Knostic Secures $3.3M in Pre-Seed Funding

April 13, 2024, 3:31 pm
Knostic
Knostic
Total raised: $3.3M
Knostic, a cybersecurity startup based in Reston, VA and Tel Aviv, Israel, has raised a whopping $3.3 million in Pre-Seed funding. Founded by industry veterans Gadi Evron and Sounil Yu, Knostic specializes in need-to-know access controls for Large Language Models (LLMs).

The funding round saw support from Shield Capital, Pitango First, DNX Ventures, and Seedcamp, along with notable angel investors like Kevin Mahaffey, David Cross, Bryson Bort, Travis McPeak, Matthew Honea, and others. With this financial boost, Knostic aims to revolutionize the way organizations approach AI-powered innovation without compromising on security or value.

Knostic's unique approach to access controls ensures that employees only have access to the information they need to perform their job effectively, in line with organizational policies. This not only accelerates the adoption of LLMs but also helps organizations navigate the complexities of implementing these powerful tools safely.

Co-founder and CTO Sounil Yu likened LLMs to race car engines, emphasizing the importance of having the right controls in place to drive innovation without risking security. CEO Gadi Evron highlighted the risks associated with LLMs, such as the potential exposure of sensitive information, and how Knostic's solution goes beyond traditional permission models to prioritize user need-to-know.

Investors like Raj Shah of Shield Capital and Admr. Mike Rogers (Ret.) have expressed confidence in Knostic's approach, recognizing the critical role of safety in enabling enterprise adoption of LLMs. As organizations navigate the early stages of LLM adoption, Knostic's offering provides valuable insights into need-to-know violations and paves the way for secure implementation.

In a rapidly evolving landscape where data security is paramount, Knostic's innovative approach to access controls is poised to set new standards for safeguarding valuable assets in the era of Large Language Models.