Leading Indian Fintech Reduces Application Risk by 98% While Maintaining Release Velocity Using CloudDefense.AI

One of India’s leading fintech companies reduced application security risk by 98% by adopting a risk-first operating model and embedding CloudDefense.AI into daily workflows. The organization remains anonymous, but the results reflect a high-scale fintech environment where every release impacts sensitive data, transaction flows, and trust.

As the company expanded across multiple customer-facing applications and API-driven services, traditional security processes became harder to operationalize. Security teams faced a growing volume of findings that required extensive manual validation, severity ratings that didn’t reflect real-world exposure, and late-cycle discovery that made fixes costlier and more disruptive. The challenge was not visibility-it was decision quality: identifying which issues truly increased risk in production, and fixing those without slowing delivery.

To regain clarity and control, the fintech organization reoriented security around exploitability and exposure. Findings were prioritized based on what could realistically be leveraged in real application flows, not simply what could be detected. Risk was tracked continuously across services and environments as code shipped and remediations landed, allowing teams to understand how exposure changed over time rather than relying on static snapshots. Developers received issues with the context needed to act quickly-what mattered, where it lived, and what to change-reducing back-and-forth and accelerating remediation without introducing friction into release cycles.

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“Fintech security becomes manageable when the goal moves from counting vulnerabilities to continuously reducing real exposure,” said the Head of Application Security at the fintech organization. “The shift to risk-driven triage and developer-ready remediation helped us move faster with greater confidence, especially across critical applications and APIs.”

The organization’s operating model also introduced a more meaningful way to measure progress. Rather than tracking success by the number of tickets closed, leadership monitored risk reduction over time-aligning security outcomes with business impact and creating a scalable framework that could keep pace with growth, expanding application surfaces, and increasing transaction volumes.

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