Intelligence written by the people who build it

FraudGuard Intelligence is the editorial home of the FraudGuard.io engineering team. We build and operate the honeypot collection, enrichment, correlation, and decision systems discussed throughout this site.

Our goal is practical: help security, fraud, abuse, and platform teams decide whether internet traffic should be allowed, challenged, investigated, or blocked—and show the evidence behind that decision.

What we publish

  • First-party research based on traffic observed by FraudGuard-operated sensors.
  • Engineering notes about ACE, our Attack Correlation Engine, and the systems that turn raw events into explainable decisions.
  • Decision guides for IP reputation, proxy risk, network abuse, authentication security, and defensive automation.
  • Product guidance that explains when a FraudGuard API or deployment model is—and is not—the right fit.
  • Fair comparisons that identify where another product is stronger instead of forcing every buyer into the same answer.

Our editorial standard

We distinguish direct observations from inference. We prefer evidence and limitations over vague claims. Product availability and pricing are reviewed during substantial article updates, and competitor facts are linked to public vendor sources whenever relevant.

Security decisions are contextual. A VPN, hosting provider, country, or ASN label is not proof of malicious behavior by itself. Our guidance consistently favors evidence-led, tiered responses—allow, challenge, monitor, rate-limit, or block—over blanket rules that create unnecessary false positives.

FraudGuard.io has been operating for more than a decade. The blog reflects lessons learned from building the product and observing hostile internet traffic over that time; it is not a substitute for your own incident-response process or risk policy.

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