Machine learning in cybersecurity is coming to IAM systems

The next time you have trouble accessing a mission critical application and need to prove your identity, you may be making your case not to network administrators or IT support but to a machine learning algorithm. The oft-discussed machine learning model has already taken root in the information security industry, as several vendors have embraced the technology to improve malware and threat detection and displace traditional signature-based detection. But now machine learning is making its way into identity and access management (IAM) to make rulings for authentications and authorizations. Several experts at the 2017 Cloud Identity Summit this week discussed machine learning in cybersecurity applications for identity management systems, as well the risks and rewards of such applications. The appeal of machine learning in cybersecurity is straightforward: IAM increasingly relies on a growing number of factors – from physical and behavioral biometrics to geolocation data -- to determine the identity and authorizations of an individual, and companies are turning to algorithms to process and judge those factors for IAM systems.