MoneyGram processes seamless money movement across 200 countries and territories through nearly 500,000 locations, serving 60 million active customers. When the company made a deliberate bet on AI-accelerated software development, it faced a question most organizations were just beginning to take seriously: if developers can ship code at machine speed, how does security keep up?
“We believed AI would fundamentally change how software gets built. Our opportunity was to ensure security evolved with it, not as a gate, but as part of the architecture. At machine speed, innovation and security shouldn’t compete; they should accelerate together.”
MoneyGram has long been an industry leader in security, with investments in repository tiering, automated GitLab workflows, and defined remediation objectives.
As MoneyGram began implementing AI across its business, the security team saw an opportunity to evolve its approach and stay ahead of the pace of innovation.
AI coding tools were increasing developer speed, so MoneyGram needed security to scale with engineering velocity - reducing noise, improving prioritization, and enabling faster, more intelligent remediation without slowing delivery.
By adopting a VulnOps operating model, MoneyGram could bring automated triage and remediation directly into the development lifecycle.
After evaluating several approaches, MoneyGram chose Pixee to operationalize VulnOps at scale. Pixee addressed both sides of the challenge directly.
Triage came first: Pixee's exploitability analysis filtered out findings that weren't real risks, cutting the noise before it reached development teams. Then remediation: for findings that were real, Pixee generated merge-ready pull requests matched to MoneyGram's code conventions and integrated directly into their existing GitLab pipelines.
With triage and remediation running in the same continuous workflow, teams stopped having to choose between speed and thoroughness. Security analysis ran automatically, developers received actionable fixes rather than alert lists, and time from finding to fix dropped significantly.
Continuous — running inside MoneyGram’s existing GitLab pipelines
Fighting machine speed with machine speed.
Pixee analyzed more than 200 repositories across 8,000+ automated scans. On the triage side, 96% of findings were processed automatically, with a 72% reduction in the findings requiring human attention, eliminating more than 3,500 hours of manual security work.
On the remediation side, Pixee generated automated fixes directly in the developer workflow, saving an additional 3,000+ hours of developer effort. Developers reviewed and merged pull requests that already matched their conventions, rather than writing fixes from scratch.
The VulnOps program delivered:
What changed most was how the team operated. With higher confidence in signal quality, teams stopped spending time on SLA-driven prioritization machinery: 30-day remediation windows, 60-day windows, exception processes and reviews. They could focus on one question:
Machine-speed security, VulnOps, continuous remediation at scale: for much of the industry, these are still goals. For MoneyGram, they're already how the team operates.
“The future of cybersecurity isn’t about choosing between speed and security—it’s about eliminating the tradeoff entirely. Machine speed is our advantage. The organizations that lead will be those that build defenses capable of moving, adapting, and responding as fast as the technology they protect.”
Pixee triages what your scanners already find, then opens merge-ready fixes inside your pipeline.
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