How an Enterprise AI Vendor Surfaced 1,000+ Data Exposures

Enterprise leaders lacked a reliable view of how employees were using AI across departments. Activity was scattered across approved tools, personal accounts, embedded AI features, and internal agents. They couldn’t clearly see which tasks were driving usage, where company data might be exposed, or which workflows warranted further investment. Our client, an enterprise AI company serving Fortune 100 organizations, needed the collection and reporting infrastructure to close that gap. The project focused on turning fragmented activity into consistent, useful information for business and security teams while limiting sensitive-content collection and supporting enterprise deployment requirements.

Genlift built the data collection, AI classification, and reporting infrastructure behind an enterprise AI platform. The system gathered usage signals from browser and endpoint activity across approved and unapproved AI tools, then organized them by platform, department, task type, and general theme. AI classification helped interpret what employees were using AI for and flag potentially sensitive activity. We standardized those signals so business and security teams could explore them through internal dashboards and reporting tools. Metadata-first collection, redaction, and access and retention controls helped limit exposure of sensitive content. It laid the foundation for owning their AI foundation, rollout, visibility, ownership, and cost control. We structured usage patterns from approved platforms to support evaluations, tests of custom AI models against the tasks employees actually perform, and the policies their company needs them to follow.

The client, an enterprise AI company serving Fortune 100 organizations, needed its platform to show how employees were really using AI: which tools, for which tasks, and where company data might be leaving. GenLift built collection from browser and endpoint activity across approved tools, personal accounts, embedded AI features and internal agents. The hard part was capturing enough context to make usage understandable without collecting sensitive content, and turning signals from very different tools into one structure that served both business reporting and security review. The team handled it with metadata-first collection, redaction, access and retention controls, and a shared data model. An AI classification step labels each interaction by task type and theme and flags potentially sensitive usage. Standardized records then feed internal dashboards for business and security teams. Usage patterns from approved platforms were also structured into a base for testing AI tools and custom models against the tasks employees actually perform and the policies they must follow. In hindsight the team would have fixed a short list of reporting questions and evaluation scenarios earlier, before widening coverage.

Organizations moving from scattered AI experimentation to coordinated adoption would benefit most, especially those with employees using multiple AI tools across departments, limited visibility into personal-account or unapproved usage, and sensitive business data to protect. It is particularly relevant to enterprise IT, security, AI platform, and transformation teams that need a shared view of usage and workflow demand to guide rollout, tool selection, and business-specific evaluations. AI software companies building these capabilities for enterprise customers could also replicate the approach. The key conditions are the ability to deploy approved browser or endpoint collection, clear privacy and data-retention policies, and business and security owners who can turn the findings into decisions.