Large petrochemical enterprises operate in one of the most complex and risk-sensitive environments in the world. Their application ecosystems span legacy systems, modern cloud-native services, third-party integrations, and highly regulated operational workflows. For these organizations, application security is not just a compliance requirement-it is a core operational necessity.
This is the story of how a Global Petrochemical Leader Secured Its Applications at scale by moving beyond “more scanning” and toward a security program that consistently reflects real, exploitable risk. For one of the world’s largest petrochemical companies, that shift meant rethinking how vulnerabilities were identified, prioritized, and remediated across a rapidly evolving application landscape. The transition ultimately led the organization to adopt CloudDefense.AI’s QINA platform as a foundational layer in its application security strategy.
Due to a strict NDA, the organization remains anonymous. The environment, challenges, and outcomes described here are real.
The Application Landscape: Large, Distributed, and Always Changing
The organization maintains a broad application ecosystem that includes internal platforms, customer- and partner-facing services, APIs, and business-critical systems supporting manufacturing, logistics, and enterprise operations.
Key characteristics of the environment include:
- Multiple development teams working across regions
- Applications built using different languages, frameworks, and architectures
- A mix of long-lived legacy code and rapidly evolving cloud-native services
- CI/CD pipelines optimized for speed, not manual security checkpoints
Security teams were expected to protect this landscape without slowing releases, disrupting pipelines, or overwhelming developers-an increasingly difficult balance to maintain as the application surface continued to grow.
What Wasn’t Working
The organization had security tooling in place, but over time it became clear that visibility did not equal clarity.
1. Alert Noise Outpaced Human Capacity
Static analysis and dependency scanning tools produced a steady stream of findings. Each additional repository or service increased reported issues-often without separating theoretical weaknesses from exploitable threats. Backlogs grew faster than teams could realistically remediate.
2. Severity Labels Failed to Reflect Real Risk
Many findings were flagged as “high” or “critical” based on generalized scoring models, even when vulnerable code wasn’t reachable in production paths, libraries weren’t used in exploitable ways, or existing controls already reduced impact. Truly dangerous issues could also get buried under the volume.
3. Manual Triage Became the Default
Because tools lacked application context, security teams repeatedly had to trace call paths, review execution flows, and reproduce scenarios to confirm practical exploitability-work that doesn’t scale and pulls senior security engineers into repetitive validation.
4. Developers Lacked Fix-Ready Context
Even when issues were valid, engineering teams often received generic reports without clear exploit paths, code-level context, or precise remediation guidance-leading to delays, back-and-forth, and reduced trust in security findings.
In short, four issues kept surfacing:
- Too much noise for teams to triage sustainably
- Severity ≠ real risk, making prioritization unreliable
- Manual validation bottlenecks due to lack of runtime-context signals
- Low developer actionability, slowing remediation and eroding trust
Why QINA Became Central to the Strategy
The organization needed a way to align findings with how applications actually behave in production.
QINA became compelling because it focuses on reachability and exploitability, not just detection. Instead of flagging everything that could be vulnerable in theory, it prioritizes what is realistically reachable and actionable.
Key factors behind adoption included:
- AI-driven reachability analysis to eliminate unreachable code paths
- Exploitability-focused prioritization aligned to real attack feasibility
- Consistently low false-positive rates, restoring confidence in findings
- Developer-ready remediation guidance, grounded in application context
- Seamless integration into existing CI/CD workflows
The goal was not broader scanning-it was better decisions.
Implementation and Rollout
QINA was introduced incrementally, starting with high-impact applications and gradually expanding across the organization’s development environment.
The rollout focused on three principles:
- Non-disruptive integration into existing CI/CD workflows
- Security insights tailored for developers, not just auditors
- Continuous measurement of decision quality and operational outcomes-not alert volume
Within weeks, both security and engineering teams began seeing measurable improvements in clarity and efficiency.
Measurable Impact
After adopting QINA across its application ecosystem, the organization observed the following outcomes:
- 98% accuracy in identifying actionable application risk, significantly reducing false positives
- Near-elimination of low-value findings, restoring developer trust in security signals
- Significant reduction in triage time, allowing security teams to focus on high-impact threats
- Faster remediation cycles, with developers acting on clear, contextual guidance
- Improved executive visibility, with risk trends clearly mapped over time
Most importantly, security became a predictable, measurable process rather than a reactive fire drill.
An Operational Shift Toward Risk-Driven Security
The most meaningful change was operational: security moved from tool output to evidence-backed risk.
1. Evidence Replaced Assumptions
Reachability and exploitability signals reduced long debates about whether an issue mattered. Findings were backed by execution context rather than generic severity labels-allowing teams to focus on decisions, not disputes.
2. Developer Trust Recovered
As alert quality improved, developer behavior changed:
- Findings were taken seriously
- Fixes were implemented faster
- Security discussions became collaborative rather than defensive
When developers trust the signal, security becomes part of the workflow.
3. Prioritization Became Consistent Across Teams
Instead of each team interpreting severity differently, risk assessment aligned around:
- Actual execution paths
- Realistic attack feasibility
- Business impact of compromise
This made remediation planning easier across a distributed organization.
4. Reporting Shifted From Volume to Impact
Leadership reporting evolved from counting findings to tracking:
- Risk decisioning accuracy and operational efficiency over time
- High-exposure areas and trends
- Improvement by application and team
This made security progress measurable, explainable, and defensible at the executive level.
Final Thoughts
For large enterprises operating in high-stakes industries like petrochemicals, application security must be accurate, scalable, and operationally realistic.By adopting CloudDefense.AI’s QINA platform, this global organization improved the accuracy of how it identifies and prioritizes actionable risk-reducing false positives and enabling faster remediation without increasing friction for development teams. The result was not just stronger security, but a more mature, evidence-driven AppSec program capable of scaling with the business.
