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ISACA AAISM Exam Syllabus Topics:

Section	Weight	Objectives
AI Governance and Program Management	31%	- AI asset and data lifecycle management - AI governance frameworks and alignment with business objectives - Business continuity and incident response for AI systems - AI security policies, standards, and procedures - AI security program development and management
AI Technologies and Controls	38%	- Data management and protection controls for AI - Security monitoring, testing, and continuous assurance for AI systems - Privacy, ethics, trust, and safety controls - AI lifecycle security: model selection, training, validation, and deployment - AI security architecture and secure design principles
AI Risk Management	31%	- Regulatory compliance and ethical considerations in AI - AI threat and vulnerability management - AI vendor and supply chain risk management - AI risk assessment, thresholds, and treatment strategies

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ISACA Advanced in AI Security Management (AAISM) Exam AAISM Prüfungsfragen mit Lösungen (Q84-Q89):

84. Frage
Which of the following strategies is the MOST effective way to protect against AI data poisoning?

A. Increasing model complexity to better handle data variations
B. Using robust data validation techniques and anomaly detection
C. Incorporating more features and data into model training
D. Ensuring the model is trained on diverse data sources

Antwort: B

Begründung:
AAISM directs organizations to prevent training-time attacks by hard-gating data ingestion with provenance checks, schema and label validation, sanitization, and anomaly/outlier detection prior to model training. These controls most directly block poisoned records from entering the pipeline and are prioritized over architectural complexity or sheer data volume. Diversity of sources can improve representativeness but does not reliably stop adversarial contamination.
References: AI Security Management (AAISM) Body of Knowledge - Adversarial ML: Training-Time Threats; Secure Data Ingestion & Validation Controls; AI Risk Treatment and Assurance. AAISM Study Guide - Poisoning Prevention Gates; Provenance, Quality, and Anomaly Screening in ML Pipelines.

85. Frage
An organization is updating its vendor arrangements to facilitate the safe adoption of AI technologies. Which of the following would be the PRIMARY challenge in delivering this initiative?

A. Unwillingness of large AI companies to accept updated terms
B. Insufficient legal team experience with AI
C. Inability to sufficiently identify shadow AI within the organization
D. Failure to adequately assess AI risk

Antwort: A

Begründung:
In the AAISM™ guidance, vendor management for AI adoption highlights that large AI providers often resist contractual changes, particularly when customers seek to impose stricter security, transparency, or ethical obligations. The official study materials emphasize that while organizations must evaluate AI risk and build internal expertise, the primary challenge lies in negotiating acceptable contractual terms with dominant AI vendors who may not be willing to adjust their standardized agreements. This resistance limits the ability of organizations to enforce oversight, bias controls, and compliance requirements contractually.
References:
AAISM Exam Content Outline - AI Risk Management
AI Security Management Study Guide - Third-Party and Vendor Risk

86. Frage
An organization is conducting an AI risk assessment and wants to establish risk thresholds for adversarial threats such as evasion attacks and data poisoning. Which of the following is MOST important to be approved by senior management?

A. The extent of human-in-the-loop oversight
B. The backup policy to prevent loss of data
C. The acceptable level of model performance degradation
D. The AI training plan focusing on human safety

Antwort: C

Begründung:
Senior management should approve the acceptable level of model performance degradation because it defines the organization's risk tolerance for adversarial threats and operational impact.
Establishing these thresholds is a governance decision that directly affects business risk acceptance and security posture.

87. Frage
A hospital is conducting an AI impact assessment for a proposed diagnostic imaging tool. To ensure conformity with regulatory and ethical standards, which risk requires the MOST domain- specific assessment beyond what is typically found in a standard IT risk assessment?

A. The model may produce systematically biased diagnoses for specific demographics, potentially leading to widespread clinical harm.
B. System downtime may increase due to hardware failure in the hosting environment, preventing medical professionals from accessing the tool.
C. Software vulnerabilities in the application's web server may allow an attacker to gain unauthorized access to the system.
D. Noncompliance with data residency laws may occur if patient data is processed in a cloud region outside the required jurisdiction.

Antwort: A

Begründung:
Bias in diagnostic outputs directly affects clinical decision-making and patient outcomes, requiring specialized medical, ethical, and regulatory expertise beyond standard IT risk assessments. This type of risk involves domain-specific evaluation of fairness, safety, and potential harm to patients.

88. Frage
A financial organization is concerned about the risk of prompt injection attacks on its customer service chatbot. Which of the following controls BEST addresses this concern?

A. Continuous monitoring
B. Human-in-the-loop
C. Input validation
D. Increasing model parameters

Antwort: C

Begründung:
AAISM emphasizes preventive technical controls for LLM threats such as prompt injection, including input validation/sanitization, instruction isolation, allow/deny lists, context segmentation, and output filtering.
These reduce the model's exposure to adversarial instructions embedded in user prompts or retrieved context.
Monitoring (A) is detective, not preventive; increasing parameters (B) does not inherently improve security against injection; human-in-the-loop (D) is valuable for high-risk decisions but does not directly neutralize injection vectors at the control boundary the way input validation and content filtering do.
References: AI Security Management (AAISM) Body of Knowledge - Technical Controls for LLM Security; Input/Output Filtering and Context Isolation; Secure Inference and Prompt Injection Mitigations.

89. Frage
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