Tool Overview:
brighterAI

About brighterAI

brighterAI provides image and video anonymization software that uses generative AI to protect personal identifiable information in visual data. The company's primary solutions include Precision Blur and Deep Natural Anonymization (DNAT), which focus on anonymizing faces, license plates, and full human bodies in video footage and images. These solutions help organizations comply with data protection regulations such as GDPR, CCPA, APPI, and PIPL while maintaining data utility for analytics and AI applications.

What does brighterAI do?

The platform's core functionality centers on three main anonymization capabilities: face anonymization, license plate anonymization, and full body anonymization. For face protection, the software offers Precision Blur for standard redaction and Deep Natural Anonymization for creating synthetic face overlays. The license plate anonymization works similarly, providing both blurring and synthetic replacement options. The full body anonymization feature extends privacy protection to include identifying features such as clothing, hairstyles, and tattoos.

brighterAI's technology processes data from various sources, including professional photography equipment, smartphones, action cameras, CCTV systems, and industrial sensors. The platform can handle different environmental conditions, operating effectively under various weather conditions, times of day, and lighting scenarios. The system operates as an on-premise solution, allowing organizations to maintain complete control over their data processing without requiring constant communication with external servers.

The platform uses Terraform deployment for automatic scaling across clusters, supporting spot instances on multiple platforms. This infrastructure allows the system to scale up or down based on processing demands, handling both steady low-volume processing and occasional high-volume requirements. The solution is cloud-agnostic, compatible with major providers including AWS, Google Cloud, Alibaba, and Red Hat OpenShift.

What makes brighterAI different?

A key differentiator of brighterAI's approach lies in its Deep Natural Anonymization Technology (DNAT). Unlike traditional blurring methods that can destroy contextual information, DNAT creates synthetic replacements for sensitive elements while preserving important attributes such as age, emotions, gaze, and ethnicity. This preservation of semantic information makes DNAT particularly valuable for organizations that need to use the anonymized data for machine learning applications or analytics.

The platform's on-premise deployment model provides enhanced security and compliance capabilities. By hosting the anonymization process within their own infrastructure, organizations maintain full control over sensitive data processing. This approach helps ensure compliance with stringent data protection regulations while allowing businesses to integrate the solution directly into their existing data management pipelines.

Use cases and industries

brighterAI serves several key industries with specific use cases. In the automotive sector, the technology supports data collection for autonomous driving development, annotation data for human-in-the-loop labeling, and vehicle and road data for mapping services. The platform processes dashcam footage for safe driving and insurance applications while maintaining GDPR compliance.

In healthcare settings, the software anonymizes sensitive medical video and image data to protect both patients and staff. The technology supports various healthcare applications, including user experience testing, digital therapeutics, education and research, and patient tracking. The solution aligns with healthcare-specific regulations such as HIPAA.

The platform also addresses needs in surveillance and security applications, particularly in manufacturing facilities, office spaces, and public areas. Here, the full body anonymization capabilities help organizations maintain security monitoring while protecting individuals' privacy by obscuring identifying features such as employee IDs, uniforms, and distinguishing physical characteristics.