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CloudDefense.AI Highlights Risk-Driven Application Security Outcomes in Global Petrochemical Environment Using QINA

QINA is built to help organizations focus on exploitable risk and give teams the context they need to remediate efficiently, especially in complex, high-stakes environments.”
— Abhi Arora, COO at CloudDefense.AI
PALO ALTO, CA, UNITED STATES, February 13, 2026 /EINPresswire.com/ -- A global petrochemical enterprise operating at significant scale has strengthened its application security operations by adopting a risk-driven approach powered by CloudDefense.AI’s QINA platform.

The organization reported 98% accuracy in risk determination, enabling teams to substantially reduce false positives and focus remediation on findings that are truly relevant in real application flows. The customer remains anonymous due to a strict NDA, but the outcomes reflect a shift many high-stakes industries are making as application ecosystems expand across legacy systems, cloud-native services, third-party integrations, and regulated operational workflows.

In environments where software supports supply chains, partner operations, and business-critical systems that cannot tolerate disruption, security teams often face a persistent challenge: high volumes of findings with limited clarity on what is actually exploitable. In this engagement, the organization’s existing tooling produced growing backlogs, severity labels that didn’t consistently map to real-world exposure, and heavy reliance on manual triage. Developers also lacked fix-ready context, which slowed remediation and gradually reduced trust in security signals.

By standardizing on QINA, the organization shifted away from “more scanning” and toward prioritization aligned with how applications behave in practice. QINA’s reachability analysis and exploitability-focused prioritization helped teams differentiate theoretical issues from actionable exposure, improving confidence in findings and reducing time spent validating noise. As the rollout expanded across high-impact applications and CI/CD workflows, teams reported clearer prioritization, faster remediation cycles for meaningful issues, reduced triage effort, and improved executive visibility into risk trends over time.

The organization’s experience reflects a broader operational shift in enterprise AppSec: moving from tool-driven outputs to evidence-backed decisioning that developers can act on and leaders can measure. With higher-confidence findings and better prioritization, security becomes less reactive and more predictable-an essential requirement for industries where uptime, safety, and regulatory alignment are non-negotiable.

If you want to take a closer look at how CloudDefense.AI fits into modern AppSec workflows, feel free to contact them connectwithus@clouddefense.ai or book a free demo now.

About CloudDefense.AI:

CloudDefense.AI, headquartered in Palo Alto, is a cutting-edge Cloud-Native Application Protection Platform (CNAPP) that provides end-to-end security for cloud infrastructures and applications. CloudDefense.AI integrates advanced technology and expertise, making it the ultimate solution for mitigating security risks from development to deployment.

Their state-of-the-art platform offers a full spectrum of security solutions, ensuring organizations can confidently protect their cloud environments. Covering every layer of security, CloudDefense.AI provides SAST, DAST, SCA, IaC Scanning, Advanced API Security, Container Security, CSPM, CWPP, CIEM, Kubernetes Security, and AI-SPM. Moreover, their exclusive CloudShield.AI technology guarantees continuous policy enforcement and proactive threat mitigation.

CloudDefense.AI enhances security with AI-driven remediation, attack path analysis, and automated risk assessment to reduce vulnerability noise and detect zero-day threats in real-time. This innovative approach boosts security efficiency, providing up to five times the value of traditional tools and establishing them as leaders in cloud security.

Emily Thompson
CloudDefense.AI
+1 650-555-0194
email us here
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