| Geospatial IP Intelligence | Regular IP Geolocation | |
|---|---|---|
| Geolocation precision | ~1-10 miles (10x more precise) | ~10-100+ miles |
| Number of records | 673M+ unique location records for individual IPs | 45M+ summarized records for CIDR ranges (typically /24) |
| Postal code match rate | 95% (11× fewer unknowns) | 45% |
| Location identification | Precise, based on large aggregated device datasets | Estimated, based on non-statistically significant pings |
| Time awareness | Yes (Dynamic) | No (Static) |
Traditional IP Geolocation does not provide a precise enough location for the threat actor-associated IP addresses so that you can take action.
| Where is the threat actor-associated IP located? | |
|---|---|
| With IP Geolocation | Probably in this country, unless they used a VPN… |
| With Geospatial IP Intelligence | Narrowed down to this specific area of the city. Now you know where to start looking. |
Non-precise location data makes it difficult to assess risk in non-standard situations.
| From where is the user connecting? | |
|---|---|
| With IP Geolocation | Probably from California, USA, which is fine… |
| With Geospatial IP Intelligence | Certainly from Tijuana, Mexico, and they’ve never been there before. |
Users keep evading household policies because traditional IP Geolocation generates too many false positives when attempting to use it for more precise geolocation.
| Is it the same household? | |
|---|---|
| With IP Geolocation | Both sessions originate from Paris, France. Both resolve to the same ISP. Concurrent streams allowed. |
| With Geospatial IP Intelligence | One session is in central Paris, the other is 30 miles away in Meaux. Both were active within minutes. Those are different households. Let’s trigger policy enforcement. |
Attacker infrastructure ends up being over- or undergrouped because traditional IP Geolocation doesn’t offer enough precision.
| Should we cluster those IP addresses? | |
|---|---|
| With IP Geolocation | These 20 IPs are from the same ASN, same country, used in the same campaign window. That’s a cluster. |
| With Geospatial IP Intelligence | 14 out of the 20 IPs are from one data center, but four others resolve to different data centers and two more are in another country, even though close to the border. Six false positives avoided. |
Security and fraud teams rely on IP location every day, yet most geolocation data was never designed to reflect how infrastructure actually behaves, how it changes, or where it actually is.
Geospatial IP intelligence introduces a more complete and reliable foundation for decision-making:
We examined two real-world cases where traditional IP geolocation fails at a fundamental level, revealing a consistent pattern across providers and methodologies:
















Geospatial IP Intelligence is a whole new form of IP geolocation built on aggregated device datasets with validated latitude/longitude readings and multi-signal device-level correlation and averaging.
By continuously analyzing observed device locations associated with IPs, it delivers high-precision mapping, recency indicators, and drift detection.
This transforms IP location into a dynamic, evidence-based signal that supports more accurate fraud prevention, stronger KYC controls, higher-fidelity threat intelligence, and reliable attack surface monitoring.
Traditional IP geolocation is based on using already reported IP range registration data and attempting to improve it with pinging and tracerouting from multiple locations. This approach can lead to significant inaccuracies and approximations, as the resulting information often does not represent where an IP address is actually being used. It provides a static picture that doesn’t account for the possible drift of the IP address with the granularity that is enough for country-level identification, but not for city-level or more precise operations. The industry has mostly accepted these limitations, but it doesn’t mean it should still be like that.
Geospatial IP Intelligence overcomes these limitations by relying on aggregated device datasets with a much higher number of database entries that are also time-aware. Instead of inferring location from network infrastructure alone, it derives geolocation from real-world device observations associated with IP addresses. This enables higher precision, broader coverage, and time-aware tracking of location stability and jurisdictional drift — delivering a dynamic, real-life, observations-based geospatial IP data rather than a static registry-based estimate.
Geospatial IP Intelligence is a far more precise version of regular IP geolocation data, but it’s also more than just that. The key benefits of using geospatial IP Intelligence can be summarized like this:
To start using Geospatial IP Intelligence or if you have any other questions, contact us, and our team will get back to you.
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