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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Risk Management | 31% | - AI risk assessment, thresholds, and treatment strategies - AI threat and vulnerability management - Regulatory compliance and ethical considerations in AI - AI vendor and supply chain risk management |
| Topic 2: AI Governance and Program Management | 31% | - AI security program development and management - AI asset and data lifecycle management - Business continuity and incident response for AI systems - AI security policies, standards, and procedures - AI governance frameworks and alignment with business objectives |
| Topic 3: AI Technologies and Controls | 38% | - Security monitoring, testing, and continuous assurance for AI systems - AI security architecture and secure design principles - AI lifecycle security: model selection, training, validation, and deployment - Data management and protection controls for AI - Privacy, ethics, trust, and safety controls |
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NEW QUESTION # 234
An attacker crafts inputs to a large language model (LLM) to exploit output integrity controls. Which of the following types of attacks is this an example of?
Answer: C
Explanation:
According to the AAISM framework, prompt injection is the act of deliberately crafting malicious or manipulative inputs to override, bypass, or exploit the model's intended controls. In this case, the attacker is targeting the integrity of the model's outputs by exploiting weaknesses in how it interprets and processes prompts. Jailbreaking is a subtype of prompt injection specifically designed to override safety restrictions, while evasion attacks target classification boundaries in other ML contexts, and remote code execution refers to system-level exploitation outside of the AI inference context. The most accurate classification of this attack is prompt injection.
References:
AAISM Exam Content Outline - AI Technologies and Controls (Prompt Security and Input Manipulation) AI Security Management Study Guide - Threats to Output Integrity
NEW QUESTION # 235
An organization has implemented a natural language processing model to respond to customer questions when personnel are not available. A pre-implementation security assessment revealed attackers could access sensitive company data through a chat interface injection attack. Which of the following is the BEST way to prevent this attack?
Answer: A
Explanation:
To prevent prompt/interface injection, AAISM prioritizes preventive technical controls at the boundary: input validation/sanitization, structured templates/system prompts, allow/deny lists, and context isolation. These measures constrain user-supplied content and block adversarial instructions from being interpreted as system directives. Monitoring (A) and audits (D) are detective/assurance activities; manual output review (B) is compensating but less scalable and does not prevent injection.
References: AI Security Management™ (AAISM) Body of Knowledge - Secure Prompting & Input Controls; Interface Injection Mitigations; Context and Instruction Isolation Patterns.
NEW QUESTION # 236
Which of the following is the BEST way to ensure role clarity and staff effectiveness when implementing AI-assisted security monitoring tools?
Answer: C
Explanation:
AAISM directs that AI adoption in security operations be governed through explicit operating models and RACI-mapped responsibilities spanning security operations, data science/ML, platform engineering, privacy, and compliance. Role clarity comes from updating the security program to codify AI-specific responsibilities (model monitoring, incident handling for AI failures, data governance, change control for models, bias/fairness reviews, supplier assurance) rather than deferring implementation, outsourcing core accountability, or relying on generic certifications. This ensures measurable accountability, reduces hand-off risk, and aligns day-to- day SOC practices with AI control objectives.
NEW QUESTION # 237
When implementing a generative AI system, which of the following approaches will BEST prevent misalignment between the corporate risk appetite and tolerance?
Answer: B
Explanation:
AAISM governance guidance specifies that alignment between AI system adoption and organizational risk appetite is achieved by defining and monitoring acceptable levels of system risk. This ensures that generative AI operations remain within boundaries approved by leadership and compliance frameworks. While KPIs track performance, they do not ensure alignment with risk tolerance. AI impact assessments help identify risks but do not maintain continuous oversight. A risk register records risks but does not dynamically enforce acceptable thresholds. The most effective governance approach is to establish and monitor acceptable AI system risk levels.
References:
AAISM Study Guide - AI Governance and Program Management (Risk Appetite and Tolerance Alignment) ISACA AI Security Management - Generative AI Risk Governance
NEW QUESTION # 238
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?
Answer: C
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.
NEW QUESTION # 239
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