INSUBCONTINENT EXCLUSIVE:
Sensitive data leakage is one of the biggest negative side-effects of cloud-based apps and services
Today, a startup that has built an AI-based platform that can detect and take action on that data is coming out of stealth with funding to
While in stealth it started out working with high-growth startups like Grofers and Exabeam, later expanding its customer base to Fortune 500
changed to reflect the expanded scope of the company, not just identifying unstructured data but being able to take actions on it
It had raised angel funding of just under $5 million that previously had not been disclosed
Bain Capital Ventures and Venrock have co-led this latest, bigger round of $15.5 million, with Pear VC (Pejman Nozad); Sri Viswanath, CTO
of Atlassian; and Kelvin Beachum, Jr
of the New York Jets also participating.If $20.3 million sounds like a sizeable investment for a company that had yet to build up a public
specifically worked on machine learning research at Stanford, focusing on HR and recruitment data, and has one exit already under his belt
(a networking and advice platform called Chalky) while also working as an investor at Venrock and as an analyst at Pejman Mar Ventures,
while Sathe was the lead engineer who built and scaled Uber Eats.Between the two of them, their experience spans a range of use cases of
where teams are handling many petabytes of data across multiple applications, which presents many opportunities for data leaks
The lack of any products on the market to address this was what led them to building Nightfall, Madan said in an interview.Nightfall is
tackling a specific issue in the market
Cloud-based collaboration platforms have been the making of distributed teams, which can use them to communicate with each other and work
together, sharing data from different apps to get things done even when they are not in the same physical space
But they have also opened the door to a potential problem when it comes to data protection: the information shared on these platforms can
this is a big big-data task: there are petabytes of data at play covering hundreds of different applications
Nightfall has built machine learning models to scan all of this, detect the data and then either provide automatic actions or options for
manual actions to take on it: typically, the options are either to delete, redact or quarantine the data, or notify the relevant teams to
Nightfall has built a powerful and elegant solution to this problem
