Qwiet AI

A single analysis covers source code, open source libraries, infrastructure as code, container images, and hardcoded secrets. Consolidation is the selling point here: one onboarding, one findings list, one policy language instead of four vendor portals with separate logins.

Qwiet AI traces whether untrusted input can reach a vulnerable function, then weights findings that attackers already exploit in the wild. The practical effect is a shorter queue at the top of the list, which is where most security teams spend their morning. Unreachable findings stay available for audits without crowding the view.

Autofix generates a code change and a unit test for a finding, and the patch appears as a normal pull request. You review it like any teammate's work, so the model never commits on its own. The vendor reports a 95% cut in remediation time, a claim worth testing on your own repositories before you plan headcount around it.

Instead of matching text patterns, Qwiet models variables, calls, and data flow across files and services. That model is what lets the scanner follow a value from an HTTP handler into a database call, and it's also how the tool spots logic that only looks safe in one file.

The scanner runs inside Harness pipelines and also connects to GitHub Actions, GitLab CI, Jenkins, Azure DevOps, and a command line client. Results land as build checks and as tickets in Jira or ServiceNow. Most teams keep their existing CI and add Qwiet as one more step.

A web console groups findings by application, severity, and reachability, and supports SARIF export for other tools. Policy rules can fail a build on a severity threshold or on a specific category, which is how the tool turns into a release gate.