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Trustworthy AAISM Exam Torrent - AAISM Dumps Questions

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q158-Q163):

NEW QUESTION # 158
Which of the following BEST addresses risk associated with hallucinations in AI systems?

A. Recursive chunking
B. Human oversight
C. Content enrichment
D. Automated output validation

Answer: B

Explanation:
AAISM prescribes human-in-the-loop (HITL) controls as the primary safeguard for high-impact generative AI use cases to mitigate hallucination risk. Human oversight ensures critical outputs are reviewed, corrected, and approved before use, with accountability, escalation, and documented decision trails. Automated validators and enrichment help reduce errors but are secondary; recursive chunking is a prompting tactic, not a governance control.
References: AI Security Management™ (AAISM) Body of Knowledge: Responsible AI & Human Oversight; Generative AI Risk Controls-Approval Workflows and Human Review; AAISM Study Guide: Hallucination Risk Treatment with HITL and Approval Gates.

NEW QUESTION # 159
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. Evasion
B. Remote code execution
C. Prompt injection
D. Jailbreaking

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 # 160
Which of the following would BEST ensure a proper business continuity plan (BCP) is in place for an AI solution?

A. Increasing the detail of AI solution backup and restoration processes
B. Enhancing monitoring and detection of model failures and anomalies
C. Implementing access controls to protect the AI system from unauthorized use
D. Testing the AI infrastructure failover mechanisms

Answer: D

Explanation:
Effective AI BCP requires validation through exercises and controlled failover tests to prove recovery objectives can be met in practice. Merely documenting backups (Option D), hardening access (Option B), or improving monitoring (Option A) does not confirm that the AI stack-data pipelines, feature stores, model registries, inference services, and dependent infrastructure-can actually fail over and recover within RTO
/RPO. AAISM prescribes periodic BCP/DR testing (including model artifact restoration, configuration reconstitution, dependency failover, and data pipeline continuity) to verify readiness and identify gaps before real incidents.
References:AI Security Management™ (AAISM) Body of Knowledge: Business Continuity & Disaster Recovery for AI; Validation and Exercising of Continuity Plans; RTO/RPO for Models, Data, and Pipelines.
AAISM Study Guide: Operational Resilience for AI Systems; BCP/DR Test Scenarios (model registry, feature store, pipeline recovery); Continuity Metrics and Evidence of Readiness.

NEW QUESTION # 161
Which of the following BEST enables an organization to strengthen information security controls around the use of generative AI applications?

A. Validating AI model training data
B. Implementing a kill switch
C. Monitoring AI outputs against policy
D. Ensuring controls exceed industry benchmarks

Answer: C

Explanation:
For generative AI, the primary enterprise security exposure is data and content exfiltration or policy violations at output, including leakage of sensitive data, toxic content, or regulatory non-compliance.
AAISM prescribes policy-aligned output monitoring (e.g., DLP checks, PII/PHI detection, toxicity/safety filters, watermark/attribution checks) integrated into inference gateways to enforce organizational policies and evidence compliance. Exceeding benchmarks (A) is not a control; training-data validation (C) may be infeasible with third-party LLMs; and kill switches (D) are essential contingency controls but do not continuously strengthen everyday security posture.
References: AI Security Management (AAISM) Body of Knowledge - GenAI Governance and Guardrails; Output Filtering and DLP Controls; Policy Enforcement at Inference. AAISM Study Guide - Monitoring & Auditing of GenAI; Gateway Patterns for Safe Use; Control Effectiveness Measures.

NEW QUESTION # 162
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 # 163
......

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Chris Lane Chris Lane

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