Every month, we retest your applications against the latest commercial and self-hosted LLMs. When a major new model drops, you get a step-change alert and a retest within two business days — so you’re never caught finding out from an attacker first. Tested with commercial models (e.g., Claude, GPT) or fully self-hosted for sensitive code.
Continuous AI-Driven Testing
Living Attack Surface Heatmap
See precisely where adversarial AI focuses on your application: which entry points it probes, how deep it gets, and where it comes closest to a breakthrough. The heatmap updates every iteration, so you can watch your exposure shift as model capabilities improve — and it’s built to be read by your board in one slide.
Architecture-Level Findings
Source and architecture access turns a surface-level check into a real audit. The deepest issues — shared credentials, flat networks, missing trust boundaries — are invisible to black-box testing and to any tool that only reads code. Our threat-modeling phase surfaces these before a single line of code is scanned.
CI/CD Regression Coverage
Every confirmed finding seeds an automated test, and variant analysis hunts for the same bug pattern’s siblings across your codebase — so a fixed issue can’t quietly resurface, and your in-house test suite grows stronger every month. Can run entirely on a local model to keep CI/CD data off the public internet.
Testing Challenges
Annual pentests provide a trusted baseline, but the gap between one test and the next now spans multiple AI capability jumps — and each one can surface attack chains the previous generation of models couldn’t find.
1
Fixed Capability
The pentester you hire today can’t benefit from next month’s model. A test scoped to one point in time gives you a snapshot, not ongoing assurance.
2
Slow Feedback Loops
Waiting six months to see pentest results means your team is rehearsing incident response only once or twice a year — not often enough to build real muscle memory.
3
Black-Box Blind Spots
Testing from the outside in misses architectural risks — flat networks, shared credentials, missing segmentation — that are invisible until someone reviews source and architecture directly.
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