Customer Stories
Global Financial Information Provider Transforms Security Remediation with AI
Using GitHub Advanced Security and Copilot Autofix, Xebia helped a global financial information provider turn security remediation into an AI-assisted engineering workflow, achieving remediation rates of up to 100% while improving developer productivity.
At a Glance
Challenge
Increasing security debt caused by high volumes of vulnerability alerts, false positives, and manual remediation workflows.
Solution
Xebia combined GitHub Advanced Security and Copilot Autofix to improve developer experience and remediation outcomes.
Results
• Up to 100% remediation rates in planned campaigns
• Faster alert resolution
• Better developer experience
• Reduced false positives
• Faster adoption of AI-assisted security workflows
The Client
The client is a global leader in financial information and analytics, operating large engineering environments across multiple teams and repositories. From capital markets to private investments, the company helps organizations analyze opportunities, assess risk, and make faster investment decisions.
The Challenge: Increasing security debt and slow vulnerability remediation impacting developer productivity
Like many enterprise organizations, the client faced a growing backlog of security findings across repositories and teams. Traditional application security tooling helped detect issues, but was no longer enough. Developers still had to manually investigate vulnerability alerts, determine whether they mattered, identify root causes, and implement fixes. False positives consumed valuable engineering time and created alert fatigue.
The client defined several clear objectives:
- Speed up vulnerability remediation
- Reduce false positives and manual effort
- Improve collaboration between engineering and security teams
- Shift security earlier in the software lifecycle
- Evaluate how AI could support remediation at scale
In short, security teams wanted stronger remediation outcomes, while developers needed solutions that integrated into their workflows and reduced operational friction.
The Solution: Improving developer experience and remediation outcomes with GitHub Advanced Security and Copilot Autofix
The client partnered with GitHub and Xebia to explore how AI-assisted security capabilities could improve developer productivity and remediation outcomes. Xebia launched two focused GitHub Advanced Security remediation campaigns centered on Copilot Autofix for CodeQL code scanning.
This changed the workflow immediately. Most security tools stop at detection. Copilot Autofix went a step further by generating remediation suggestions that developers could review and apply directly in GitHub, removing a major bottleneck. Instead of jumping between security dashboards, documentation, and codebases, developers could resolve issues where they already worked.
The campaigns combined:
- GitHub Advanced Security
- CodeQL code scanning
- AI-assisted remediation with Copilot Autofix
- Dedicated remediation sprints
- Hands-on enablement from Xebia
Xebia played a central role in adoption. Through daily support sessions, working sessions, practical workshops, and targeted enablement, Xebia helped developers and security teams build confidence in AI-assisted remediation.
More than 80 developers participated, creating real momentum across engineering teams and helping establish security as a shared engineering responsibility.
The Results: Faster Fixes, Better Adoption, Less Friction
The impact was immediate.
- The first remediation campaign achieved a 100% completion rate for planned alerts.
- Teams resolved 85 targeted vulnerabilities and proactively fixed another 153 issues beyond the original campaign scope.
- The second campaign achieved a 97% remediation rate, resolving 114 out of 117 planned alerts.
- One engineering team resolved more than 4x the number of vulnerabilities originally planned during a focused 2-week sprint.
- GitHub Advanced Security and Copilot Autofix helped 86% of participating developers resolve alerts in under 3 hours.
- Teams also reported far fewer false positives. That meant less time validating noise and more time fixing real issues.
- Security became more naturally embedded into the software development lifecycle.
Driving AI adoption responsibly
The initiative surfaced an important lesson. Although AI can dramatically improve remediation speed, it still needs human validation, particularly in areas involving architectural complexity or business-specific application logic.
Together, the client, GitHub, and Xebia established practical guardrails for AI-assisted remediation:
- Review AI-generated fixes carefully
- Validate changes in application context
- Create feedback loops to improve future recommendations
- Combine human oversight with automation
The initiative also generated valuable feedback for GitHub’s product teams around campaign scalability, governance, and enterprise rollout models.
Looking Ahead
What started as a focused remediation initiative became something much bigger. With executive sponsorship, engaged engineering teams, and continued collaboration between GitHub and Xebia, the organization is building long-term AI-assisted security practices across its engineering landscape.
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