In financial services, security isn’t a department goal. It’s a business survival requirement.
When you’re moving money, handling identities, and operating under tight compliance expectations, “we’ll fix it later” isn’t a real option. At the same time, engineering teams can’t pause delivery every time a scanner produces another long list of red flags. New features still need to ship. Reliability still matters. Customers still expect speed.
That’s the balance a leading U.S. financial services company was trying to protect. Their name stays private under an NDA, but their challenge is familiar: keep building fast, keep risk under control, and be able to prove it-internally and externally-without turning security into a constant blocker.
After putting CloudDefense.AI into their application security workflow, they drove their application risk down by 98%.
The environment they were operating in
This team wasn’t running a simple monolith with a slow release schedule. They were operating like most modern financial services companies do-cloud-native applications, frequent deployments, multiple services, and a growing dependency footprint.
In that kind of environment, application risk doesn’t come from one obvious place. It builds quietly across:
- code that changes every week
- open-source packages that update constantly
- APIs that must stay available and performant
- CI/CD pipelines that decide what reaches production
- teams that are under pressure to deliver features quickly
Their security team wasn’t looking for “more findings.” They were looking for fewer surprises and more control. They wanted the organization to spend time on the issues that truly matter, not on the ones that simply look scary in a report.
What wasn’t working before
Before CloudDefense.AI, their AppSec setup looked fine in theory. Scans ran on schedule. Reports were produced. Tickets existed. Security reviews happened.
But the lived experience felt messy.
Findings would arrive in batches, and the first problem was always the same: not everything deserved the same attention, but everything was competing for it anyway. Security had to sift through noise. Developers had to decide whether issues were real. And in the middle of that, velocity slowed down while risk didn’t drop as much as it should have.
One pattern kept repeating.
Security would flag issues. Developers would respond with questions that are completely fair in a fast-moving engineering org:
- Is this actually exploitable?
- Is it reachable in a live code path?
- Is it present in production or only in a corner case?
- Is this a real priority or just another scanner warning?
The bottleneck wasn’t scanning. The bottleneck was certainty.
When certainty is low, everything turns into back-and-forth. Triage becomes debate. Debate becomes delay. And the backlog starts to feel endless.
Over time, that creates something dangerous: people stop trusting the priority list. Not out of laziness, but out of self-preservation. Developers can’t treat hundreds of findings as equally urgent. Security can’t manually validate every item forever. Leadership can’t tell whether the organization is truly getting safer or just staying busy.
Why they chose CloudDefense.AI
This company wasn’t trying to buy a prettier dashboard. They were trying to remove friction from the parts of AppSec that slow everyone down.
They needed a solution that would help them answer a simple question quickly:
What should we fix first if we want risk to go down in the real world?
Their expectations were practical.
They wanted a risk-first approach instead of a volume-first approach. They wanted remediation that didn’t require a meeting to interpret. They wanted the workflow to fit into the way engineers already work-so security becomes part of delivery, not an interruption that people quietly work around.
CloudDefense.AI fit that model, and that’s why they moved forward.
How they rolled it out without creating chaos
They didn’t start by flooding teams with tickets.
They started by rebuilding trust in the signal.
The first phase focused on creating a baseline that security and engineering could both stand behind. Instead of treating every finding as equally urgent, they concentrated on the issues that would materially reduce risk if fixed.
That shift sounds simple, but it changes behavior immediately.
Developers respond differently when the request is “fix what matters most” instead of “fix everything.” The second message creates resistance. The first creates momentum.
Once the baseline was established, they moved into a steady rhythm that matched their release cadence. Security could identify the highest-impact items without drowning in volume. Engineering could take action with less ambiguity. The conversation changed from “how many findings” to “how much risk did we remove.”
How remediation became consistent instead of exhausting
In many companies, vulnerabilities aren’t ignored because developers don’t care. They’re ignored because the handoff is unclear.
A ticket that says “injection risk” or “insecure deserialization” can become a time sink. Developers have to ask where it is, whether it triggers, what makes it risky, and what the correct fix actually looks like.
This company needed the opposite experience.
They needed remediation to feel clean and predictable. When a vulnerability is raised, the next steps should be obvious: who owns it, what needs to change, how to validate the fix, and how to confirm closure.
With CloudDefense.AI in their workflow, they built a loop that stayed consistent:
- identify high-impact issues
- route them to the right owners quickly
- reduce ambiguity with clear fix direction
- verify closure with confidence
- track progress in a way leadership can understand
That’s when things started to click. Security stopped feeling like a constant escalation engine. Developers stopped feeling like security tickets were a guessing game. The backlog stopped behaving like a flood and started behaving like a system the organization could control.
What changed in measurable terms
Their application risk dropped by 98%.
That wasn’t achieved by hiding issues or redefining the goal. It came from focusing attention on the vulnerabilities that actually move risk and sustaining a remediation rhythm that didn’t burn teams out.
Alongside that risk reduction, the internal experience changed in ways that matter.
Security teams spent less time arguing about severity and more time driving outcomes. Developers spent less time questioning whether issues were real and more time fixing the ones that were clearly worth fixing. Leadership got reporting that felt grounded, not stitched together manually.
Most importantly, improvement wasn’t temporary. Risk trended downward in a way the organization could see and maintain.
Conclusion
What this financial services company put in place wasn’t flashy-and that’s the point.
They didn’t rely on bigger reports or more scanning to feel secure. They focused on clarity, stronger prioritization, and a remediation rhythm their teams could actually maintain while shipping at speed.
The 98% risk reduction is the measurable outcome, but the longer-term value is the workflow behind it: fewer severity debates, faster fixes, and a security posture that keeps improving instead of drifting back over time.
In an industry where trust is earned every day, that kind of steady, repeatable progress matters.
