The information employees share with artificial intelligence tools, the data these systems access, and the reliability of the content they produce are creating new security challenges in the finance, healthcare, public-sector, and defense industries. Polygraf AI, founded in the US in 2021 by Azerbaijani-born entrepreneur Yagub Rahimov, is developing a security layer between organizations and artificial intelligence tools. The AI Behavioral Control Plane product aims to identify sensitive data, enforce security policies, and detect the sharing of confidential information, artificial intelligence-driven fraud, and generated content. The company’s small language models can operate in private data centers or systems isolated from the internet. Its Content Source Detection technology focuses on distinguishing between human-produced, artificial intelligence-generated, and subsequently modified documents; its patent was approved by the US Patent and Trademark Office in April 2026. Rather than developing models, the company has shifted from detecting untrustworthy content toward artificial intelligence security, data privacy, and content verification.
Why it matters
This approach addresses the need to apply security policies at a centralized layer instead of monitoring individually the artificial intelligence tools used by employees at organizations handling sensitive data. The ability of the solutions to operate in private data centers and environments isolated from the internet directly concerns financial, healthcare, public-sector, and defense organizations that limit the transfer of data to external systems. The company’s shift from model development toward security, privacy, and content verification shows that artificial intelligence is being addressed not only through its production capacity but also in conjunction with institutional risks and the source of outputs. However, because the text provides no data on detection accuracy, false-positive rates, or performance in organizational applications, the technology’s effectiveness under real-world conditions remains an open question.