Review questions about AI-enabled features, data handling, model providers, risk, evaluation, and human oversight.
About these answers. Most entries explain common industry practices, not Klistalabs-test controls. SIG entries marked “Company response” use approved Klistalabs-test wording supplied for this site.
AI use is scoped by feature and purpose, such as input analysis, content generation, or workflow assistance. AI use in one function does not imply use throughout the service.
An AI feature processes the inputs required for its stated purpose and produces corresponding outputs. The feature architecture and applicable terms define the specific data categories, processing boundaries, and retention period.
Use of customer content for model training is distinct from processing content to provide an AI-enabled feature. The service configuration and applicable terms define whether customer content is used for training, excluded from training, or used only with permission.
AI provider scope is defined by the implementation and includes the providers required for model development, hosting, inference, or related support. Provider identities and roles are specific to the service architecture.
AI risk assessment covers privacy, security, reliability, bias, misuse, and the potential impact of outputs. A material change to a feature, model, provider, data flow, or intended use calls for reassessment.
AI output can be inaccurate, incomplete, or unsuitable for a particular context and requires human assessment before use in consequential decisions. Review, correction, and escalation controls are defined at the feature level.
AI evaluation covers intended tasks, foreseeable failure and misuse scenarios, and quality, safety, privacy, and security. Post-release monitoring and reassessment address regressions and material changes in performance or risk.
User-facing notices and feature documentation identify where AI is involved, its intended purpose, and important limitations. Information for users reflects material changes to AI functionality or data handling.
AI misuse assessment covers foreseeable harmful prompts, attempts to bypass safeguards, and potential misuse of outputs. Safeguards, monitoring, and escalation processes are proportionate to feature risk and are reassessed when that risk changes.
A material change to a model, provider, or AI data flow is assessed for its impact on security, privacy, performance, and customer-facing behavior. Change governance addresses approval, communication, rollback planning, and change records according to the impact.