Is Machine Learning the Future of Cloud-Native Security?

Cloud-native architectures help businesses reduce application development time and increase agility, at a lower cost. Although flexibility and portability are key drivers for adoption, a cloud-native structure brings with it a new challenge: managing security and performance at scale. They have a dissolved perimeter, meaning that once a traditional perimeter is breached, lateral movement of attacks (such as malware or ransomware) often goes undetected across data centers and/or cloud environments. Sorting through interconnected data from thousands of services across millions of short-lived containers to understand a specific security or compliance violation in time is akin to finding a needle in a haystack. Developers are failing to bake security in early, opting instead to add it on at the end, and ultimately, they are increasing the chance of potential exposures in the infrastructure.