Traceable AI, the industry's leading API security company, today announced that John Kindervag, known for creating the Zero Trust Model for cybersecurity, will be joining Traceable as an advisor. As one of the world's foremost cybersecurity experts, Kindervag will be deeply involved in the product strategy for Traceable's API Security Platform, as well as helping Traceable educate the market on the urgency of prioritizing API security. Kindervag, who currently serves as SVP, Cybersecurity Strategy, and Global Fellow at ON2IT, is the first in a series of experts to support Traceable's mission to revolutionize the API security market. "APIs offer companies an unprecedented opportunity to drive economic growth," shared Kindervag. "But most people don't understand how they work or why they essentially offer hackers the most direct route to the sensitive data they're seeking. Traceable understands both the economic potential as well as the danger that APIs present for organizations and their customers, and I'm excited to work with them on bringing API security to the forefront of the industry's priorities."

While the use of machine learning (ML) and artificial intelligence (AI) for IT security may not be new, the extent to which data-driven analytics can detect and thwart nefarious activities is still in its infancy. As we've recently discussed here on BriefingsDirect, an expanding universe of interdependent application programming interfaces (APIs) forms a new and complex threat vector that strikes at the heart of digital business. Stay with us now as we answer that question by exploring how advanced big data analytics forms a powerful and comprehensive means to track, understand, and model safe APIs use. To learn how AI makes APIs secure and more resilient across their life cycles and ecosystems, BriefingsDirect welcomes Ravi Guntur, Head of Machine Learning and Artificial Intelligence at Traceable.ai. The interview is moderated by Dana Gardner, Principal Analyst at Interarbor Solutions. Gardner: Why does API security provide such a perfect use case for the strengths of ML and AI? Why do these all come together so well? Guntur: When you look at the strengths of ML, the biggest strength is to process data at scale. And newer applications have taken a turn in the form of API-driven applications. Large pieces of applications have been broken down into smaller pieces, and these smaller pieces are being exposed as even smaller applications in themselves. To process the information going between all these applications, to monitor what activity is going on, the scale at which you need to deal with them has gone up many fold. That's the reason why ML algorithms form the best-suited class of algorithms to deal with the challenges we face with API-driven applications. Gardner: Given the scale and complexity of the app security problem, what makes the older approaches to security wanting?

Jyoti Bansal founded AppDynamics, a company that Cisco bought in 2017 for $3.7 billion. He might have been content to rest on that big win, but instead he went on to launch Harness and a venture capital arm, Unusual Ventures. Today, he announced his newest company called Traceable, which attacks security at the code level. Bansal says that security has traditionally looked at protecting the network and hardware, but today the attack surface is more at the software level, and that's why he decided to start another company. "The software is becoming the primary attack vector for a lot of things. If you look at most of the sophisticated data breaches […], they are happening in the code, not in the network or the infrastructure anymore," he explained.

Traceable, a startup developing an end-to-end cloud app security solution, today emerged from stealth with $20 million in venture equity financing. Newly flush with capital, CEO Jyoti Bansal intends to focus on acquiring customers globally while growing Traceable's team and accelerating R&D. Cloud-native apps are often built with hundreds or even thousands of API microservices (i.e., loosely coupled services), making them difficult to protect at scale. Gartner predicts that by 2022, API abuses will be the most frequent attack vector, which isn't surprising considering API calls represented 83% of web traffic as of 2018. Traceable ostensibly protects these APIs with machine learning algorithms that analyze app activity from the user and the session all the way down to the code.