AAISM Exam & AAISM Dumps Deutsch

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ISACA AAISM Prüfungsplan:

Thema	Einzelheiten
Thema 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.
Thema 2	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.
Thema 3	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.

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AAISM Dumps Deutsch & AAISM Deutsche

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ISACA Advanced in AI Security Management (AAISM) Exam AAISM Prüfungsfragen mit Lösungen (Q189-Q194):

189. Frage
An organization is designing an AI-based credit risk assessment system that will integrate with sensitive financial datasets. Which of the following would BEST support the implementation of security-by-design principles in the AI system's architecture?

A. Applying threat modeling specific to AI components before deployment
B. Restricting access to AI models using IP allow lists to reduce public exposure
C. Segmenting AI services across containers to manage resource constraints
D. Integrating differential privacy mechanisms into model training to limit data leakage

Antwort: A

Begründung:
Security by design in AI requires establishing risk-informed requirements at the earliest stages of the lifecycle and systematically translating them into architectural controls. Conducting AI-specific threat modeling before deployment is the highest-leverage action because it identifies assets (data, models, pipelines), trust boundaries (feature stores, training/inference services), threat events (poisoning, evasion, model extraction), and attack paths unique to ML systems. The outputs (abuse/misuse cases, control objectives, verification plans) then drive selection and prioritization of controls such as privacy-enhancing techniques, access controls, isolation, monitoring, and assurance testing.

190. Frage
A SaaS-based LLM system has risks including prompt injection, data poisoning, and model exfiltration. What is the BEST way to ensure consistent risk treatment?

A. Rely on vendor audit reports and SLAs
B. Implement an AI threat control matrix mapping threats to controls and assurance
C. Focus on post-deployment red teaming
D. Apply control baselines from a recognized industry standard

Antwort: B

Begründung:
AAISM highlights the importance of an AI threat-control matrix that:
- maps threats (e.g., poisoning, exfiltration, injection)
- identifies required controls
- defines assurance/testing activities
- ensures consistent coverage across all AI components

191. Frage
An organization plans to use AI to analyze the shopping patterns of its customers to predict interests and send targeted, customized marketing emails. Which of the following should be done FIRST?

A. Verify customer email addresses
B. Train the marketing department
C. Update the terms of service
D. Obtain customer consent

Antwort: D

Begründung:
The first action, before any processing of personal data for AI-driven profiling and targeted communications, is to establish a lawful basis for processing. Under AAISM-aligned privacy governance, explicit and informed consent is prioritized for new or sensitive uses such as interest profiling and targeted marketing.
Consent ensures purpose limitation, transparency, and user control prior to model ingestion and campaign activation. Training teams, updating terms of service, or verifying contact details are important, but they do not provide legal authority to process data; therefore, they follow after consent is obtained.
References: AI Security Management (AAISM) Body of Knowledge - Privacy Governance and Lawful Basis; Purpose Limitation and Transparency; Consent Management in AI-enabled Marketing. AAISM Study Guide - Data Protection Controls for AI Profiling; Consent Capture and Record-Keeping.

192. Frage
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?

A. Jailbreaking
B. Prompt injection
C. Remote code execution
D. Evasion

Antwort: B

Begründung:
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.

193. Frage
Which of the following is the BEST way to ensure an organization remains compliant with industry regulations when decommissioning an AI system used to record patient data?

A. Perform a post-destruction risk assessment to verify that there is no residual exposure of data
B. Ensure the certificate of destruction is received and archived in line with data retention policies
C. Update governance policies based on lessons learned and ensure a feedback loop exists
D. Ensure backups are tested and access controls are recorded and audited to ensure compliance

Antwort: B

Begründung:
For regulated data such as patient information, AAISM requires provable data lifecycle closure at decommissioning. The authoritative evidence is a certificate of destruction (covering primary, replicas, backups, and caches) retained per the organization's records retention policy. While testing backups and auditing access (A), updating policies (B), and doing post-destruction risk assessment (C) are valuable practices, documented destruction attestation is the primary compliance proof point that the data was disposed of in accordance with regulatory and contractual obligations.
References: AI Security Management™ (AAISM) Body of Knowledge - Data Lifecycle Governance; Decommissioning & Secure Disposal; Records Retention and Evidence of Destruction.

194. Frage
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