> For the complete documentation index, see [llms.txt](https://propscreen.gitbook.io/propscreen/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://propscreen.gitbook.io/propscreen/introduction/the-problem.md).

# The Problem

## Overview of the Problem

The adoption of customized-LLMs trained on or leveraging proprietary enterprise information in corporate settings has led to a double-edged sword: increased efficiency but with a heightened risk of sensitive information disclosure. As employees interact with these powerful tools, they can inadvertently leak confidential and personal data that creates significant security vulnerabilities, legal exposures, and potential loss of business revenue from IP theft. This problem is exacerbated by the rapid expansion of workforces and the pressing demand for AI-driven productivity solutions. This challenge is particularly acute for small and medium-sized businesses (SMBs), which can't afford to rely solely on employee trust to protect critical information or expend significant resources towards re-training or re-wiring customized LLMs.

## Who should care? (Who are we solving this for?)

* Business Leaders: Concerned with protecting intellectual property and maintaining competitive advantage.
* Security Professionals: Focused on mitigating risks associated with unauthorized data disclosures.
* Developers and Engineers: Interested in integrating robust security measures into AI solutions.
* Venture Capitalists: Looking to invest in scalable, secure AI technologies that address critical market needs.


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