ShieldLabs is a platform for fraud detection and prevention, primarily aimed at digital products, helping them detect and stop account abuse and bot behavior before revenue is damaged.
Main Uses
ShieldLabs' core goal is to protect products from abuse. It can detect a variety of risky users, visitors, and bots, regardless of the disguise methods they use, thereby playing a role in the following areas:
- Protect revenue: catch duplicate trial signups, bonus abuse, and account sharing before they erode profits.
- Protect advertising budgets: identify risky traffic and fake signups so that acquisition spending goes to real users.
- Trust metrics: understand which users and traffic sources are trustworthy, so that growth decisions reflect reality.
- Reduce manual review: bring together visitor history, related users, and named risk signals so that reviews have clear evidence.
Main Capabilities
The platform integrates identification, risk signals, account abuse detection, and traffic quality analysis:
- Precise identification: identify returning visitors even if users clear cookies, use incognito mode, or change IP addresses; discover related users using different email addresses. The company says its identification accuracy rate reaches 99.9%.
- High-risk event detection: automatically identify related users out of the box, with no need to train fraud models; provide ready-made detection of automation, disguise, and deception, with named risk signals behind every result. The default detection threshold is 3 accounts per visitor, adjustable in the dashboard.
- Bot and AI traffic detection: detect bots, scripts, and browser automation, distinguishing useful bot and AI traffic from abusive behavior.
- Device fingerprinting: identify devices through fingerprinting, distinguish browsers, operating systems, and device types, and detect headless browsers and tampering behavior.
- IP and network intelligence: reveal the real IP address and geographic location behind disguises, and detect VPNs, proxies, and private relays.
- Traffic quality scoring and risk analysis: provide a score for all traffic and ready-made quality results by source, channel, referral, and UTM campaign.
- Risk scoring: score each visit from 0 to 100 and classify it as trusted, suspicious, or dangerous, while showing the signals behind the score.
How to Use
- Integration: the company says integration takes 5 minutes, with identification, detection, and scoring completed through a single API. Risk data is returned through API responses and webhooks, allowing selected data points to be integrated into business logic.
- Dashboard: start with the traffic quality overview, then investigate an individual user or visit; explore risk signals, related identities, visit history, and source attribution in the dashboard.
- Free trial: 5,000 free detections are provided, with no credit card required. The free tier allows you to integrate the product first and view detection results before choosing a paid plan. You can start using it for free on the ShieldLabs website.
Use cases
Suitable for digital products that need to prevent account abuse, bot behavior, and fraudulent traffic, for example:
- Prevent the same person from creating multiple accounts with different emails to claim the same benefit.
- Detect account sharing and account takeover.
- Identify anomalous login locations (impossible travel).
- Evaluate traffic quality for advertising and acquisition channels.
- Reduce manual review workload.
Detection scope and limitations
- ShieldLabs detects VPNs, proxies, Tor, Apple Private Relay, anti-detect browsers, browser automation, bots, AI agents, and risk signals such as operating system and time zone mismatches.
- Multi-account detection is achieved by correlating account activity with visitor, device, and network context, including activity across different emails; the result is a multi-account finding, not a report of linked registrations.
- Traffic risk assessment combines more than 300 signals from devices, browsers, operating systems, IP addresses, and networks. Each detected risk signal is returned by name, and the risk score summarizes that visit on a 0–100 scale, with the score remaining interpretable.
- The official claimed accuracy for risk signal detection is 99.9%.
- Regarding supported platforms, specific pricing, compliance certifications, data retention policies, and similar details, the materials do not currently provide them.
