The correct answer is C because understanding algorithmic methodology is primarily a design, development, and model evaluation activity rather than part of a deactivation or sunset risk strategy. A deactivating risk strategy focuses on safely retiring or discontinuing an AI system, ensuring that operational, legal, and business risks are properly managed. This includes evaluating regulatory requirements such as data retention and deletion obligations, assessing business continuity and financial implications, and understanding dependencies with upstream and downstream systems to avoid disruption. These steps ensure a controlled and compliant system shutdown. In contrast, analyzing algorithmic methodology is relevant during system development, validation, and explainability efforts, not during the decommissioning phase of the AI lifecycle.

An AI system’sfunction,industry, anddeployment locationdefine itsrisk profile, which directly influences theinternal governance structuresan organization must put in place.

From theAI Governance in Practice Report 2025:

“There are many challenges and potential solutions for AI governance, each with unique proximityand significance based on an organization’s role, footprint, broader risk-governance profile and maturity.” (p. 4)

“AI governance starts with defining the corporate strategy for AI… and formulating policy standards and operational procedures to reflect industry, use case, and location.” (p. 11)

A– Organizational accountability is broader and not directly scoped by industry or function.

C– Diversity of data sources is tied to data strategy.

D– Explainability is more influenced by model type, not use context.

The correct answer isB. AllowingPIIto be freely entered into prompts without safeguards is considered amajor privacy and security riskand is not a responsible governance practice.

From the AIGP ILT Guide – Generative AI & Third-Party Risk Management:

“Use of personal or sensitive information in AI prompts can result in unintended exposure, regulatory breaches, and downstream liability.”

The AI Governance in Practice Report 2025 highlights:

“PII should be minimized or protected by design. Prompt engineering should prevent entry of personally identifiable data unless legally and technically safeguarded.”

A, C, and D are established best practices under responsible AI procurement and use.

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The correct answer is D because one of the most significant risks associated with generative AI is the potential leakage of sensitive, confidential, or proprietary information. AI governance frameworks emphasize data protection, confidentiality, and secure data handling as critical organizational responsibilities. When employees input sensitive data into generative AI tools, especially external or third-party systems, that information may be stored, reused, or exposed unintentionally. This creates legal, regulatory, and reputational risks. As a result, organizations may restrict or prohibit generative AI use to prevent unauthorized data disclosure. The other options relate to operational or business considerations, which are not primary governance drivers. Protecting sensitive information aligns with core AI governance principles such as privacy, security, and risk mitigation, making it the strongest justification for such a policy.

Business A is integrating a generative AI model licensed from a third party (Business B) and is primarily concerned with the risk of toxic or obscene outputs being delivered to users. In this scenario,testing and validationof the AI model for such content risks is the most direct and effective governance strategy.

According to theAI Governance in Practice Report 2025, organizations thatdeployAI must engage inperformance monitoring protocolsand ensure systems perform adequately for theirintended purposes, including filtering harmful content:

“Operational governance… development of: →Performance monitoring protocols to ensure systems perform adequately for their intended purposes.” (p. 12)

“Product governance... includes: →System impact assessments to identify and address risk prior to product development or deployment.” (p. 11)

Furthermore, under theEU AI Act, which sets the global standard many organizations aim to align with, there is a clear obligation to test and monitor systems for potential harmful behavior:

“The act imposes regulatory obligations… such as establishing appropriate accountability structures,assessing system impact, providing technical documentation,establishing risk management protocols and monitoring performance...” (p. 7)

Option B directly reflects this best practice ofpre-deployment testing and validationto ensure that the model aligns with Business A’s minimum content safety requirements.

Let’s now evaluate the incorrect options:

A. Fine-tuning on verified user-generated textmay improve model alignment but does not guarantee that the model will generalize correctly, especially if Business A lacks access to model internals (common in third-party licensing scenarios). Fine-tuning also introduces its own risks and may be contractually restricted.

C. A user reporting featureisreactive, not preventive. While helpful for long-term monitoring and mitigation, it does not prevent the initial harm of toxic outputs, which isBusiness A ' s primary concern.

D. Requesting documentation from Business Bis useful for transparency and risk management, but it does not replaceindependent verificationthat the model meets Business A’s content safety standards.

Thus,testing the model ' s behavior for unacceptable outputs before deploymentis the most aligned approach with AI governance best practices and obligations.

Respecting intellectual property rights means adhering to licensing terms and ensuring that generated content complies with these terms. A system that categorizes and applies filters based on licensing terms ensures that content is used legally and ethically, respecting the rights of content creators. While providing attribution is important, categorization and application of filters based on licensing terms are more directly tied to compliance with intellectual property laws. This principle is elaborated in the IAPP AIGP Body of Knowledge sections on intellectual property and compliance.

The correct answer is C because it follows a structured, risk-based AI governance approach aligned with best practices and regulatory expectations. AI governance frameworks emphasize identifying and assessing risks through probability and severity analysis before deployment. This allows organizations to prioritize the most critical risks, such as bias and discrimination in loan approvals. Applying a risk mitigation hierarchy ensures that controls are implemented proactively rather than reactively. Only after risks are assessed and mitigated should pilot testing occur to validate system performance in a controlled environment. Other options either delay risk assessment until after deployment or focus on limited aspects like benchmarking or explainability without addressing core risk management principles. A proactive, risk-first approach is essential for compliance, fairness, and responsible AI deployment.

Data minimization requires organizations to collect and process only personal information that is adequate, relevant, and necessary for the specified purpose. A basic to-do application may reasonably require limited account or contact information, but political opinions and trade-union affiliations have no apparent necessity for compiling, researching, or prioritizing tasks. Those attributes are also treated as particularly sensitive under European data-protection law. Privacy by design requires privacy safeguards and data-protection principles to be incorporated into the product from the design stage and reflected in default settings. Therefore, unnecessary collection at installation directly implicates both minimization and privacy by design. Purpose limitation may also become relevant, but explicit consent does not by itself cure unjustified overcollection. Transparency, portability, and access rights address different privacy obligations and do not best characterize the defect described here.

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Post-deployment AI maintenance typically includes ensuring that all model components are subject to a control framework, dedicating experts to continually monitor the model output, and evaluating the need for audits under certain standards. However, defining thresholds to conduct new impact assessments is usually part of the initial deployment and ongoing governance processes rather than a maintenance activity. Maintenance focuses more on the operational aspects of the AI system rather than setting new thresholds for impact assessments.

When an AI system causessignificant false positives, especially in sensitive contexts likefraud detection, the priority is tohalt harmful activityand perform a full assessment. Continued use without understanding the fault may cause furthercustomer harmand legal exposure.

From theAI Governance in Practice Report 2025:

“Incident management plans should enable identification, escalation, and system rollback to prevent continued harm from malfunctioning AI systems.” (p. 12, 35)