Artem Oleshko/123RF

AI In Cybersecurity Use Case #8: Valid

  • 16 July 2019
  • Sam Mire

This interview is part of our new AI in Cybersecurity series, where we interview the world's top thought leaders on the front lines of the intersections between AI and cybersecurity.

In this interview, we speak with Kevin Freiburger, Director of Identity Programs at Valid, to understand how his company is using AI to transform cybersecurity, and what the future of the cybersecurity industry holds.


1. What’s the story behind your company? Why and how did you begin?

KF: Valid offers identity management platforms for clients using Amazon Web Services. Valid integrates cybersecurity tools to monitor for potential malicious activity. The data generated from the activity logs is massive, so machine learning technology intelligently detects anomalies and alerts for potential bad actors. Valid is able to create a secure environment for customer data using cybersecurity tools augmented by machine learning.

2. Please describe your use case and how Valid uses artificial intelligence:

KF: Another way Valid uses machine learning is through facial recognition in identity management and enrollment solutions for enterprise customers. Facial recognition is often used to help the customer detect potential fraud. The algorithm uses machine learning to improve accuracy and reduce bias, so customers can more accurately detect potential fraud without increasing the number of false positives.

3. Could you share a specific customer/user that benefits from what you offer? What has Valid done for them?

KF: Valid hosts several U.S. states’ driver’s license applications. Machine learning improves biometric matching accuracy which enhances the government’s capability to accurately identify fraud and that directly reduces the number of fraudulent credentials issued.

Fraudulent credentials are typically used for commercial driver’s licenses, healthcare fraud, and other crimes. Solutions that integrate machine learning to improve biometric precision reduce the risk of crime perpetrators who might have otherwise used a government-issued fraudulent credential. Machine learning applications also improve cybersecurity to protect sensitive government data by detecting anomalous hacking activity.


About Sam Mire

Sam is a Market Research Analyst at Disruptor Daily. He's a trained journalist with experience in the field of disruptive technology. He’s versed in the impact that blockchain technology is having on industries of today, from healthcare to cannabis. He’s written extensively on the individuals and companies shaping the future of tech, working directly with many of them to advance their vision. Sam is known for writing work that brings value to industry professionals and the generally curious – as well as an occasional smile to the face.