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ISACA AAISM Exam Overview:

Certification Vendor:	ISACA
Exam Name:	ISACA Advanced in AI Security Management (AAISM) Exam
Exam Number:	AAISM
Passing Score:	450 (out of 800)
Certificate Validity Period:	3 years
Exam Format:	Multiple-choice, Scenario-based
Exam Duration:	150 minutes
Real Exam Qty:	90
Exam Price:	US$459 (members), US$599 (non-members)
Related Certifications:	CISM CISSP
Available Languages:	English
Recommended Training:	ISACA Official AAISM Training
Exam Registration:	ISACA AAISM Registration
Sample Questions:	ISACA AAISM Sample Questions
Exam Way:	Computer-based; available at authorized PSI testing centers or via remote proctoring
Pre Condition:	Hold an active CISM or CISSP certification
Official Syllabus URL:	https://www.isaca.org/credentialing/aaism/aaism-exam-content-outline

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Free PDF 2026 ISACA High Hit-Rate AAISM: Training ISACA Advanced in AI Security Management (AAISM) Exam Materials

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ISACA AAISM Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 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.

ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q264-Q269):

NEW QUESTION # 264
A healthcare provider plans to use an external vendor's AI system to analyze patient medical images. Management is concerned about protecting patient data and managing the risk of the new technology. Which of the following should be done FIRST before signing the vendor contract?

A. Include guaranteed 24/7 system support and technical performance requirements in the contract terms.
B. Review the vendor's data handling, regulatory compliance, and supply chain dependencies.
C. Ask patients to sign consent forms acknowledging that AI will be used in their care.
D. Focus only on the vendors with a good reputation in healthcare and their industry certifications.

Answer: B

Explanation:
Reviewing the vendor's data handling practices, compliance with healthcare regulations, and supply chain dependencies ensures that risks related to patient data protection and third-party exposure are understood and addressed before entering into a contractual agreement.

NEW QUESTION # 265
During red-team testing of an AI system used for lending decisions, which technique BEST simulates a data poisoning attack?

A. Inputting encrypted data
B. Corrupting training datasets to manipulate outcomes
C. Stealing model weights
D. Adding noise to output predictions

Answer: B

Explanation:
AAISM defines data poisoning as intentional manipulation of the training data to influence model behavior or outputs. Corrupting training data (D) is the exact definition of this attack type.

NEW QUESTION # 266
A programmer suspects an AI system is inferring sensitive user information. What is the BEST action?

A. Suggest fine-tuning
B. Conduct a code review
C. Inform the governance panel
D. Alert the CIO

Answer: C

Explanation:
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 # 267
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. Applying statistical methods to address missing data and reduce bias
C. Deleting outlier data points to prevent unusual values impacting the model
D. Increasing the frequency of model retraining with the existing data set

Answer: B

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 # 268
Employees are regularly using open-source generative AI without guidance. What should be the CISO's GREATEST concern?

A. Data leakage
B. Policy violations
C. Lack of monitoring
D. Model hallucinations

Answer: A

Explanation:
AAISM emphasizes that the primary and most severe risk of uncontrolled generative AI usage is data leakage, including:
* confidential data
* regulated personal data
* intellectual property
While hallucinations (A) are harmful, they do not cause direct data loss. Lack of monitoring (C) is a secondary risk. Policy violations (D) occur because of lack of controls but do not outweigh the impact of leaked sensitive data.
References: AAISM Study Guide - Generative AI Risks; Human-Initiated Data Exposure.

NEW QUESTION # 269
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