Questions we hear from teams evaluating SourceScan.ai

A pen test probes the running application from the outside for a fixed period. It is valuable, but it only finds what is reachable and exercised during the engagement. SourceScan.ai reads the source itself, so it finds issues in code paths a tester never reached, and it runs continuously instead of once a year. Many teams use both: SourceScan.ai to find and fix, a pen test to confirm.

Traditional SAST matches code against rule patterns and cannot tell whether a flagged line is actually exploitable, which is why its output is so often ignored. SourceScan.ai analyzes code in context, following how data actually flows, and every finding receives an expert verdict before it becomes work. The result is a short list of real issues with owners, not a long list of alerts.

Your own security team can use the platform directly. If you do not have one, our partner cybersecurity analysts perform the deep analysis and verdicts, and your developers and decision makers work with the results in the same dashboard.

Analysis is driven by understanding code rather than by per-language rule packs, so mixed and unusual stacks are fine. Current engagements include PHP, .NET and VB, Java, JavaScript and TypeScript, and Python codebases, including very old ones.

In our cloud, repositories are cloned into an isolated tenant and analyzed there with your own AI credentials. If that is not acceptable, the same platform deploys on your infrastructure and nothing leaves your network boundary.

Large ones. The advanced scan shards the repository across parallel workers and deduplicates findings across shards and across scans. You always see the estimated scope and cost before starting, and you can begin with focus areas on the highest-risk modules.

Request a demo and we will walk through the workflow on one of your repositories. Onboarding is a GitHub connection, a quick inventory and a first scan, usually in the same session.