ShieldLabs

ShieldLabs is an identity intelligence and fraud prevention platform designed to help digital businesses identify risky visitors, detect abusive behavior, and improve the quality of their online traffic. The platform analyzes device, browser, network, and behavioral signals to recognize visitors even when they attempt to hide their identity through techniques such as VPNs, proxies, incognito sessions, rotated IP addresses, or anti-detect browsers. ShieldLabs turns these signals into persistent visitor identities, detection flags, and explainable risk scores that businesses can use to build their own fraud prevention rules.

One of the platform's core capabilities is visitor identification. ShieldLabs creates a persistent identifier using more than 100 device, browser, operating system, IP, and network signals collected when a visitor first interacts with a website. This allows businesses to recognize returning visitors even when cookies have been cleared or IP addresses have changed. Visitors, devices, and accounts can also be connected into a single identity graph, helping organizations understand relationships that may not be visible from individual sessions.

ShieldLabs provides detailed anonymity and network intelligence. The platform can identify signals associated with VPNs, proxies, Tor, privacy relays, datacenter IP addresses, suspicious IP reputations, IP mismatches, anti-detect browsers, operating system tampering, browser automation, incognito sessions, IP geolocation, and location spoofing. These signals can help businesses distinguish ordinary visitors from traffic exhibiting characteristics associated with anonymity, automation, or potentially abusive activity.

Another major feature is explainable risk scoring. ShieldLabs assigns a risk score from 0 to 100 and provides the signals responsible for that score instead of treating the result as an unexplained black box. For example, a visitor might receive a higher risk score because of an anti-detect browser combined with a proxy or timezone mismatch. Businesses can then decide how their own applications should respond to those signals, whether that means allowing access, requesting additional verification, limiting an action, or blocking a transaction.

The platform supports a broad range of fraud and abuse prevention use cases. These include multi-accounting, account sharing, account takeover, promotional and bonus abuse, free-trial abuse, referral fraud, payment fraud, ban evasion, Sybil attacks, loyalty fraud, paywall bypass, and advertising fraud. ShieldLabs can also identify returning visitors, helping businesses recognize trusted users and potentially provide a smoother experience without repeatedly applying unnecessary checks.

ShieldLabs includes pre-built behavioral patterns that correlate accounts, devices, and signals across a company's traffic history. These patterns can surface suspicious relationships involving account farms, multi-accounting, account sharing, account takeovers, and other forms of abuse. Results are assigned severity levels so teams can identify potentially problematic activity without having to build their own fraud detection model from scratch.