Understanding How to Govern AI Deployment and Use

Governance responsibilities for selecting, assessing, deploying and operating AI systems responsibly, including vendor risk, monitoring, incident response and deactivation.

5 minutes 5 Questions

Understanding how to govern AI deployment and use is a critical competency for AI Governance Professionals, encompassing the frameworks, policies, and practices necessary to ensure AI systems are deployed responsibly, ethically, and in compliance with applicable regulations. AI governance in deplo…

Concepts covered
Risks and Opportunities for Proprietary AI Model DeploymentPerformance Requirements and Data Availability for DeploymentClassic vs. Generative AI Model SelectionSmall vs. Large AI ModelsCloud vs. On-Premise vs. Edge AI DeploymentAgentic Architectures for AI DeploymentEvaluating Vendor and Licensing Agreement Terms for AIObligations and Liability When Deploying Own vs. Third-Party AIApplying Policies and Ethical Considerations to AI DeploymentUser Training for Deployed AI SystemsPost-Deployment Maintenance, Updates and RetrainingReducing Downstream Harms of Deployed AIDeactivation and Localization of AI SystemsEvaluating AI Use Case Context and Business ObjectivesEthical Considerations in AI Deployment DecisionsWorkforce Readiness for Deployed AIProprietary vs. Open-Source AI ModelsLanguage vs. Multimodal AI CapabilitiesUsing AI As-Is vs. Fine-TuningRetrieval-Augmented Generation (RAG)Deployment Impact AssessmentKey Risks in AI Vendor ContractsData Governance in AI DeploymentRisk and Issue Management During AI DeploymentContinuous Monitoring Post-DeploymentDocumenting Incidents, Issues, Risks and Monitoring PlansAudits and Red Teaming of Deployed AIThreat Modeling and Security Testing of Deployed AIForecasting Secondary and Unintended Uses of AIExternal Communication Plans for AI
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AIGP - Understanding How to Govern AI Deployment and Use Example Questions

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Question 1

A retail company plans to deploy an AI recommendation system to its website. The system requires real-time user clickstream data to generate instant, personalized recommendations. However, the central data warehouse only synchronizes this clickstream data once every 24 hours. What is the most appropriate action the AI governance team should take regarding this deployment?

Correct Answer: Delay the deployment until the infrastructure team can upgrade the data pipeline to support real-time streaming.

The correct approach is to delay the deployment until the required infrastructure is in place because deploying a real-time system without real-time data support will fundamentally compromise its intended functionality and performance.

Deploying with daily batch updates ignores the system's explicit requirement for real-time personalization. Utilizing a secondary algorithm to estimate missing data introduces unnecessary complexity, risk, and potential inaccuracy. Requesting manual hourly uploads is an inefficient, unscalable workaround that is highly prone to human error.

The key concept tested is infrastructure readiness and data pipeline alignment for AI system deployment.

Question 2

What is the primary function of establishing a fallback strategy when deploying an artificial intelligence system into production?

Correct Answer: To transition operations to a reliable baseline system or human alternative when unexpected anomalies arise.

A fallback strategy serves as a critical safety net during deployment, ensuring operational continuity by allowing organizations to immediately revert to a proven baseline system or human-in-the-loop process whenever the artificial intelligence encounters unexpected edge cases, anomalies, or performance degradation.

Continuously retraining the model using live inputs describes online learning instead of a contingency plan. Automating the creation of regulatory documentation is a separate compliance workflow rather than a production safeguard. Systematically adjusting internal network parameters refers to model tuning or calibration rather than managing operational failures.

The key concept tested is artificial intelligence system resilience and operational risk management.

Question 3

Which of the following best describes the concept of data drift during the post-deployment phase of an artificial intelligence system?

Correct Answer: The statistical properties of the input data change compared to the original training baseline.

Data drift occurs when the statistical distribution of the data an AI system encounters in production diverges from the baseline data used during its initial training, potentially leading to degraded performance.
Autonomous parameter shifting describes continuous learning or model drift rather than data drift. Hardware latency relates to system infrastructure performance, not changes in data properties. Purging datasets describes data lifecycle management and privacy compliance, which is entirely unrelated to statistical shifts in live input data.
The key concept tested is post-deployment AI monitoring and the identification of data drift.

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