Validating AI for Automated Code Vulnerability Detection
A bank wanted to enhance software quality assurance by using AI to automatically detect code vulnerabilities, benchmark security coverage, and reduce manual review time - improving release speed and reliability across development teams.
Outcomes
5x
Faster Security Issue Resolution
70%
MTTD Reduced
4 week
End to End Proof of Concept
0%
Production Data Exposured
Technology Vendors Suited to Evaluation
Business Problem
The bank’s software development process was slowed by manual security reviews and inconsistent vulnerability detection. Developers spent significant time finding and fixing issues, while undetected flaws left applications exposed to potential breaches and compliance failures.
The bank needed an automated, reliable way to identify code vulnerabilities early in the development cycle to improve speed, security, and regulatory alignment.
Challenges
- Manual inefficiencies: Developers spend excessive time identifying and fixing security flaws.
- Security gaps: Unidentified vulnerabilities increase the risk of data breaches.
- Compliance risks: Failure to meet security standards results in regulatory fines.
From Idea to Evidence with NayaOne
NayaOne enabled the bank to validate AI-powered code vulnerability detection tools in a secure, controlled environment that replicated production pipelines without risk.
- Sandboxed CI/CD Pipeline: Snyk and SonarQube were deployed within an isolated workspace, using synthetic and open-source sample code containing known vulnerabilities. This allowed safe, repeatable scans of financial applications without touching live systems.
- Automated Testing: Each vendor’s tool ran full vulnerability scans across the same codebase, measuring detection accuracy, false positive rates, and remediation guidance.
- Performance Benchmarking: The sandbox supported side-by-side comparison of tools for speed, depth, and ease of integration into existing DevSecOps workflows.
Outcome: Within four weeks, the bank identified the most efficient and accurate solution for automated QA, accelerating secure software delivery and freeing development teams from repetitive manual testing.
Impact Metrics
PoC Timeline Reduction
4 weeks with NayaOne vs 12 – 18 months traditionally
Time Saved in Vendor Evaluation
1+ year
Decision Quality
The bank gained hard evidence on detection accuracy, speed, and integration fit - enabling a data-driven vendor choice and faster approval across risk and procurement.
KPIs
- Vulnerability Detection Rate (%): Percentage of known issues accurately identified during automated scans.
- False Positive Rate (%): Proportion of non-issues incorrectly flagged as vulnerabilities.
- Scan Duration (minutes): Average time taken per codebase to complete a full vulnerability scan.
- Integration Time (days): Time required to connect each tool to the sandboxed CI/CD pipeline.
- Remediation Efficiency (%): Improvement in average time for developers to fix vulnerabilities post-scan.
- Developer Productivity Gain (%): Reduction in manual testing effort after automation.
- Compliance Alignment (%): Conformance with internal and regulatory security standards (e.g. ISO 27001, PCI DSS).
Validate Automated Code Security Tools Before Deployment
Use NayaOne’s secure sandbox to test AI-driven vulnerability detection tools side by side – measuring accuracy, integration ease, and performance without touching production systems.
