AAISM Practice Test 9 — 10 Questions

Question 1

What is the primary role of 'Data Encryption at Rest' for AI datasets?

Accepted Answer

Protect data from unauthorized access on storage media. Data encryption at rest protects AI datasets by rendering stored data unreadable without the correct cryptographic keys, preventing unauthorized parties who gain access to storage media (stolen disks, misconfigured buckets, compromised backups) from reading the data. It is a confidentiality control, not a performance, detection, or patching mechanism.

Answer

Increase the training speed of the model

Answer

Detect potential poisoning attacks

Answer

Automatically patch model software

Question 2

A financial services firm uses a third-party large language model (LLM) API to summarize internal audit reports. The CISO is concerned that confidential report content could be retained by the provider and later resurface in responses to other customers. Which risk management action BEST addresses this specific concern?

Accepted Answer

Require the provider to contractually commit to zero data retention and verify this through their SOC 2 Type II report and data processing addendum.. The core risk is that a third-party LLM provider may retain confidential audit content and expose it to other tenants. A contractual zero-retention commitment, backed by independent audit evidence and a data processing addendum, creates enforceable, verifiable assurance. Technical controls like prompt filtering or DLP address different threat vectors and would not prevent provider-side retention or cross-tenant leakage.

Answer

Deploy a data loss prevention (DLP) solution that blocks any audit report content from leaving the corporate network.

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Fine-tune an open-source LLM on the audit reports and host it entirely on-premises.

Answer

Implement prompt injection filtering on all outbound requests to the LLM API.

Question 3

Which of these is a 'Proactive' risk management strategy?

Accepted Answer

Adversarial testing and threat modeling.. Adversarial testing and threat modeling are proactive strategies because they identify and mitigate potential risks before they materialize. They involve simulating attacks and analyzing system design to uncover vulnerabilities. This aligns with proactive risk management, which focuses on prevention rather than reaction.

Answer

Shutting down the system after an attack.

Answer

Incident response to a breach.

Answer

Adding more security after a leak is discovered.

Question 4

A global retailer is expanding its AI governance program to include third-party AI vendors. The Chief AI Officer wants to ensure that vendor-provided AI systems are subject to the same risk management rigor as internally developed models. Which TWO activities should be prioritized to integrate third-party AI into the governance framework? (Choose two.)

Accepted Answer

Conduct periodic security assessments of vendor AI systems, including penetration testing and review of their AI incident response plans.. Prioritizing vendor model documentation and periodic security assessments ensures transparency and ongoing risk management for third-party AI. Documentation reveals potential risks, while security assessments verify controls over time. Delegating responsibility, limiting usage, or mandating identical tools do not provide the necessary oversight and integration into the governance framework.

Answer

Require vendors to use the same AI development tools and platforms as the organization to ensure compatibility.

Answer

Delegate all AI risk management responsibilities to the vendor and rely on their internal governance processes.

Answer

Limit vendor AI usage to non-critical business functions to reduce potential impact.

Question 5

When evaluating AI security, 'Model Drift' refers to performance degradation over time. Which tool would a practitioner use to detect drift in a production environment?

Accepted Answer

Amazon SageMaker Model Monitor. Amazon SageMaker Model Monitor is purpose-built to detect data drift, model quality drift, bias drift, and feature attribution drift in production endpoints. It captures inference data, compares it against baselines, and emits CloudWatch alarms when drift exceeds thresholds. This directly addresses the need to detect performance degradation over time in a deployed model.

Answer

AWS Config

Answer

Amazon GuardDuty

Answer

AWS CloudTrail

Question 6

What is the 'Membership Inference' attack in machine learning?

Accepted Answer

Determining if a specific record was in the training set. Membership inference determines whether a specific record was part of the model's training set by observing the model's outputs (e.g., confidence scores) for that record. It is a privacy attack because it can reveal sensitive information — for example, that a particular patient's data was used to train a medical model, which itself leaks health status. The attack exploits the tendency of models to be more confident on training data than on unseen data.

Answer

Brute forcing the user password

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Adding noise to the output

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Stealing the model's entire weights file

Question 7

When designing a secure AI development lifecycle (SDLC), which of the following is an essential governance activity during the 'Model Deployment' phase?

Accepted Answer

Finalizing the Model Card to document intended use, limitations, and security parameters.. The Model Card is the governance artifact that documents a model's intended use, limitations, performance characteristics, and security parameters, and finalizing it at deployment ensures downstream consumers and reviewers understand the model's boundaries and risks. It is an essential governance activity because it creates the accountability record required before a model goes live. Deployment gates typically require a completed model card as evidence of responsible release.

Answer

Running a brute-force attack on the model API.

Answer

Updating the GPU drivers on the inference servers.

Answer

Resetting the encryption keys for the production database.

Answer

Removing all training data from the production environment.

Question 8

Which THREE of the following are examples of AI system 'Access Controls'?

Accepted Answer

API Key authentication. API Key authentication (B) is an access control because it requires a caller to present a valid key before the AI service or endpoint will accept the request, authenticating and gating access. Role-Based Access Control (C) is a classic access control mechanism that grants permissions to AI resources and operations based on a user's assigned role, enforcing least privilege. Network Security Groups (D) act as access controls by allowing or denying inbound and outbound traffic to AI workloads based on rules, thereby restricting which sources can reach the system. Disabling logging services (A) is not an access control but rather the removal of an auditing/monitoring capability, and increasing model training epochs (E) is a model-training hyperparameter change that has no bearing on who or what may access the AI system.

Answer

Disabling logging services

Answer

Increasing model training epochs

Question 9

A healthcare organization is deploying a generative AI chatbot that provides medical information to patients. The AI security manager is concerned about the risk of the chatbot generating harmful or misleading medical advice. Which control is MOST appropriate to mitigate this risk before deployment?

Accepted Answer

Fine-tune the generative model on a curated dataset of clinically validated medical information and implement a guardrail that restricts responses to that knowledge base.. In healthcare, generative AI must be grounded in validated knowledge to prevent harmful hallucinations. Fine-tuning on curated clinical data combined with a retrieval guardrail that limits responses to that data ensures the chatbot provides accurate, safe information. Content filtering, disclaimers, or full human review are either ineffective, insufficient, or impractical for this use case.

Answer

Require all chatbot responses to be reviewed and approved by a licensed physician before being sent to the patient.

Answer

Implement a content filter that blocks any response containing medical terminology not approved by the organization's clinical team.

Answer

Deploy the chatbot in a limited pilot with a disclaimer that the information is not medical advice and monitor user feedback for harmful outputs.

Question 10

Which phase of the AI Lifecycle is most critical for 'Bias Detection'?

Accepted Answer

Model testing and evaluation.. Model testing and evaluation is the phase where bias metrics (e.g., disparate impact, equal opportunity difference) are computed against validation and test datasets across protected groups. This is where the model's actual behavior is measured and compared to fairness thresholds before deployment. Bias detection requires empirical evaluation of outputs, making testing/evaluation the critical phase.

Answer

Contract negotiation with vendors.

Answer

Model decommissioning.

Answer

Data center power-up.