AAISM Multiple Select — 60 Questions

Question 1

Which THREE components are critical for an 'AI Incident Response' plan?

Accepted Answer

Communication channels with data scientists and legal teams.. Option A is correct because an AI incident response plan must define communication channels that bring together data scientists (who understand model behavior and can diagnose failures) and legal teams (who handle regulatory, liability, and disclosure obligations), ensuring coordinated and compliant escalation. Option C is correct because procedures for model rollback or disablement are essential to quickly contain harm by reverting to a prior known-good model version or taking the model offline when it misbehaves. Option D is correct because identifying model-specific failure indicators — such as data drift, adversarial inputs, bias spikes, or anomalous confidence scores — enables early detection and triage of AI incidents that differ from traditional IT failures. Option B does not belong because publishing all internal vulnerabilities publicly is not a standard incident response requirement and could increase risk and violate disclosure norms. Option E does not belong because automated deletion of all logs destroys the forensic evidence needed for incident investigation, root-cause analysis, and compliance reporting.

Answer

Requirements to publish all internal vulnerabilities publicly.

Answer

Automated deletion of all logs.

Question 2

Which TWO of the following are effective methods for monitoring and detecting AI security incidents in a cloud environment?

Accepted Answer

Ingesting model inference logs into a SIEM. Option C is correct because ingesting model inference logs into a SIEM centralizes AI telemetry, enabling correlation of prompts, responses, and metadata with other security events to detect prompt injection, data exfiltration, or model abuse in near real time. Option E is correct because configuring anomaly detection for API call patterns baselines normal invocation frequency, latency, token usage, and source IPs, then alerts on deviations that indicate credential theft, model extraction, or denial-of-wallet attacks against AI endpoints. Option A is wrong because disabling logging removes the audit trail needed for detection and forensics, and cost reduction is not a security monitoring method. Option B is wrong because standard database backup schedules address data durability and recovery, not real-time detection of AI security incidents. Option D is wrong because hardcoding API credentials in application code exposes secrets and increases compromise risk rather than helping monitor or detect incidents.

Answer

Disabling logging to reduce cloud spend

Answer

Using standard database backup schedules

Answer

Hardcoding API credentials in application code

Question 3

Which THREE activities help manage 'AI Lifecycle' risk?

Accepted Answer

Pre-deployment design and threat modeling.. Option A is correct because pre-deployment design and threat modeling identify AI-specific risks (e.g., data poisoning, model evasion, prompt injection, bias) before the system is fielded, allowing controls to be built in rather than bolted on. Option B is correct because formal decommissioning and sunsetting processes manage end-of-life risk by retiring models, revoking credentials, archiving or destroying training data, and preventing stale or unsupported AI components from remaining exploitable. Option E is correct because continuous operational monitoring detects model drift, data drift, performance degradation, and anomalous inference behavior during runtime, which is essential since AI risk evolves after deployment. Option C is incorrect because ignoring software updates leaves known vulnerabilities unpatched and increases lifecycle risk. Option D is incorrect because bypassing security reviews for speed skips necessary risk assessment and control validation, directly undermining AI lifecycle risk management.

Answer

Ignoring all software updates.

Answer

Bypassing security reviews for speed.

Question 4

Which THREE security controls are essential for protecting the integrity of the AI model training supply chain?

Accepted Answer

Data provenance and lineage tracking. Data provenance and lineage tracking (A) is essential because it records the origin, transformations, and custody of every training dataset, allowing integrity violations such as tampered or poisoned data to be detected and traced through the pipeline. SBOM analysis for dependencies (B) is required because AI training stacks pull in numerous third-party libraries and frameworks, and an SBOM lets you identify and remediate vulnerable or malicious components before they affect the model. Cryptographic signing of model artifacts (D) is essential because digital signatures (e.g., using asymmetric keys over hashes of weights and checkpoints) let consumers verify that a model was produced by a trusted source and has not been altered in transit or storage. Hardening the physical security of the server room (C) protects physical access to hardware but does not address the logical integrity of data, dependencies, or model artifacts across the supply chain, and increasing the number of neurons (E) is a model-capacity design choice that has no bearing on supply chain security.

Answer

Hardening the physical security of the server room

Answer

Increasing the number of neurons in the model

Question 5

Which THREE elements are essential for 'AI Governance'?

Accepted Answer

Defined AI ethics and safety policies.. AI governance requires a formal policy foundation, so option A (defined AI ethics and safety policies) is correct because written principles for fairness, transparency, accountability, and safety give the organization enforceable rules and decision criteria for AI use. Option C (a standard AI risk management framework) is correct because a recognized framework such as the NIST AI RMF or ISO/IEC 42001 provides a structured, repeatable process for identifying, assessing, treating, and monitoring AI risks across the lifecycle. Option E (a cross-functional oversight committee) is correct because governance needs an accountable body drawing on legal, compliance, security, data science, and business stakeholders to approve, review, and enforce AI decisions. Option B is not essential and would violate privacy and least-privilege principles, since governance never requires blanket access to employees' private emails. Option D is not essential because governance is about policy, risk, and accountability, not about mandating the most expensive GPU hardware, which is a cost and performance decision rather than a governance element.

Answer

Access to all employees' private emails.

Answer

A mandate to use the most expensive GPUs available.

Question 6

Which THREE of the following are potential indicators of a 'Model Poisoning' attack occurring during the training phase?

Accepted Answer

Inconsistent model convergence during training. Option B is correct because model poisoning corrupts the training data or update process, so the loss curve and parameter updates no longer converge consistently, producing erratic or stalled convergence during training. Option C is correct because poisoned training samples can cause the model to memorize manipulated patterns, yielding high training-set accuracy while validation accuracy degrades due to the corrupted decision boundary. Option E is correct because attackers often implant backdoors or trigger-based behaviors, so the model behaves normally on ordinary inputs but produces attacker-chosen outputs when a specific trigger pattern is present. Option A is not a training-phase indicator, since increased API request latency reflects inference or service performance rather than poisoned training data. Option D is also not indicative, because larger model-weight storage is a resource/cost artifact and does not by itself demonstrate poisoned training behavior.

Answer

Increased latency for API requests

Answer

Increase in storage costs for the model weights

Question 7

A security analyst is evaluating an AI-based intrusion detection system (IDS) that uses a machine learning model to classify network traffic. The analyst wants to assess the model's robustness against adversarial attacks. Which two techniques should be used to test the model's resilience? (Choose two.)

Accepted Answer

Projected Gradient Descent (PGD). FGSM and PGD are both adversarial attack techniques used to generate perturbed inputs that can fool a model. They are standard methods for testing a model's robustness, especially in security-sensitive applications like intrusion detection. By applying these attacks, analysts can identify vulnerabilities and improve defenses.

Answer

Membership inference

Answer

Model inversion

Answer

Data poisoning

Question 8

Which THREE documents are vital for an AI audit trail?

Accepted Answer

Model validation and testing reports.. Model validation and testing reports (B) are vital because they document how the AI model's performance, accuracy, bias, and robustness were assessed before and after deployment, providing auditable evidence of due diligence. Training data source documentation (C) is essential since it records the provenance, licensing, collection methods, and characteristics of the data used to train the model, which is necessary for tracing outputs and demonstrating compliance with data governance requirements. Model versioning history (D) is required to establish a traceable record of changes, retraining events, hyperparameters, and rollbacks, enabling auditors to reproduce or explain the behavior of any given model version at a point in time. The office lease agreement (A) and a copy of the employee handbook (E) are unrelated to the technical and data governance artifacts needed to reconstruct an AI system's development and decision-making, so they do not belong in an AI audit trail.

Answer

The office lease agreement.

Answer

A copy of the employee handbook.

Question 9

A hospital is preparing to fine-tune a clinical summarization model on de-identified patient notes. The AI security manager must select controls that reduce the risk of training-data memorization leaking back through model outputs. (Choose two.)

Accepted Answer

Apply differential privacy during fine-tuning with a formally tracked privacy budget.. Memorization leakage is mitigated by constraining how much any single record shapes the model and by verifying empirically that it did not. Differential privacy with a tracked budget provides the preventive, measurable constraint, while membership inference testing provides the detective evidence that the constraint held. Capacity scaling, data co-location, and log suppression either worsen the risk or address unrelated concerns.

Answer

Store the de-identified training corpus in the same object bucket used for model artifacts to simplify lineage tracking.

Answer

Increase the model's parameter count so that training examples are distributed across more weights.

Answer

Disable output logging on the summarization service so that generated text cannot be harvested.

Question 10

A multinational retailer is expanding its AI governance program to cover AI systems developed by its regional subsidiaries. The central AI governance office wants to ensure that subsidiary-developed AI systems are consistently registered, risk-assessed, and monitored. Which two governance mechanisms best achieve this consistency? (Choose two.)

Accepted Answer

A federated AI governance model in which each subsidiary appoints an accountable AI risk owner who applies centrally defined risk assessment criteria and reports into the central AI inventory.. Consistency across subsidiaries is best achieved by combining a federated governance model with accountable local AI risk owners and a shared governance platform that standardizes intake, risk assessment, and monitoring. The federated model supplies local accountability and context, while the platform enforces common data structures and gives the central office enterprise-wide visibility. Together they cover registration, assessment, and monitoring without centralizing every decision.

Answer

An annual attestation from each subsidiary's CIO that all AI systems comply with the central AI policy.

Answer

A central mandate requiring every subsidiary AI project to be approved by the central AI governance office before any development begins.

Answer

A requirement that all subsidiary AI systems use the same foundation model provider as the parent company.

Question 11

A global manufacturer is standing up an AI governance committee. The CISO wants to ensure the committee can make defensible decisions about high-risk AI deployments. Which TWO responsibilities are MOST essential for this committee to own directly? (Choose two.)

Accepted Answer

Resolving escalated AI risk issues and directing remediation when monitoring or audit identifies unacceptable risk.. A governance committee's essential authorities are approving high-risk use cases against defined criteria and resolving escalated risk issues with documented decisions. These responsibilities ensure accountability, consistency with risk appetite, and auditability. Implementation, daily monitoring, and contract negotiation are execution activities that belong to engineering, operations, and procurement, and assigning them to the committee would undermine its oversight independence.

Answer

Performing daily operational monitoring of model drift and data pipeline health for every deployed model.

Answer

Negotiating pricing and service-level agreements with third-party AI model providers.

Answer

Writing and maintaining the production code for all AI models used in manufacturing operations.

Question 12

A multinational corporation is establishing an AI governance program. The board has asked the AI security manager to identify the essential components that must be in place to ensure the program can enforce accountability across business units. (Choose two.)

Accepted Answer

A defined AI governance structure with clear roles and responsibilities, including an accountable executive.. Enforcing accountability requires both a clear governance structure with assigned responsibility and a centralized inventory of AI models with risk assessments. The governance structure provides authority and decision rights, while the inventory provides the visibility needed to track and oversee AI use. Communication tools, technology mandates, and surveys may support the program but do not establish the enforcement mechanisms essential for accountability.

Answer

A requirement that all AI projects use the same programming language and cloud provider.

Answer

An annual employee satisfaction survey that includes questions about AI tools.

Answer

A monthly newsletter highlighting AI success stories from each business unit.

Question 13

A global retailer is deploying a generative AI assistant that summarizes customer service transcripts for human agents. The legal team is concerned about privacy risk from the assistant retaining or regurgitating personally identifiable information (PII) from previous conversations. Which TWO controls BEST reduce this risk? (Choose two.)

Accepted Answer

Add a PII detection and redaction layer on both the input transcripts and the generated summaries.. The privacy risk stems from the assistant retaining or retrieving PII across sessions. Session isolation with no cross-session memory prevents one customer's data from appearing in another's summary, while input and output redaction catches residual identifiers. Together they address both the retrieval path and the generated output, which are the two points where leakage occurs.

Answer

Increase the model's temperature setting to reduce deterministic recall of training data.

Answer

Require human agents to sign an acceptable use policy prohibiting them from viewing PII.

Answer

Apply differential privacy during fine-tuning of the assistant on historical transcripts.

Question 14

A hospital is deploying an AI triage system that recommends patient priority levels. The risk committee wants to ensure ongoing risk visibility after go-live. Which TWO activities provide continuous risk visibility for this deployed system? (Choose two.)

Accepted Answer

Monitoring model input distribution drift against the training baseline.. Continuous risk visibility after deployment requires monitoring activities that generate ongoing signals. Input distribution drift monitoring detects when live data diverges from training conditions, and tracking clinician override rates captures real-world disagreement that may indicate model errors or edge cases. Static artifacts such as validation reports, one-time audits, and architecture documentation do not provide continuous visibility.

Answer

Documenting the model architecture and hyperparameters in the model registry.

Answer

Conducting a one-time fairness audit before the system goes live.

Answer

Reviewing the model's original validation accuracy report from the pre-deployment assessment.

Question 15

An AI security manager is assessing the risk of model inversion attacks on a machine learning model that predicts patient outcomes. The model is accessible via an API that returns confidence scores. Which TWO controls are MOST effective at reducing the risk of model inversion? (Choose two.)

Accepted Answer

Limit the API to return only the top-1 predicted class without confidence scores.. Model inversion attacks exploit the model's tendency to memorize training data, often using confidence scores to reconstruct sensitive information. Limiting API output to top-1 class removes the granular data attackers need, while differential privacy mathematically bounds the influence of any single training record, directly preventing memorization. These two controls together provide strong defense, whereas rate limiting, encryption, and smaller models do not adequately address the core risk.

Answer

Encrypt the model's parameters at rest and in transit.

Answer

Use a model architecture with fewer parameters to reduce its memorization capacity.

Answer

Implement rate limiting on the API to restrict the number of queries per user per hour.

Question 16

A hospital network is deploying a clinical decision support model that recommends triage priorities. The risk committee wants to establish human oversight that satisfies AI risk management expectations while preserving clinical workflow efficiency. Which TWO design choices BEST achieve meaningful human oversight? (Choose two.)

Accepted Answer

Require the clinician to record an override reason whenever the recommendation is rejected, and route that record into periodic model review.. Effective human oversight requires that the reviewer has enough information to evaluate the recommendation and that their disagreement produces a usable signal. Presenting rationale and uncertainty supports informed judgment, while capturing override reasons converts clinical disagreement into evidence that feeds model review. Together these choices create an oversight control that is both substantive at the point of care and useful for ongoing risk management, rather than a procedural formality.

Answer

Restrict clinician access to the model's inputs so that staff cannot second-guess the recommendation with incomplete information.

Answer

Configure the model to auto-populate the triage decision and require the clinician to click a single confirmation button to accept it.

Answer

Limit oversight to a monthly aggregate dashboard review by the quality committee after decisions have already been executed.

Question 17

A financial services firm is building an AI risk taxonomy for its enterprise risk management framework. The CISO asks which characteristics distinguish AI-specific risks from traditional IT risks so that appropriate controls can be designed. Which TWO characteristics should be included? (Choose two.)

Accepted Answer

AI risk exposure changes continuously as models learn from or are exposed to new data, requiring ongoing rather than point-in-time assessment.. Two characteristics genuinely distinguish AI risk from traditional IT risk: emergent behavior that defies component-level prediction, and continuous change that invalidates point-in-time assessments. Together they justify ongoing monitoring, staged deployment, red-teaming, and version governance. The other characteristics either describe similarities, restate legal misconceptions, or falsely claim determinism, none of which belong in a defensible AI risk taxonomy.

Answer

AI systems are exempt from data protection regulations because their outputs are statistical rather than factual.

Answer

AI models are subject to the same patch management and vulnerability disclosure cycles as conventional enterprise software.

Answer

AI models always produce deterministic outputs, allowing identical risk treatment to traditional deterministic business logic.

Question 18

Which TWO strategies are recommended for 'Human-in-the-Loop' governance?

Accepted Answer

Standardizing workflows for human intervention.. Option C is correct because standardizing workflows for human intervention ensures that Human-in-the-Loop governance is applied consistently, repeatably, and auditably across processes rather than relying on ad hoc judgment. Option E is correct because defining clear decision-making thresholds specifies exactly when a human must review, approve, or escalate an AI-generated decision, which is the core mechanism that keeps humans meaningfully in control. Together, these strategies establish both the process structure and the trigger criteria needed for effective human oversight. Option A is incorrect because using only AI for all HR decisions eliminates the human element entirely, which contradicts the Human-in-the-Loop principle. Option B is incorrect because allowing overrides without logging destroys the audit trail and accountability that governance requires. Option D is incorrect because removing humans to save money is the opposite of Human-in-the-Loop governance and removes necessary oversight.

Answer

Using only AI for all HR decisions.

Answer

Allowing humans to override without logging.

Answer

Removing humans to save money.

Question 19

A healthcare insurer is standing up an AI governance program and must decide which structural components to include in its governance framework to ensure AI systems are developed and operated consistently across business units. (Choose two.)

Accepted Answer

A defined governance body with assigned decision rights for AI approvals, exceptions, and oversight. A functioning AI governance framework rests on two structural pillars: a documented policy framework that sets principles, risk appetite, and mandatory controls, and a governance body with assigned decision rights for approvals, exceptions, and oversight. Together they ensure consistent treatment of AI across units, while controls and communications support execution.

Answer

A vendor contract template requiring AI suppliers to disclose training data sources

Answer

A monthly newsletter summarizing emerging AI regulations for interested employees

Answer

A data loss prevention tool configured to block outbound transfers of model weights

Question 20

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.