IP Trust Score is an attribute returned in Real Contact API and Decision Signals API. It’s a value from 0 to 100 that indicates how legitimate an online interaction looks based on the IP address behind it.
What is an IP Trust Score? #
An IP Trust Score (0-100) indicates the legitimacy of an interaction based on the user’s IP address. A higher score points to a stronger likelihood that the interaction came from a genuine source. A lower score points to potential risk, such as anonymizing services, IP sharing, or suspicious activity patterns.
An IP (Internet Protocol) address is the unique identifier assigned to a device on a network. When someone fills out your web form or opens an account, the IP tied to that session is the closest thing you have to where the interaction came from. Our contact data verification indicates whether an identity is real and reachable, while IP Trust Score answers a different question: Does this specific interaction look like it actually came from that person?
How do I use IP Trust Scores? #
Include an IP address within your request and the score will come back in response. The IP should be the one captured at the moment of the interaction, such as a web form submission, signup, or checkout. In Decision Signals API, the IP signals feed the Identity Score and are also returned individually so you can build rules against them.
If you don’t send an IP address, the rest of your verification runs as usual, and IP attributes return NULL in the response.
How do I interpret an IP Trust Score? #
Read the score as a trust threshold you set, not a pass or fail verdict. The model is calibrating into two tiers:
| Score | Interpretation |
| >30 | Higher trust. Strong signals that the interaction is legitimate. |
| 30 or below | Lower trust. Signals of potential risk such as anonymizing services, high device sharing, or suspicious activity patterns. |
Please note: These tiers are a starting point. Your ideal threshold will shift based on your industry, lead source, and the validations layers you already run. We recommend benchmarking the score against your own outcomes before you set a cutoff, or schedule a call with us to talk through benchmarks for your use case.
How does IP Trust Score interact with IP Distance-to-Address? #
These two signals are most useful together. IP Distance-to-Address is the approximate distance in miles between the IP’s location and the address on record. A short distance on its own doesn’t prove the person is nearby, since a VPN exit node can sit close to the address they gave you. That’s why distance is best assessed with the IP Trust Score.
| IP Trust Score | IP Distance-to-Address | Interpretation | Recommendation |
| <=30 | >=200 miles | Low trust. Strong risk signals with significant location mismatch. | Do not buy or contact these leads. |
| <=30 | <200 miles | Low trust. Risk signals present, though location is reasonably aligned. | Treat as lower priority. |
| >30 | >=200 miles | Moderate trust. Trust signals are strong, but location mismatch introduces uncertainty. | Proceed outreach with caution. |
| >30 | <200 miles | High trust. Consistent behavior with strong location alignment. | Prioritize these leads as per the contact grades. |
These combinations are a starting point, not a rule book. Validate them against your historical outcomes before you wire them into automated decisions.
Why are IP Trust Scores important? #
Contact data can check out completely and still come from a submission you’d never want in your pipeline. A bot cycling through stolen identities can submit a valid phone number, a deliverable email, and a name that aligns with both. The person on the other end would tell you they never inquired.
Verification confirms the identity is real. IP intelligence helps confirm the real person was the one behind the interaction. Including both gives you a fuller picture than either can give you alone.
The score also works without the false positives that come from creating blanket rules. Blocking every VPN or proxy IP penalizes plenty of legitimate people who use one for work, travel, or privacy, and misses the traffic that actually matters like bad actors using residential proxies that look like ordinary home connections. A model trained on real-world outcomes weighs the type and behavior of a connection instead of flagging a whole category.
Some relevant use cases include:
- Lead Verification and Prioritization: Filter out submissions that look automated or misrepresented before you pay for or dial them.
- Signup and Onboarding Workflows: Clean good users without adding friction, and route questionable accounts for further review.
- Assessing Risk and Preventing Fraud: Add an interaction-level signal of the identity checks that inform your rules engine.
Does IP Trust Score have additional costs? #
No. IP intelligence (including IP Trust Score) is included in Real Contact API 2.0 at the same per-query price as version 1.1, and there is no additional charge for the score. It’s also included in the standard price-per-query for Decision Signals API.
How is the data sourced, and how accurate is it? #
IP Trust Score comes from a model we built and trained on real-world outcomes, rather than from a static rule set. It weighs three kinds of input:
- Geolocation data tied to the IP, built from registry records, Internet Service Provider (ISP) and network-infrastructure mapping, and active network measurements.
- Metadata like connection type, ISP, Autonomous System Number (ASN), domain, and organization. An ASN identifies a group of networks under a common routing policy, roughly the way a carrier identifies a phone network.
- Network and behavioral signals, including how widely the IP is shared and device and usage counts over time.
We use a model rather than the rules because IP metadata has enormous variety and changes constantly. There are countless ASNs, ISPs, and anonymization services in circulation. A rule set that keeps up with this volatility would either lag behind or blanket block trustworthy IP addresses. A model handles those combinations more nimbly, and lets you set the threshold that fits your risk tolerance.
Where can I learn more? #
Reach out to your account executive or email support@trestleiq.com and we’ll help you read the score against your own data, and set thresholds that fit your risk tolerance.