Using AI to Enhance IaC for Greater Efficiency in Next-Generation Infrastructure

This article explores some of the important areas where AI is reshaping IaC operations and discusses what might happen in the future.

In today’s technology landscape, artificial intelligence (AI) has had a profound impact in almost every field. Infrastructure as Code (IaC) enthusiasts have been exploring how AI can drive the next revolution in the IaC ecosystem.

As you can see, AI plays an important role in improving DevOps and platform capabilities. And it is clear that AI will be at the core of future IaC practices. The following will explore some important areas where AI is reshaping IaC operations and discuss what may happen in the future.

Writing and maintaining IaC

The rise of IaC has greatly improved infrastructure efficiency and developer self-service capabilities. However, the increasing complexity of writing infrastructure code (whether it is YAML, JSON or HCL) has brought some challenges.

Despite advances in tools like Pulumi and AWS CDK, which allow developers to write IaC using general-purpose programming languages, writing large amounts of IaC code can be overwhelming. This barrier has prompted many engineering organizations to form dedicated DevOps and platform teams to master the process.

However, over time, the development velocity of these teams became a bottleneck in the deployment process, delaying the speed of infrastructure configuration and software delivery. AI tools like GitHub Copilot are revolutionizing the way developers write and maintain application code. These tools use machine learning models trained on large datasets to provide intelligent code suggestions and auto-completion features.

For example, when writing a function or method, Copilot can predict the next line of code, make suggestions for entire code blocks, and correct syntax errors on the fly. This not only speeds up development, but also helps maintain code quality by enforcing best practices.

The same principle applies to IaC, where AI can write configurations for frameworks such as Terraform, OpenTofu, CloudFormation, and Pulumi. For example, when defining an AWS S3 bucket using OpenTofu, the AI ​​tool can suggest the best configuration for bucket policies, versioning, and lifecycle rules based on industry best practices.

Similarly, when Pulumi is used with TypeScript, AI can recommend appropriate resource configurations, manage dependencies between resources, and ensure adherence to organizational standards.

An AI model trained on a large set of IaC code can identify areas for improvement, such as refactoring repeated code into reusable modules to improve efficiency and consistency. For example, if EC2 instances with similar configurations are frequently set up across projects, the AI ​​model can suggest creating a module to encapsulate the setup, thereby reducing duplication and potential errors.

AI also helps maintain consistency and governance in large-scale environments. By defining and enforcing policies based on industry best practices, AI helps organizations ensure compliance and security, especially for large and complex infrastructures. This reduces the need to "reinvent the wheel" and simplifies infrastructure management.

Automated Testing of IaC

Similar to developing IaC code, developers are often resistant to testing the code they write. However, to maintain good IaC practices, infrastructure code is treated as equally important as software code, and testing is a key factor in ensuring code quality.

Recent technological developments have paved the way for AI’s role in IaC testing, such as the introduction of testing capabilities in OpenTofu and Terraform (version 1.6). AI-based testing tools like CodiumAI, Tabnine, and Parasoft have already demonstrated great value in software development, a trend that is now expanding to the IaC space.

AI assistants can help developers by automatically generating tests for new and existing IaC code. This reduces the time and effort required for them to create tests, enabling faster implementation of testing frameworks in IaC tools. AI-driven testing will ultimately streamline processes, thereby improving the quality of IaC over time.

In addition, the integration of AI with integrated development environments (IDEs) makes automatic test generation more accessible. Tools like Copilot and Tabnine work seamlessly within developers’ preferred environments, providing suggestions and improvements directly in the workflow.

Advanced IaC management tools can support developer-optimized functionality, import resources directly into the IDE, and simplify development and infrastructure management without the need for additional tools.

Observability of IaC using AI

As modern systems grow in size and complexity, infrastructure observability, especially in cloud computing environments, becomes increasingly important. A notable example was GitLab’s two-hour outage due to an outdated production configuration, which highlighted the need for strong IaC practices and real-time monitoring to prevent configuration drift.

Managing cloud assets and resources at scale is a unique challenge in multi-cloud operations. AI can provide visibility into cloud management and analyze the extent to which infrastructure is managed through IaC, APIs, or manual ClickOps (which should be migrated to IaC where possible). AI can also classify operations, optimize resource management, and enforce AI-defined policies related to tagging, compliance, security, access control, and cost optimization.

The role of AI in observability goes beyond infrastructure management. By analyzing signals from large amounts of log data on platforms such as Datadog and Logz, AI can identify patterns and anomalies to help optimize system performance, troubleshoot problems, and prevent outages. This capability is particularly useful for IaC, as AI can detect abnormal behavior and respond to ensure that infrastructure remains secure and efficient.

For example, on the platform, AI is already used to perform detailed analysis of CloudTrail payloads, which helps to find hard-to-detect patterns in large data sets. This, in turn, can quickly identify anomalies and IaC coverage gaps and report potential risks and cost-saving opportunities, such as retiring idle resources.

        Figure 1 Using CloudTrail for IaC coverage and risk analysis

AI in IaC: Beyond the Hype

AI is more than just a buzzword, it is a powerful tool that enhances many engineering fields, including IaC. And the technological advances we are seeing today are just the beginning.

Looking ahead, AI will play an increasingly important role in areas such as code generation, automated testing, anomaly detection, policy enforcement, and cloud computing observability. By integrating AI into IaC workflows, organizations can achieve greater efficiency, security, and cost-effectiveness, laying the foundation for more advanced and scalable cloud computing infrastructure.

The future of IaC is not just about writing better code, it will also leverage AI to drive innovation and facilitate the next wave of infrastructure and cloud management.