The Advent of Containerization: A Game-Changer for Cloud Computing

The early 2000s saw the emergence of powerful processors that came with hardware support for virtualization, marking the beginning of the computing revolution that led to the development of cloud computing. This revolution enabled businesses to offer their users multiple services and applications that would have been financially impractical, if not impossible, in the past.

However, virtual machines (VMs) have several downsides. While they offer more malleability, scalability, and agility than bare-metal servers, they require significantly more memory and processing power. Moreover, they are less agile than the next evolution of this technology – containers. Containerized applications consist of only the necessary parts of an application and its supporting dependencies, making them lighter and more easily configurable.

The Security Challenges of Virtual Machines and Containers

Virtual machines and containers exhibit the same security issues that affect their bare-metal counterparts. However, container security issues reflect those of their component parts. With regards to VMs, bare metal installs, and containers, cybersecurity concerns and activities are very similar. But container deployments and their tooling bring specific security challenges to those charged with running apps and services.

Container-Specific Security Risks

Containers pose unique security risks, including:

• Misconfiguration: Complex applications made up of multiple containers can be misconfigured, granting unnecessary privileges and increasing the attack surface.
• Vulnerable container images: Malicious images can be stored in public registries, and the process of pulling images from public registries is opaque.
• Orchestration layers: Orchestration tools like Kubernetes can increase the attack surface, usually due to misconfiguration and high levels of complexity.

Container Security with Machine Learning

Machine learning can address the specific challenges of container security. By creating a baseline of normal behavior, machine learning can identify anomalies that could indicate potential threats from unusual traffic, unauthorised changes to configuration, odd user access patterns, and unexpected system calls.

Machine learning-based container security platforms can scan image repositories and compare each against databases of known vulnerabilities and issues. Scans can be automatically triggered and scheduled, helping prevent the addition of harmful elements during development and in production. Auto-generated audit reports can be tracked against standard benchmarks, or an organisation can set its own security standards.

Final Word

Machine learning can reduce the risk of data breach in containerised environments by working on several levels. Anomaly detection, asset scanning, and flagging potential misconfiguration are all possible, plus any degree of automated alerting or amelioration are relatively simple to enact.

The transformative possibilities of container-based apps can be approached without the security issues that have stopped some from exploring, developing, and running microservice-based applications. The advantages of cloud-native technologies can be won without compromising existing security standards, even in high-risk sectors.