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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Risk Management | 31% | - AI risk assessment, thresholds, and treatment strategies - AI vendor and supply chain risk management - AI threat and vulnerability management - Regulatory compliance and ethical considerations in AI |
| Topic 2: AI Governance and Program Management | 31% | - AI asset and data lifecycle management - Business continuity and incident response for AI systems - AI security program development and management - AI governance frameworks and alignment with business objectives - AI security policies, standards, and procedures |
| Topic 3: AI Technologies and Controls | 38% | - AI lifecycle security: model selection, training, validation, and deployment - AI security architecture and secure design principles - Privacy, ethics, trust, and safety controls - Data management and protection controls for AI - Security monitoring, testing, and continuous assurance for AI systems |
Tech firms award high-paying job contracts to ISACA Advanced in AI Security Management (AAISM) Exam (AAISM) certification holders. Every year many aspirants appear in the AAISM test of the certification, but few of them cannot crack it because of not finding reliable ISACA Advanced in AI Security Management (AAISM) Exam prep materials. So, you must prepare with real exam questions to pass the certification exam. If you don't rely on actual exam questions, you will fail and loss time and money.
NEW QUESTION # 60
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?
Answer: D
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 # 61
Which of the following BEST reduces the risk of exposing sensitive data through the output of large language models (LLMs) in applications?
Answer: A
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.
NEW QUESTION # 62
An organization implementing a large language model (LLM) application notices significant and unexpected cost increases due to excessive computational resource usage. Which vulnerability is MOST likely in need of mitigation?
Answer: B
Explanation:
AAISM highlights unbounded consumption (token/payment exhaustion, unmetered tool calls, prompt bombs) as a key LLM risk affecting cost and availability. Controls include request quotas, max tokens, rate-limits, budget guards, circuit breakers, and cost-aware routing.
NEW QUESTION # 63
Which of the following is the MOST likely cause of model drift?
Answer: C
Explanation:
Model drift occurs when the statistical properties of input data and/or the relationship between features and outcomes change over time, causing degraded model performance. The AAISM guidance classifies data- centric causes (distribution shift, concept drift, and contamination) as the primary drivers and highlights that malicious contamination of training or incremental learning data (data poisoning) is a direct, high- likelihood driver of observable drift in production because it changes the effective data-generating process the model learns from. In contrast:
* Perfect knowledge is an attacker capability descriptor, not a drift cause.
* Membership inference targets privacy of the training set and does not inherently shift data distributions.
* Model stealing targets IP/confidentiality; it does not change the victim model's data distribution or decision boundary in situ.
References:* AI Security Management (AAISM) Body of Knowledge: Model Risk & Drift; Data Integrity Risks; Adversarial ML-Poisoning vs. Evasion* AAISM Study Guide: Production Monitoring & Drift Management; Risk Scenarios-Data Poisoning Impacts and Controls* AAISM Mapping to Standards:
Lifecycle Risk Treatment-Robustness to Data Contamination; Continuous Monitoring and Feedback
NEW QUESTION # 64
What is the role of an AI governance committee within an organization?
Answer: D
NEW QUESTION # 65
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