Cybersecurity startup Red Sift announces $8.8m Series A

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Cognitive data platform, Red Sift, recently announced the closure of its $8.8m investment round from research-led VC MMC Ventures. Red Sift provides cloud-based email security solutions to its customers using machine learning to analyse and synthesise data from core business processes.

Founded in 2015 by Rahul Powar and Randal Pinto, Red Sift is a data-driven cybersecurity business, analysing thigs like emails, to support its global customer base to manage its online security more effectively.

Co-founder and CEO of Red Sift, Rahul Powar said: “We live in a world where users are told to simply accept that email has a fundamental security flaw and organisations transfer the onus of detection onto their busy employees. Meanwhile, it’s getting more difficult to identify these rogue messages as the targeting and sophistication of the attacks mature.

“The bruising reality for businesses is that it is around 400 times more expensive to defend against an attack than it is to launch one, meaning the economics of cybersecurity have to date, always favoured the largest businesses with the deepest pockets. This funding round will help us to continue developing transformational cybersecurity solutions essential for everyday cybersecurity for all types of organisations.”

Mina Samaan, Investment Manager at MMC Ventures added: “There is no hiding in the fact that the use of email as an initial attack vector continues to dominate the way in which hackers are able to begin cyber-attacks. Red Sift’s easy-to-implement product suite offers businesses of all sizes the ability to protect against advanced email threats while also flagging to employees the potential threats from emails they receive.

“We’ve been tracking the team at Red Sift from the very early stages of the business and we’ve been consistently impressed with their ability to execute and achieve their goals. We look forward to working with Rahul, Randal and the rest of the team as they enter the next phase of growth”.