technology
Engineering
representative

DevSecOps Pipeline Integration for a Cloud- Native Company

A cloud-native company with 200 engineers operates a CI/CD pipeline deploying containerized services to Kubernetes clusters across three cloud regions. They practice \"shift-left\" security but have found that SAST/DAST scanners (Semgrep, ZAP) produce too many false positives and miss logic-level vulnerabilities. They want to add intelligent, context-aware penetration testing as a gate in their deployment pipeline.

Runs onVedha

false positive rates drop: 40%false positive rates drop

How the work runs

The pressure that made this worth automating, the steps the system runs, and what came out the other side.

Pressure & Trigger Points

Traditional DAST scanners flag hundreds of \"potential\" issues per scan, overwhelming developers with noise and causing alert fatigue.
Logic vulnerabilities such as privilege escalation through a sequence of valid API calls, or race conditions in payment processing cannot be detected by signature-based scanners.
The security team wants pentest-quality findings without blocking the deployment pipeline for days.

The run · 5 operational steps

Click any step to inspect telemetry signals, model reasoning, and governance gates.

scroll →

API-Driven Flow Triggering

The CI/CD pipeline (GitHub Actions) triggers a VEDHA Flow via a GraphQL mutation after a successful staging deployment. The mutation payload includes the target URL, an authentication token for the staging environment, and a goal tailored to the changed services: \

Input Signal:

Real-time operational telemetry & queue

Reasoning Pattern:

MCP grounded vector inference

Governance Gate:

Policy constrained with audit write-back

Verified Business Outcomes

A race condition in the payment processing endpoint is caught during a pipeline run, preventing a bug that would have allowed double-charging customers from reaching production.
False positive rates drop from ~40% (traditional DAST) to under 5%, because VEDHA's agents reason about whether a finding is actually exploitable rather than simply pattern-matching.
Developers regain trust in security tooling and start proactively reviewing pentest findings instead of ignoring scanner noise.

Capabilities this relies on

workflow orchestration
generative design
root cause reasoning
risk scoring
evidence audit trail
human approval
in boundary deployment

Related catalog agents