In Sportstech, software doesn’t just support the business-it runs the experience.
On game day, traffic patterns change in minutes. Real-time feeds, ticketing, identity flows, partner APIs, mobile clients, and content delivery all become part of the same system. Teams ship fast because they have to. But security teams still need to answer a hard question with confidence:
“Which findings are real risk-and which ones are noise we can safely ignore?”
One of the leading U.S. Sportstech companies faced that exact problem. They weren’t missing scans. They were missing signal-and the ability to turn security output into decisions engineering could move on quickly.
The company remains anonymous under NDA, but the workflow and outcomes below reflect a real production AppSec program.
Environment
This company operated like most modern Sportstech platforms:
- Multiple services and release trains: teams deployed continuously, often independently, across a wide set of applications.
- Public-facing and partner-facing surfaces: APIs consumed by mobile apps, internal systems, and third-party partners.
- Sensitive data + identity: user accounts, payment flows, and authorization logic were business-critical.
- A fast-changing dependency graph: modern frameworks, SDKs, and third-party packages updated frequently.
- High variability in load: sudden peaks during events forced engineering to prioritize performance and reliability while still shipping features.
Security was not a side concern. It had to be embedded into delivery without adding friction the organization couldn’t afford.
What Wasn’t Working
The security team’s core challenge wasn’t lack of alerts. It was lack of confidence in what those alerts meant.
1) Too Many Findings, Too Little Actionability
Engineers received long lists of issues without confidence that the top items were truly exploitable or even relevant.
2) Prioritization Stalled Across Teams
Security couldn’t consistently prove which issues were reachable and urgent, so the same debates repeated sprint after sprint.
3) Noise Reduced Trust and Increased Delivery Friction
False positives and low-relevance alerts still consumed engineering time, weakening trust in the security program.
4) Progress Was Hard to Measure Credibly
Metrics were based on “counts of findings,” not validated exploitability or meaningful risk reduction.
What They Needed
The requirements were simple to say, harder to implement:
- Higher confidence prioritization (less arguing, more fixing)
- Evidence for exploitability (not just scoring labels)
- Consistency across teams (same rules, same logic)
- CI/CD-friendly workflows (fast feedback without blocking delivery)
- Traceable reporting (who fixed what, why it mattered, and what improved)
Why They Chose CloudDefense.AI QINA
They adopted CloudDefense.AI QINA as the prioritization and operating layer for AppSec-built to turn raw findings into high-confidence action.
QINA was selected primarily for its ability to:
- Reduce false positives through code-aware reasoning
- Provide context-driven prioritization aligned with real exploitability
- Keep security and engineering operating on a shared, consistent “source of truth”
- Fit into existing delivery pipelines rather than becoming a parallel security system
How They Used QINA in Practice
What made the difference wasn’t a single feature. It was how QINA fit into their operating model: detect → validate → prioritize → remediate → measure.
Phase 1: Build a Clean Baseline Across Code and Dependencies
They started by ensuring consistent visibility into two major sources of risk:
Code-Level Issues Through QINA Clarity (AI SAST)
They used QINA to analyze code patterns that frequently lead to real incidents in modern applications-issues in input handling, auth logic, unsafe data flows, and insecure APIs.
But instead of stopping at “this looks suspicious,” the workflow emphasized whether it actually mattered in the application’s execution reality.
Dependency Risk Through SCA
They also pulled in dependency findings to avoid the common blind spot: “our code is clean, but our packages aren’t.”
This was essential because modern Sportstech stacks ship fast-and dependency risk can change without any code changes from the team.
Ownership That Matched Engineering Reality
They mapped findings to:
- service ownership,
- repo boundaries,
- teams responsible for remediation,
- and release timelines.
That alone reduced friction because issues stopped getting lost in shared inboxes and generic backlogs.
Phase 2: Make Prioritization Evidence-Based (Not Opinion-Based)
This is where the program changed.
Instead of debating whether a vulnerability “sounds bad,” their security team and engineers used QINA to prioritize based on factors like:
- Reachability: is the vulnerable logic actually invoked in production paths?
- Exploitability context: does a realistic path exist from external input to impact?
- Impact boundary: what data or system does this touch if exploited?
- Service criticality: does this sit in a game-day critical workflow or peripheral service?
- Release timing: what must be fixed before the next release, and what can be scheduled?
This made security work feel less like a constant interruption and more like structured engineering work.
Phase 3: Integrate With CI/CD Without Becoming a Delivery Blocker
They intentionally avoided turning security into a blunt gate that breaks builds for everything.
Instead, they used QINA to create a tiered model:
“Fix-Now” Findings
High-confidence, high-impact issues that were provably actionable were surfaced as immediate remediation items.
“Fix-Soon” Findings
Important issues that weren’t release-blocking were queued into planned remediation cycles-so teams could handle them without thrashing.
Continuous Feedback Loops
Each release cycle improved the prioritization model because outcomes were tracked and fed back into what “actionable” looked like in their environment.
Phase 4: Operationalize Remediation as a Repeatable Loop
Once stabilized, the workflow became predictable:
- Scans run as part of delivery and change cycles
- Findings are correlated and prioritized with context
- Engineers remediate top items with clear evidence
- Fix verification and status tracking happen continuously
- Reporting reflects real progress (not raw counts)
This mattered because the company wasn’t trying to “do security once.”
They were building a system that could run every week-during calm periods and peak events.
Measured Outcomes
After the program matured, the company reported measurable improvements in signal quality and remediation efficiency.
- 98% accuracy in determining which findings were actionable vs noise, dramatically reducing false-positive churn.
- Reduced time spent disputing severity labels and debating what to fix first.
- Standardized prioritization improved follow-through across services even as the environment changed.
Under NDA, additional environment specifics and internal KPIs remain private, but the outcomes above reflect real production impact.
Why This Worked for a Sportstech Environment
Sportstech is unforgiving. It’s not just scale-it’s unpredictable scale under public scrutiny, where reliability and velocity can’t be traded off easily.
What QINA enabled for them was a shift from:
- “security produces findings”
to - “security produces decisions engineers trust”
What Changed Day to Day
- Fewer false-positive escalations
- Clearer prioritization across teams and services
- Less time wasted on debates
- More predictable remediation cycles
- Progress that could be explained credibly to leadership
Closing Note
For this leading U.S. Sportstech company, the breakthrough wasn’t “more scanning.”
It was getting to a place where AppSec could answer, quickly and consistently:
“Is this real risk-and should we fix it now?”
That clarity-delivered through CloudDefense.AI QINA-helped them reduce noise, increase confidence, and run application security as a workflow that engineering teams could actually sustain at high velocity.
