Compare Adaptive Security vs. DeepFake-o-meter

Adaptive Security is OpenAI’s investment for AI cyber threats. The company was founded in 2024 by serial entrepreneurs Brian Long and Andrew Jones. Adaptive has raised $50M+ from investors like OpenAI, a16z and executives at Google Cloud, Fidelity, Plaid, Shopify, and other leading companies. Adaptive protects customers from AI-powered cyber threats like deepfakes, vishing, smishing, and email spear phishing with its next-generation security awareness training and AI phishing simulation platform. With Adaptive, security teams can prepare employees for advanced threats with incredible, highly customized training content that is personalized for employee role and access levels, features open-source intelligence about their company, and includes amazing deepfakes of their own executives. Customers can measure the success of their training program over time with AI-powered phishing simulations. Hyper-realistic deepfake, voice, SMS, and email phishing tests assess risk levels across all threat vectors. Adaptive simulations are powered by an AI open-source intelligence engine that gives clients visibility into how their company's digital footprint can be leveraged by cybercriminals. Today, Adaptive’s customers include leading global organizations like Figma, The Dallas Mavericks, BMC Software, and Stone Point Capital. The company has a world class NPS score of 94, among the highest in cybersecurity.

An accessible platform that incorporates cutting-edge methods for detecting deepfakes is available for use. Users can upload a single video file from their computer at a time. Deepfakes, which are AI-generated fabricated media, can create misleading representations of individuals and their actions, posing significant risks when misused. The DeepFake-o-meter, created by the UB Media Forensics Lab, is an open-source tool designed to identify deepfake technologies created by third parties. It offers a user-friendly service that allows for the analysis of deepfake content using various advanced detection techniques, ensuring that results are delivered securely and privately. Additionally, it features an API framework that enables developers to integrate their deepfake detection algorithms and execute them on a remote server. Furthermore, it serves as a valuable resource for researchers in digital media forensics, providing a platform for assessing and benchmarking the effectiveness of different detection algorithms against one another. This multifaceted approach ultimately enhances the fight against the misuse of deepfake technology.