Stytch Unveils Advanced Authentication Tech with Enhanced Fraud and Bot Protection for Developers -- ADTmag

Stytch Unveils Advanced Authentication Tech with Enhanced Fraud and Bot Protection for Developers

By John K. Waters
September 20, 2024

Identity platform provider Stytch has launched new device fingerprinting capabilities designed to provide enhanced fraud and bot protection for developers. The upgrade incorporates advanced AI functionality, offering an easy-to-integrate solution for boosting security within authentication flows, the company said. Stytch’s technology aims to provide seamless and highly accurate bot detection, reducing user friction and enhancing security without visible interventions.

Stytch's updated device fingerprinting leverages supervised machine learning to create a persistent identifier for every visitor, which the company says offers a comprehensive defense against zero-day threats. The platform’s latest features combine the company's proprietary deterministic approach to security with AI’s real-time, dynamic insights. Stytch claims its new system achieves 99.99% accuracy in detecting fraudulent activity and bot traffic, significantly reducing reliance on traditional tools like reCAPTCHA or Web Application Firewalls.

Unlike other methods, Stytch’s fingerprinting solution remains undetectable to users while resisting reverse-engineering and spoofing attempts, even in scenarios involving incognito modes, VPNs, or TOR nodes. The technology assesses deep hardware signals, network TLS profiles, and browser markers to create an accurate device identifier, adapting to evolving threats.

"Authentication is evolving to counter more sophisticated, often AI-generated threats," said Reed McGinley-Stempel, CEO and Co-founder of Stytch, in a statement. "Our device fingerprinting solution defines what the future of identity management should look like – providing developers with powerful tools to balance security and user experience."

Stytch's new features include intelligent rate limiting, which uses predictive analysis to detect unusual traffic patterns, and machine-learning-driven device detection, which identifies new device types and assesses their risk. Additionally, a configurable rules engine allows developers to customize security responses, making it easier to balance stringent security with user flexibility.

About the Author

John K. Waters is the editor in chief of a number of Converge360.com sites, with a focus on high-end development, AI and future tech. He's been writing about cutting-edge technologies and culture of Silicon Valley for more than two decades, and he's written more than a dozen books. He also co-scripted the documentary film Silicon Valley: A 100 Year Renaissance, which aired on PBS. He can be reached at [email protected].