During World War II, Morse code operators would tell who sent a message based on the sender’s unique typing style, similar to identifying a person’s handwriting.

TypingDNA has updated that idea by developing a cybersecurity system that helps “recognize people by the way they type,” a “behavioral biometric” that can be used to authenticate digital identities. The New York-based company raised $7 million from Gradient Ventures, Techstars Ventures and Gapminder, to expand its support network and offer more tools on its platform.

The funding testified to investors’ growing interest in cybersecurity and appetite for innovative technologies in the field. Cybersecurity companies raised $8.56 billion in venture capital last year. Of that, only $96.8 million went to seven companies focused on biometric identity verification.

Biometrics such as fingerprint, voice and iris recognition have been seen as more secure than long passwords, for instance, and TypingDNA wants to add users’ typing patterns to the mix. 

The company prevents false logins by examining people’s typing patterns, or keystroke dynamics, to add another layer of protection in addition to inputting the correct password.

With growing privacy concerns over facial recognition, typing biometrics is seen as the best identification method without compromising privacy or security, said Darian Shirazi, general partner at Gradient Ventures.

Raul Popa, TypingDNA’s CEO and co-founder, added, “Keyboards are incorporated in almost any device today, making typing behavior the most widely available user biometric.”

  • London-based identity verification company Onfido scored $65.2 million in Series C from investors including Salesforce Ventures and SoftBank, the largest capital raised in the space. Plug and Play Tech Center is the most active investor in the space, with nine deals since 2013 including early venture rounds of Onfido.
  • Powered by artificial intelligence, TypingDNA’s technology records each user’s unique typing pattern and verifies users’ typing against the pattern to detect fraud.