the following can BEST help an organization manage security risk?

Re-evaluating the risk appetite

Seeking third-party advice

Evaluating compliance requirements

Adopting a phased approach

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.

NEW QUESTION 227
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?

Increasing the frequency of model retraining with the existing data set

Applying statistical methods to address missing data and reduce bias

Deleting outlier data points to prevent unusual values impacting the model

Tuning model hyperparameters to increase performance and accuracy

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 228
Which of the following is BEST to review in order to detect issues related to data used throughout an AI system’s life cycle?

Data quality requirements

User access logs

Data retention policies

Data flow diagrams

Data flow diagrams provide a comprehensive view of how data moves through the system across all stages of the AI life cycle, enabling identification of issues related to data handling, transformation, and usage.

NEW QUESTION 229
Which of the following is the BEST reason to immediately disable an AI system?

Excessive model drift

Slow model performance

Overly detailed model outputs

Insufficient model training

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.

NEW QUESTION 230
A programmer suspects an AI system is inferring sensitive user information. What is the BEST action?

Inform the governance panel

Suggest fine-tuning

Conduct a code review

Alert the CIO

AAISM directs that potential privacy, ethical, or compliance risks must be escalated to the AI Governance Panel, the body responsible for oversight, risk approval, and corrective action.
Fine-tuning (B) is premature and may worsen risk. Code review (C) does not address model-level inference issues. Escalating directly to the CIO (D) bypasses the required governance process.
References: AAISM Study Guide – AI Governance Committees; Escalation Procedures.

NEW QUESTION 231
An organization’s CIO provided the AI steering committee with a list of AI technologies in use and tasked them with categorizing the technologies by risk. Which of the following should the committee do FIRST?

Begin grouping similar AI products and solutions together

Identify vulnerabilities related to the technologies in use

Ensure the AI technologies are included in the asset inventory

Assess risk levels based on risk appetite and regulatory requirements

AAISM governance practices state that before categorizing technologies by risk, the first step is to ensure that all AI systems are documented in the organizational asset inventory. A complete inventory provides the foundation for subsequent risk analysis, accountability, and governance.
Grouping solutions, identifying vulnerabilities, and assessing risk levels come afterward, once inventory accuracy is established. Without confirming that the technologies are recorded in the inventory, risk categorization may miss critical assets.

NEW QUESTION 232
A bank is training an AI credit scoring model on a vast amount of sensitive customer data. Which of the following is the MOST critical security risk in this training process?

Model inversion or membership inference attacks

Resources needed to retrain and validate the AI model

Algorithmic bias leading to discriminatory lending

Data poisoning to corrupt training outcomes

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