// OpenLane

OpenLane is an AI sandbox that combines a universe of synthetic checkpoint security screening data with AI tools to create new possibilities in threat detection.

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An Open Platform for Security Screening

OpenLane connects OEMs, researchers, and government personnel with the data and tools to deploy advanced and accurate screening systems.

Improve Data Access

Eliminate security and privacy friction with synthetic data generators.

Save Time

Eliminate manual annotation and virtualize data collection for faster iterations.

Ensure Safety

Increase threat model coverage to eliminate bias and ensure model safety.

Use Cases

Support a variety of use cases with a single platform.

“Having a source for computer-generated and annotated training images will significantly reduce the time and expense for fielding new machine learning models for more efficient screening and in response to new threats.”

U.S. Department of Homeland Security | Science and Technology Directorate (S&T) Silicon Valley Innovation Program (SVIP) Press Release on Cignal Award

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Resources

Explore how Cignal helps advance innovative AI technologies for public safety.

Tutorial

Augmenting datasets with Drag GAN

In this video, Kenny demonstrates how synthetically-generated millimeter wave (MMW) images can be perturbed for novel poses and angles using DragGAN.‍

Tutorial

Using open source models to auto segment security datasets

In this demonstration, Kenneth Brown shows how to use SAM2 to auto-annotate a millimeter wave security image dataset.

Energy

Cignal's AI Sandbox as Your STIX/TAXII Testing Ground

Cignal's AI sandbox enables energy utilities to securely test and validate AI-powered threat detection models against STIX/TAXII data, strengthening NERC CIP compliance and enhancing cyber resilience.