New Release AAISM PDF Dumps [2026] - AAISM ISACA Advanced in AI Security Management (AAISM) Exam Exam Questions

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ISACA AAISM Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: AI Risk Management	31%	- Third-party and supply chain risk management 1. Supply chain security controls 2. Vendor risk evaluation - AI risk assessment and treatment 1. Risk thresholds and mitigation 2. Risk identification and analysis - AI threat and vulnerability management 1. AI vulnerability assessment 2. Threat modeling for AI systems
Topic 2: AI Governance and Program Management	31%	- Stakeholder considerations and governance frameworks 1. AI governance models and oversight 2. Industry frameworks and regulatory requirements - AI security program management 1. Incident response and business continuity 2. Program development and maintenance - AI policies and procedures 1. Policy implementation and compliance 2. AI-related strategy development - AI asset and data lifecycle management 1. AI asset inventory and management 2. Data governance
Topic 3: AI Technologies and Controls	38%	- AI security monitoring and operations 1. Operational AI controls 2. Security monitoring and logging - AI security architecture and design 1. Secure AI system architecture 2. AI infrastructure protection - Trust, safety and compliance controls 1. Responsible AI practices 2. Compliance and governance validation - Data management and privacy controls 1. Data integrity and protection 2. Privacy and ethical AI controls

Section

Weight

Objectives

Topic 1: AI Risk Management

31%

- Third-party and supply chain risk management

1. Supply chain security controls
2. Vendor risk evaluation

- AI risk assessment and treatment

1. Risk thresholds and mitigation
2. Risk identification and analysis

- AI threat and vulnerability management

1. AI vulnerability assessment
2. Threat modeling for AI systems

Topic 2: AI Governance and Program Management

31%

- Stakeholder considerations and governance frameworks

1. AI governance models and oversight
2. Industry frameworks and regulatory requirements

- AI security program management

1. Incident response and business continuity
2. Program development and maintenance

- AI policies and procedures

1. Policy implementation and compliance
2. AI-related strategy development

- AI asset and data lifecycle management

1. AI asset inventory and management
2. Data governance

Topic 3: AI Technologies and Controls

38%

- AI security monitoring and operations

1. Operational AI controls
2. Security monitoring and logging

- AI security architecture and design

1. Secure AI system architecture
2. AI infrastructure protection

- Trust, safety and compliance controls

1. Responsible AI practices
2. Compliance and governance validation

- Data management and privacy controls

1. Data integrity and protection
2. Privacy and ethical AI controls

>> Top AAISM Questions <<

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q343-Q348):

NEW QUESTION # 343
Which of the following would BEST ensure a proper business continuity plan (BCP) is in place for an AI solution?

A. Increasing the detail of AI solution backup and restoration processes
B. Testing the AI infrastructure failover mechanisms
C. Enhancing monitoring and detection of model failures and anomalies
D. Implementing access controls to protect the AI system from unauthorized use

Answer: B

Explanation:
Effective AI BCP requires validation through exercises and controlled failover tests to prove recovery objectives can be met in practice. Merely documenting backups (Option D), hardening access (Option B), or improving monitoring (Option A) does not confirm that the AI stack--data pipelines, feature stores, model registries, inference services, and dependent infrastructure--can actually fail over and recover within RTO/RPO. AAISM prescribes periodic BCP/DR testing (including model artifact restoration, configuration reconstitution, dependency failover, and data pipeline continuity) to verify readiness and identify gaps before real incidents.

NEW QUESTION # 344
An AI fraud detection system frequently flags legitimate transactions as suspicious, leading to customer dissatisfaction and operational obstacles. Which of the following is the BEST way to mitigate this issue without compromising security?

A. Disable the AI model temporarily and revert to manual fraud checks.
B. Increase the AI model's sensitivity threshold to reduce false positives.
C. Conduct a model review to improve accuracy and introduce explainability techniques.
D. Collect more data points such as social media activity to improve fraud predictions.

Answer: C

Explanation:
Conducting a model review helps identify why legitimate transactions are being incorrectly flagged and enables improvements to model accuracy. Adding explainability techniques also helps analysts and stakeholders better understand decision outcomes, reducing operational issues while maintaining effective fraud detection capabilities.

NEW QUESTION # 345
An AI application development team has been given access to user information and now must format it to be readable by the AI model. During which phase of the data life cycle would this MOST likely occur?

A. Data minimization
B. Data normalization
C. Data preparation
D. Data collection

Answer: C

Explanation:
According to AAISM's data life-cycle model, data preparation is the phase where raw data is transformed into a model-ready format. The materials describe this phase as including "cleaning, encoding, formatting, feature engineering, and other transformations required for model consumption." This directly matches the scenario where a team formats user information to be readable by an AI model.

NEW QUESTION # 346
AI developers often find deep learning systems difficult to explain PRIMARILY because:

A. Knowledge dynamically changes without logs
B. Neural network architectures include statistical methods not fully understood
C. Training data is spread across public domains
D. Algorithms rely on probability theories

Answer: B

Explanation:
AAISM notes that deep learning systems lack transparency due to complex neural architectures, where internal representations are statistical, nonlinear, and not directly interpretable.
While probability (C) and data sourcing (D) contribute to opacity, the root cause is the intrinsic complexity and opacity of deep neural networks.
References: AAISM Study Guide - Explainability Challenges in Deep Learning.

NEW QUESTION # 347
After an organization deploys an AI-based system, a regulator warns of increased risk related to AI re-identification attacks on anonymized datasets. Which of the following should the information security manager do FIRST?

A. Assume anonymization is permanent and maintain business as usual.
B. Require staff to sign nondisclosure agreements (NDAs) and implement strong access controls for services using anonymized data.
C. Implement a continuous monitoring program for re-identification risk, including privacy audits and adversarial testing.
D. Immediately delete all anonymized datasets and suspend AI services.

Answer: C

Explanation:
Establishing continuous monitoring with privacy audits and adversarial testing directly addresses the evolving risk of re-identification by detecting vulnerabilities and measuring the effectiveness of anonymization over time. This proactive approach enables the organization to assess and mitigate the risk without unnecessarily disrupting operations.

NEW QUESTION # 348
......

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