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100% Pass ISACA - AAISM The Best Actual Exam
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Free PDF 2026 Latest ISACA AAISM: ISACA Advanced in AI Security Management (AAISM) Exam Actual Exam
The ISACA Advanced in AI Security Management (AAISM) Exam (AAISM) certification is one of the hottest career advancement credentials in the modern ISACA world. The AAISM certification can help you to demonstrate your expertise and knowledge level. With only one badge of AAISM certification, successful candidates can advance their careers and increase their earning potential. The ISACA AAISM Certification Exam also enables you to stay updated and competitive in the market which will help you to gain more career opportunities.
ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q253-Q258):
NEW QUESTION # 253
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. Deploy a real-time logging and monitoring system
- B. Assess the business impact of known threats
- C. Restrict all user inputs containing special characters
- 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 # 254
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. Data poisoning
- B. Model inversion
- C. Availability attack
- D. Deepfake
Answer: C
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 # 255
Which of the following is the PRIMARY purpose of a dedicated AI system policy?
- A. Ensuring environmental impact is minimized
- B. Providing a framework to set AI objectives
- C. Optimizing AI accuracy
- D. Complying with external regulations
Answer: B
Explanation:
Per AAISM, an AI policy is a governance instrument that defines objectives, principles, roles, responsibilities, accountability, and control requirements for AI systems across their lifecycle. It establishes the framework within which performance, compliance, ethics, risk appetite, security, privacy, and sustainability objectives are set and operationalized. Environmental considerations (A), accuracy optimization (B), and regulatory compliance (D) are important outcomes addressed under the policy, but the primary purpose is to provide the overarching framework for objectives and controls.
References: AI Security Management (AAISM) Body of Knowledge - AI Governance Frameworks; Policies, Standards, and Procedures; Roles and Accountability in AI Programs.
NEW QUESTION # 256
An organization is deploying an automated AI cybersecurity system. Which strategy MOST effectively minimizes human error and improves security?
- A. Manual monitoring of alerts
- B. Using historical data to train detection software
- C. Conducting periodic penetration testing
- D. Utilizing machine learning algorithms to ensure responsible use
Answer: B
Explanation:
AAISM states that the effectiveness of automated AI cybersecurity systems depends heavily on well-trained detection models using high-quality historical attack data.
Historical data improves:
* detection accuracy
* reduction of false positives
* reduction of human misinterpretation
Manual monitoring (A) increases human error. ML "ensuring responsibility" (C) is not a defined control. Pen testing (D) does not reduce human mistakes.
References: AAISM Study Guide - AI in Cybersecurity; Model Training for Threat Detection.
NEW QUESTION # 257
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?
- A. Data poisoning
- B. Deepfake
- C. Model drift
- D. Hallucinations
Answer: B
Explanation:
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 # 258
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