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Global Screening Services reduces Terraform review time with an AI Cloud Guardian
Storm Reply built an AI assistant for Global Screen Services (GSS) that explained what proposed cloud changes would do and highlight potential disruption, reducing complex release preparation from two days to less than four hours.
The CHALLENGE
Release changes to GSS’s cloud infrastructure faster without increasing the risk of disrupting its sanctions screening platform.
THE SCENARIO
A careful review process that consumed engineering time
GSS provides a sanctions screening platform for regulated financial institutions, where the platform has to stay available and trustworthy at all times. In this environment, every infrastructure change must be assessed carefully.
GSS manages this infrastructure using Terraform. Terraform allows engineers to define servers, networks, permissions and other cloud resources in code. Before a change is released, it produces a detailed plan showing everything that will be added, changed or removed. This required engineers to read the plan and work out whether planned changes could lead to disruption. While a single review would take 30 minutes, a complex release could require two days of work.
The Solution
An AI assistant that identifies disruptive changes
Storm Reply worked with GSS to build Cloud Guardian, an AI assistant integrated into the company’s existing release process.
After a proposed change passed its initial automated tests, the release pipeline produces one or more Terraform plans. Cloud Guardian read these plans together, looked for potentially disruptive changes and creates a concise report for the engineer.
Instead of repeating the technical plan, the report explains the changes, identifies affected infrastructure and highlights any issues that require closer attention.
A recommended route for each change
Routine updates deploy automatically, while high-risk changes are intelligently escalated to a controlled release process for mandatory human review.
Automated checks on the AI's output
Automated validation ensures the AI’s output always aligns with GSS’s release policies. This strict governance prevents teams from acting on incomplete, inaccurate, or non-compliant data.
Earlier visibility of differences between environments
Engineers gain immediate insight into configuration discrepancies between staging and production, allowing them to resolve environment drift before pushing changes live.
HOW WE DID IT
Embedded collaboration and iterative design
Rather than a traditional vendor hand-off, Storm Reply embedded directly within the GSS platform team. This co-development approach ensured the AI model was finely tuned to GSS's specific release pipelines and cost-efficiency requirements.
Because Cloud Guardian was injected directly into existing workflows, GSS didn't need to overhaul its toolchain or introduce friction with new platforms. Furthermore, the embedded approach upskilled GSS engineers, equipping them with the knowledge to maintain and scale the AI solution independently.

