Adaptive Security
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.
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Sumsub
Sumsub is a single verification platform that allows you to onboard more customers worldwide, speed up their access, reduce costs, and fight digital fraud. Sumsub combines effective verification flows with higher conversion rates worldwide through a powerful, all in one suite designed for a wide variety of needs: KYC/AML verification, KYB verifications, payment fraud prevention and face authentication.
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DeepFake Detector
Deepfake technology poses significant risks by enabling the creation of misleading videos and audio that can confuse audiences and spread false information. Our DeepFake Detector is designed to help you effectively identify and screen out these AI-generated media, ensuring that you can trust the content in critical contexts, such as news reporting and judicial matters. Recognizing the serious implications of deepfakes, we prioritize the need for genuine audio and video content. By utilizing our professional verification services, you can easily distinguish authentic media from misleading fakes. To begin the verification process, simply select a video or audio file for analysis, keeping in mind that files should ideally be a minimum of 8 seconds in duration and free from edits or special effects for optimal accuracy. Once you upload your chosen file, just hit the "detect deepfake" button to initiate the process, and you will receive an assessment indicating the likelihood of the media being a deepfake versus legitimate content. This empowers you to make informed decisions based on the authenticity of the media you are analyzing.
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FakeCatcher
Intel has developed the FakeCatcher deepfake detection technology, which evaluates the “blood flow” in video pixels to quickly assess the authenticity of a video in mere milliseconds. This system is seamlessly integrated into editing software widely used by content creators and broadcasters, allowing for effective detection of manipulated content during the editing process. Furthermore, it serves a critical role in screening user-generated content, ensuring that authenticity checks are part of the upload process. By providing a universally accessible platform for deepfake detection, it empowers individuals and organizations alike to verify the legitimacy of videos with ease. Deepfakes represent synthetic media that distort reality, presenting actors and actions that are fabricated. While many deep learning-based detection systems examine raw data to identify inconsistencies and flaws, FakeCatcher takes a different approach by searching for genuine indicators of authenticity within real footage, focusing on the minuscule evidence of human traits—such as the subtle variations in pixel color caused by blood circulation. When the heart pumps, the color of our veins shifts, creating the unique data that FakeCatcher utilizes to distinguish between real and manipulated videos. This innovative detection method signifies a significant leap forward in the fight against deepfake technology.
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