AI-DRIVEN RELEASE RISK INTELLIGENCE FOR SITE RELIABILITY ENGINEERING

Today's microservice platforms ship hundreds of changes every sprint across dozens of independently deployed services.While CI/CD pipelines are great at making sure code builds and tests pass, they don't tell us much about what really matters in production -things like operational risk, observability blind spots, or how ready a release truly is.As a result, SRE teams are often asked to push changes live without a clear picture of how those changes will impact reliability, performance, security, or the business-critical user journeys that actually drive revenue.This paper introduces an AI-powered Release Risk Intelligence system that looks at pull requests, code changes, and service dependencies to automatically produce clear release summaries, identify operational risks, and recommend the right level of SRE-grade monitoring.By using large language models to reason over code diffs and release data, the system turns raw development activity into practical, reliability-focused insight.This allows SRE teams to move beyond reacting to incidents and instead actively govern releases before problems reach production.