Is Machine Learning the Future of Cloud-Native Security? Posted on : Jul 15 - 2019

The nature of containers and microservices makes them harder to protect. Machine learning might be the answer going forward.

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.

Challenges in the Cloud

The nature of containers and microservices makes it harder to protect them in these ways:

1. 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.

2. With a DevOps mindset, developers are continuously building, pushing, and pulling images from various registries, leaving the door open for various exposures, whether they are operating system vulnerabilities, package vulnerabilities, misconfigurations, or exposed secrets.

3. The ephemeral and opaque nature of containers leaves a massive amount of data in its wake, making visibility into the risk and security posture of the containerized environment extremely complicated. 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. View More