Building data security for Gen AI. Prev: Palo Alto Networks, Adobe | MIT Alum
Cofounder at UnboundSecurity. Building cyber security for gen AI apps. Previously engineering at Shogun, Tophatter, and Adobe Systems.
Unbound Security prevents leakage of sensitive information when employees use Gen AI applications. For example, we intervene and stop when someone pastes a code snippet that contains a secret key into ChatGPT while troubleshooting.
AI app discovery: We discover and catalog all AI apps in use by employees and help security teams understand the risk levels of each of these apps.
Granular application access policies: Rather than block all apps by default, we allow enterprises to allow some trustworthy AI apps and steer users towards those. For example, if your org invested in Chat GPT enterprise, we help get the most out of that investment by encouraging people to use that instead of a random cool file summarizer on the internet.
Protect data leakage: On the allowed AI apps, Unbound monitors all prompts and also blocks users from sharing sensitive information based on the configured AI usage policy.
Vignesh and Raj worked together at Adobe on the same Engineering team for 5+ years, solving some of the most complex problems in the digital advertising space - The team built sub-millisecond latency systems that processed billions of advertising artifacts. Vignesh went on to be founding engineer at Tophatter and an early engineer at Shogun (YC S18). Raj has built data security products for Palo Alto Networks and Imperva prior to Unbound where he observed the struggles of data security programs in large enterprises.