Skip to main content

Using AI and Machine Learning to Accelerate DevSecOps Transformation

The application of AI and machine learning for DevSecOps security is conceptualized by a robot monitoring a digital cybersecurity display.


The DevSecOps methodology seeks to integrate Development, Security, and Operations processes into a single, streamlined software lifecycle. Doing so requires shifting left, which means performing security testing and analysis early and often in the development cycle. DevSecOps also involves continuous security monitoring, data collection, and process improvement. AI and machine learning technologies can help accelerate DevSecOps transformation by automating many security workflows, which lowers the time, effort, and expertise needed to adopt DevSecOps practices.

How AI and Machine Learning Accelerate DevSecOps

Artificial intelligence (AI) and machine learning (ML) use highly sophisticated computer systems to process and learn from vast amounts of data, which makes them adept at quickly sorting through information to spot patterns and irregularities. This characteristic makes AI and machine learning technology, such as neural networks, useful for cybersecurity and DevSecOps applications.

Let’s discuss four common examples of how AI and machine learning can enable and improve DevSecOps.

Automated Software Security Testing

DevSecOps requires security teams to perform vulnerability and defect testing as early and often as possible. If these tests must be done manually, it will add bottlenecks and delays to your pipeline. One of the most common ways that automated security testing helps to alleviate these delays is by using signature-based analysis to compare code to a list of known vulnerabilities.

However, signature-based detection still suffers from some limitations – it’s prone to false positives, and, even worse, it often fails to recognize new threats that don’t match known vulnerabilities. AI and ML security testing solutions overcome these limitations without slowing the testing process.

Rather than comparing code to a list of known vulnerabilities, AI and machine learning solutions utilize various complex algorithms that can learn from past experience. In this context, “learning” means extracting information from past examples and then developing an algorithm that can recognize, predict, and categorize new examples. That means AI and ML can extrapolate a new potential vulnerability even if it has not seen it before.

This ability to learn and evolve makes AI and machine learning useful for network and endpoint security applications, such as intrusion detection and prevention systems (IDS/IPS), antivirus software, and spam filters. While these aren’t directly related to software security testing or the development pipeline, they’re essential for Ops security and reduce the chances of operational delays.

Effective Security Triaging

Automated security testing is only the first step. Once a vulnerability is detected, it needs to be analyzed, prioritized, and assigned to the appropriate team for resolution. As a manual process, this involves sorting through alerts to sift out the false positives from the true ones, analyzing the severity, assigning a priority level, then determining which team is responsible for resolving the problem. This method creates another potential security bottleneck in your DevSecOps pipeline.

AI and machine learning tools analyze detected vulnerabilities and intelligently determine (based on previous data input and prior experience) which ones are legitimate. The tools then prioritize issues using quantitative threat levels (e.g., 1-5, or low-med-high), which security engineers can use to triage and assign incidents. Many solutions also include automated incident resolution capabilities, which can handle low-risk vulnerabilities without wasting an engineer’s valuable time and effort.

Continuous Monitoring in Production

The security threats to your software and systems don’t stop once your product is in production, which is why continuous security monitoring is essential. In the same way that AI and machine learning improve security detection and response during development, they can continue to monitor and protect software security after release.

Actionable DevSecOps Metrics

One of the basic requirements of DevOps (and, by extension, DevSecOps) is continuous improvement. That means monitoring and collecting data from every process and workflow in the development lifecycle, then analyzing that data to determine how to improve and optimize.

AI and ML combine analytics and data science, making them ideal tools for finding and understanding patterns. AI tools for DevOps can analyze data and make data driven decisions, providing dashboard visualizations of existing data trends and predicting future outcomes based on that data. This aspect gives you actionable DevSecOps metrics that you can use to improve your processes and further streamline software releases.

Implementing AI and Machine Learning in Your DevSecOps Environment

AI and machine learning can help reduce the barriers to DevSecOps adoption and optimization, but successful implementation requires a thoughtful strategy. You should start by identifying the specific DevSecOps pain points or processes that could be improved by incorporating AI/ML tools. Value stream analysis is one method of finding inefficiencies in software development pipelines that AI and machine learning would solve.

Additionally, you should set your teams up for success by providing the best AI/ML training, tools, and support. For example, the DevSecOps experts at Copado Strategic Services can work with your organization to identify strengths and weaknesses in your processes, bridge knowledge gaps, and develop customized solutions.