PreCogIQ predicts and classifies domains to uncover abuse patterns before they’re part of an attack.
Powered by adaptive AI, the system constantly evolves, integrating fresh data and patterns to help enterprise security teams act early,
reduce noise, and stop emerging threats.
Safeguard your users, brand, and infrastructure with an early warning system built for today’s threat landscape.
We continuously gather and process large volumes of global domain activity and apply AI, heuristics, and historical data to uncover patterns, naming structures, and behavioral signals that may indicate malicious intent. Our models analyze this information in context to surface early indicators of abuse.
Our AI models classify domains based on patterns associated with abuse. This classification includes deeper inspection of subdomains and structural traits often used in impersonation, deception or evasion.
You can provide brand terms or other high-interest keywords to augment our detection engine. These inputs help surface suspicious assets that mimic brands, target specific sectors, or align with active threat themes, supporting phishing defense, brand protection, and tailored threat monitoring.
We deliver threat alerts in near real-time — typically within 1–2 minutes of classification — through multiple channels: APIs, RPZ feeds, etc. This flexibilty ensures seamless integration into your existing workflows, enabling faster response and more informed decision-making at operational speed.
PreCogIQ empowers your security team with early, actionable intelligence on domains likely to be weaponized for campaigns and attacks. By detecting malicious intent at the domain level as early as registration or first observation, PreCogIQ enables earlier detection and disruption of domains tied to:
Unlike traditional feeds that rely on retrospective evidence like payload delivery or confirmed abuse, PreCogIQ identifies threats based on behavioral patterns, lexical structure, infrastructure associations, and risk-linked metadata to deliver a critical time advantage for defenders.
Key Benefits:
PreCogIQ vs. Them:
| Capability | Traditional Threat Feeds | PreCogIQ |
|---|---|---|
| Detection Timing | After threat activation | Before weaponization |
| Indicators Used | Blacklists, payloads | AI pattern recognition, past abuse |
| Use Case Fit | Incident response | Prevention, threat hunting, SOC enrichment |
| Integration Standard | APIs & formats | Same, with near real-time alerting |
| Freshness | Hours to days | Minutes after detection |
| Subdomain detection | Passive DNS | Robust using different techniques |
PreCogIQ is a joint initiative between two long-standing leaders in threat intelligence. Malware Patrol and CyberTOOLBELT®. This partnership brings together decades of expertise, rich historical data, and a shared commitment to delivering high-impact, real-time intelligence across the global threat landscape.
Founded in 2005, Malware Patrol is a trusted provider of real-time cyber threat intelligence used by enterprises, service providers, and security teams worldwide. With a deep, historically rich dataset and broad visibility across threat types, from phishing and malware to command-and-control infrastructure, we help customers detect, correlate, and stay ahead of evolving threats.
CyberTOOLBELT was founded in 2011 after being split off from iThreat Cyber Group. Its inteligence platform was introduced in 2013 it is and has been used by many Fortune 100 companies and government/law enforcement agencies. CyberTOOLBELT® has many terabytes of abuse data upon which to draw from. Thuier platform has many investigative and monitoring tools.