Fingerprint, a leader in device intelligence, has unveiled new Smart Signals and platform enhancements to combat the rising threat of malicious bots and AI agents in agentic commerce. As autonomous AI agents grow more sophisticated, enterprises face increasing challenges in distinguishing legitimate automated traffic from fraudulent activity, which now accounts for over half of internet traffic.
Fingerprint introduces four new Smart Signals to detect malicious bots and AI agents.
Over 50% of internet traffic is bots, with 30% classified as malicious.
New features protect against payment fraud in agentic commerce.
Bot Detection Smart Signal identifies legitimate vs. malicious automation.
Virtual Machine and Residential Proxy Detection enhance fraud prevention.
Request Filtering optimizes costs by excluding trusted AI agents.
With bots comprising over half of internet traffic, 30% of which are malicious, enterprises need robust solutions to safeguard operations. Fingerprint's new Smart Signals provide real-time risk indicators based on device behavior, environment, and configuration. These tools help businesses differentiate between beneficial automation, such as AI assistants, and malicious activities like payment fraud, ensuring seamless operations in agentic commerce. Gartner predicts fully autonomous AI agents by 2036, underscoring the urgency for advanced fraud detection.
The Bot Detection Smart Signal identifies dozens of bot and browser automation tools. By analyzing each API request, it classifies traffic as legitimate or malicious, ensuring only verified beneficial bots are trusted. This feature is critical for enterprises aiming to protect against fraud while supporting legitimate transactions in agentic commerce. "Bots and AI agents represent both the biggest current risk and the fastest-evolving threat landscape we've seen," said Dan Pinto, CEO and co-founder of Fingerprint.
Virtual machines are commonly used in automated fraud schemes, making their detection vital. The Virtual Machine Detection Smart Signal enhances Fingerprint’s ability to identify these environments, offering an additional layer of protection against sophisticated attack vectors. This capability strengthens enterprise defenses in the rapidly evolving landscape of agentic AI.
Residential proxies, which route traffic through real IP addresses, pose a significant challenge in fraud detection. Fingerprint’s Residential Proxy Detection Smart Signal identifies these proxies with confidence levels, enabling businesses to combat fraudsters masking their activities. This feature is crucial for maintaining security in agentic commerce environments.
Fingerprint’s Request Filtering functionality allows enterprises to exclude known legitimate AI agents used for web scraping or task automation from fingerprinting. This optimization reduces billing costs without compromising the ability to detect AI-driven fraud, providing a balanced approach to security and efficiency.
The introduction of Fingerprint’s Smart Signals marks a significant step forward in addressing the challenges of agentic AI and malicious bots. By offering real-time, intelligent detection capabilities, Fingerprint empowers enterprises to embrace AI-driven innovation while safeguarding against evolving fraud threats. These features are available immediately and integrate seamlessly with existing Fingerprint implementations.
Fingerprint, powered by the most accurate device fingerprinting technology, enables companies to reduce fraudulent transactions, strengthen account security, and improve customer lifetime value. Fingerprint processes 100+ signals from the browser, device and network to generate a stable and persistent unique VisitorID that can be used to understand visitor behavior. With a commitment to best-in-class data security and privacy, Fingerprint is proud to be SOC 2 Type II, GDPR and CCPA compliant. Fingerprint is trusted by over 6,000 companies worldwide, including 16% of the top 500 websites, to help catch sophisticated fraudsters and personalize experiences for trusted users.
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