How API security provides a killer use case for ML and AI

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?