More and more enterprises today are turning to GitOps as a way to better control and audit technology. Initially focused on code, GitOps increasingly focuses on configurations and IT management as more security and network capabilities are shifted to application teams. Because it is based on Git versioning and enables close monitoring of changes to configurations, GitOps should make it easier to solve configuration error issues that are the root cause of so many security problems. This approach ensures consistency and reproducibility, as configurations can be easily reverted to a previous state if an issue arises. The Git approach also makes rollbacks faster and less traumatic.

In practice, GitOps is only a partial solution to configuration problems. Versioning bad configurations only captures errors but does not correct them. A far more important problem to address is why so many configuration errors continue and what can be done about them. Rather than rely only on GitOps, teams should first implement AI and analytics capabilities of applications that can reduce human configuration errors.

The Promise of GitOps for Configuration Security

Let’s be clear. GitOps offers several key advantages for configuration security over older approaches, like managing configurations in Ansible or via a spreadsheet. One of the primary benefits of GitOps is the enhanced ability to track and audit configuration changes through Git versioning. Every modification is meticulously recorded, creating an audit trail that ensures transparency and accountability. This level of detailed monitoring can significantly improve control over configurations, reducing the frequency of errors that often compromise security.

Moreover, GitOps introduces a declarative approach to configuration management. Instead of manually configuring systems, configurations are defined in code and stored in Git repositories. The automation inherent in GitOps workflows further reduces the risk of human error, as changes are automatically applied and validated against the defined configurations.

The Limitations of GitOps in Addressing Configuration Issues

Despite its strengths, GitOps is not a silver bullet for all configuration problems. One critical limitation is its ability to capture errors without inherently correcting them. While GitOps can document every change and help teams identify breaking changes that result in problems, it does not automatically resolve these errors. Further, GitOps does not guarantee security. Without configuration scanning and, even more importantly, some way of analyzing configurations to ensure they conform with best security practices as recommended by vendors or open-source project maintainers, GitOps remains an incomplete solution.   Teams may push multiple breaking configuration changes without realizing their error, or they may inadvertently insert security vulnerabilities as configuration changes when they roll back to older versions. Worse still, they may insert vulnerabilities as a successful deployment and never notice their mistake.

The persistence of configuration errors often stems from deeper, systemic issues. Human operators make mistakes. Manual configuration processes are inherently prone to errors. Even with GitOps, if the initial configuration is incorrect, versioning alone cannot rectify the fundamental error. Additionally, systemic issues within IT management, such as inadequate training, lack of standardized processes, and insufficient cross-team collaboration, contribute to the continued prevalence of configuration errors.

Augmenting GitOps with AI and Analytics

To effectively address configuration errors, it will be essential to augment GitOps practices with AI and configuration analytics. Infrastructure-as-code (IaC) scanners accomplish some of this, but they tend to be limited in the scope of their coverage and unable to keep up with best practices or security recommendations for the entire landscape of open source and proprietary infrastructure components. At best, IaC scanners are blunt instruments. They recommend fixes without comprehension of the nuances of an infrastructure setup. This can result in performance issues, downtime, or other problems when suggested fixes break your systems.

In contrast, AI and machine learning technologies can analyze vast amounts of data to identify configuration errors and recommend changes based on the context and details of your infrastructure footprint. By identifying patterns of best practices among all users of a component and matching those used that mirror most closely to an organization’s infrastructure architecture and requirements, AI-driven tools can suggest optimal configurations and prioritize security fixes. Even better, AI tools can use an organization’s own security data, control structures and log files to recommend truly bespoke configuration optimizations.

Conclusion: GitOps + AI Equals Better Together

AI is not a Holy Grail. Machine learning systems (and generative LLMs) in particular can make mistakes, so a human should remain in the loop for changes. Human judgment remains essential to maintaining good configuration security and hygiene. However, as organizations continue to suffer from tool and API sprawl, keeping up with configurations for more and more services becomes increasingly difficult. By creating a clearer structure and clean versioning and auditing lanes, GitOps is a necessary but not sufficient step toward better configuration management. AI designed to leverage and complement GitOps can significantly enhance this process by augmenting human expertise. Together, GitOps and AI build upon each other, creating a robust foundation for modern configuration management. This combined approach not only strengthens configuration security but also paves the way for a more resilient and efficient IT infrastructure. GitOps makes you more organized. AI makes you smarter. Together, they make you better.