A Unified Approach For Connecting AI/ML And DevSecOps Lifecycles

AIML And DevSecOps Lifecycles featured image

DevOps became mainstream when the Agile Manifesto entered the picture in 2001. Fast-forward to the present, and we all agree that DevOps is at the core of today’s SDLC management! The latest upgrade to DevOps is the integration of security with DevSecOps. While DevSecOps is grasping agile methodologies, artificial intelligence has emerged as a revolutionary technology using machine learning models!

“Faster deployments, improved security, enhanced quality assurance, intelligent monitoring, and the list continues. The jury of AI/ML models and DevSecOps curate exceptional outcomes, benefitting the global software development market!”

AI/ML in DevSecOps is now a reality. Almost 31% of DevSecOps teams use AI/ML models for code suggestions and reviews. Nearly 37% of Software Testers prefer AI-powered testing tools to minimize testing efforts. Not only that, but AI-driven monitoring and analytics systems are standard practices across the world. The transition to AI/ML and DevSecOps is so quick that even a tech enthusiast like you might feel left out. But don’t worry! We’ll help you catch up with today’s reality!

DevOps To DevSecOps: Security Taking Center Stage In SDLC

DevOps has played a significant role in the software development life cycle. However, there has been a shift recently with the introduction of DevSecOps. Unlike before, security is now an integral part of the SDLC rather than an afterthought. This change is crucial in tackling the increasing cybersecurity challenges we face today. As we embark on this journey, AI/ML models are emerging as contributors to enhance the security of development processes.

Does ML/AI Model Development Bring The Next Evolutionary Change in DevSecOps?

Well, the change is already here! As the market for A/ML continues to grow, IDC predicts worldwide spending on AI systems will increase by 42.8% within the following year. Most companies are focusing on data-driven operations across their digital platforms. However, only 28% of them succeed in achieving their transformation goals! But those who are growing are because they are using AI and ML models to realize their transformative ambitions.

What AI and ML bring to the platter of DevSecOps is indeed revolutionary! From changing how organizations build their DevOps culture to an effective digital transformation, AI/ML-powered DevSecOps leads a significant shift in the current software development market. Primarily, it sets DevSecOps at the front and center! But how are these intelligent systems enticing DevSecOps environments? We will discuss this in detail in the coming section!

A Complete Overview of AI/ML-Powered DevSecOps

More automation, more system-driven operations, more development flexibility, more functional productivity, more secured SDLC – DevSecOps shines in every aspect when AI/ML-driven Continuous Delivery takes center stage! Are you wondering how that is possible? Let’s decode the bits and pieces involved in running AI/ML-powered DevSecOps workflows!

#1 Use Of ML Within DevSecOps Pipelines

First, you need to understand the functionalities of an ML-driven pipeline. It is a chain of tools that automate processes using machine learning. These tools allow the correlation of ML data into an AI/ML model so that you can evaluate other data sets and train the ML model accordingly.

When you integrate the ML-driven pipeline over the DevSecOps tooling layer, the CI/CD workflows utilize both functionalities! While DevSecOps manages the security aspects at each SELC stage, ML-driven pipelines use system-generated data for data analytics and mining.

#2 Data-Backed Advanced DevSecOps Automation

Do you know that AI/ML models can easily automate complex security processes using data-backed machine learning? You can also leverage them proactively by fortifying the entire development lifecycle:

  • Automate container scanning to capture and analyze the security data using ML models.
  • Automate vulnerability scanning workflows alongside leveraging DevSecOps toolchains.
  • Automate data collection and processing across CI/CD workflows.

Continuous training in the AI/ML models gives you better insight into which DevSecOps processes require automation and how to reduce manual configurations. You can take advantage of data analytics reports to focus on advanced automation!

#3 AI-assisted Code Suggestions & Code Reviews

Once you finish implementing AI/ML to automate mundane tasks, you need to look for potential development areas where you can help the developers! You can introduce the following AI-assisted development capabilities to reduce the cognitive load:

  • Code Suggestions: This feature aims to increase development speed as the code segments get auto-filled suggested code. It helps increase the development productivity in the beta mode.
  • Code Reviews: Use an AI-driven toolset to auto-suggest the best code reviews during a merge request. It eliminates the guesswork with the proper contextual knowledge of code reviewing. Developers can easily consider the suggestions to implement the necessary code changes.

#4 Automated Software Testing Using DevSecOps Toolset

ML-powered automated testing tools accelerate the testing process. Developers using ML-powered vulnerability detection systems easily detect code defects during development. Thus, the QA team can efficiently perform regression testing and unit testing. Further, they can use real-time insights to identify potential threats during production release.

Benefits of Integrating AI/ML with DevSecOps: Reaching Beyond Human Capability

As you navigate AI/ML-powered DevSecOps principles, you will see how this revolutionary change benefits the development teams!

  • Faster Deployments: The more you can automate various aspects of the SDLC, the faster your teams can develop and deliver software features.
  • Improved Security: You can quickly identify and mitigate potential security risks using automation testing and analysis.
  • Quality Assurance: You can quickly increase the software quality by analyzing data patterns with a deeper understanding.
  • Intelligent Alerting: Using AI/ML, you can monitor DevSecOps workflows in real-time and receive alerts about potential anomalies.
  • Predictive Analytics: Your data engineers can easily predict potential issues and identify patterns that can impact the SDLC.

Well, the results of incorporating AI/ML models into DevSecOps have no boundaries. The more you integrate AI capabilities in your CI/CD pipelines, the better results your SDLC produces. So, it’s up to you and your implementation strategy to make your DevSecOps transformation successful!

A Quick Recap Of The Key Points

As we usher in the future of connected intelligent systems across DevSecOps workflows, we confirm that AI/ML models can bring the best outcomes! In DevSecOps, AI and ML automate data analysis, a task beyond human capabilities. The process of automating workflows while incorporating security greatly benefits organizations! So, you should embrace both technologies for achieving a successful data-driven digital transformation!


#1 What is MLOps?

Machine learning operations combine a set of DevOps practices that automate the deployment of machine learning (ML) workflows. You can implement MLOps alongside existing DevOps pipelines to standardize the SDLC.

#2 Can AI/ML with DevSecops boost development productivity?

Many organizations are already experiencing a step-function increase in development productivity with the adaptation of AI/ML in their DevSecOps processes. Indeed, it’s possible to boost development productivity with the correct implementation.

#3 How can DevSecOps use AI/ML models?

DevSecOps workflows should use AI/ML models to receive code suggestions and reviews. If the organization can successfully implement this process, then DevSecOps teams can automate workflows like continuous deployment, automated testing, and vulnerability scanning.

#4 Can I train my AI/ML models using the data generated through CI/CD pipelines?

You can collect the data from CI/CD workflows and use it to train your AI/ML models. Further, you can continue the process so that the AI-driven system can auto-detect data patterns to enhance the overall operational functionalities.

#5 Can AI-assisted code suggestions advance DevSecOps?

As AI-assisted code suggestions reduce development efforts, developers can substantially improve their programming experience. Further, AI/ML tools help developers reduce code defects and help them write code faster.

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Co-Founder & Director, Business Management
BDCC Global is a leading DevOps research company. We believe in sharing knowledge and increasing awareness, and to contribute to this cause, we try to include all the latest changes, news, and fresh content from the DevOps world into our blogs.

About BDCC

BDCC Global is a leading DevOps research company. We believe in sharing knowledge and increasing awareness, and to contribute to this cause, we try to include all the latest changes, news, and fresh content from the DevOps world into our blogs.