AI and automation can help ensure DevOps security doesn’t impact performance
The world revolves around software and internet-based applications—and speed is crucial. Users have very little patience for delays and will quickly jump to something else if performance lags. Organizations have embraced DevOps and containers in an effort to accelerate development and deployment of software and maintain a competitive edge over rivals. One of the biggest challenges, though, is how to identify vulnerabilities and detect security issues without significantly degrading performance.
There will always be some impact on performance. You are adding an extra layer of functionality—a layer of security intended to detect and resolve security issues. No matter how fast that detection is, it will still take more time than not having any detection at all. The goal is to implement detection with as little impact on performance as possible. You don’t want detection to be a bottleneck for your cybersecurity efforts, or a drag on performance in general.
Bridging the Gap Between Speed and Security
One of the defining characteristics of a cloud-based, containerized, DevOps environment is its dynamic and continuous nature. In an effort to streamline and accelerate business, organizations embrace continuous development, continuous integration and continuous deployment combined with continuous monitoring, continuous testing and continuous improvement—all of which require continuous security.
Real-time monitoring for suspicious or malicious activity is crucial. The internet doesn’t sleep, and attackers are constantly scanning and probing for vulnerabilities and weak links to exploit in your network and applications.
Security is important, but not at the expense of productivity. The challenge is how to bridge the gap between speed and security—and ensure you can detect attacks effectively without introducing undue friction. You need balance—security that provides necessary protection without defying the whole point of trying to streamline and accelerate business. In other words, security needs to become a guardrail rather than a roadblock for DevOps.
Improve Detection with Automation and AI
Real-time protection is necessary—but not all real-time security solutions are the same. How attacks are detected is as important—or more important—than just detecting them.
Why? Simple. Attack detection based on signatures or generic patterns such as a core rule set or regular expressions are not customized to recognize threats in the context of the application. For example, with a RASP (runtime application self-protection) solutions, even if the control points are good, the end result will be poor without the right context and detection techniques. This approach is also reactionary and leaves you exposed for some period of time. It is generally ineffective until the signature is developed, and the signature can’t be created until the threat is detected and identified.
Automation and artificial intelligence (AI) can accelerate the process and provide more effective protection with less friction. An AI-powered platform can learn from stateless traffic and deconstruct application logic to create application-specific rules—and update the rules dynamically without the need for human intervention. Using dynamic, application-specific rules rather than signatures and manual rules can result in efficient detection and an exceptionally low false-positive rate. An automated solution that leverages artificial intelligence also can protect without waiting for signature updates.
Modern Security for Modern Networks
The days of periodic scans are long gone. The network architectures and threat landscape today demand constant, real-time protection. Legacy detection techniques are cumbersome and can’t keep pace, but an AI-powered attack detection platform that is automated will be more effective for protecting a dynamic, cloud-based, containerized, DevOps environment.
