Organisations should prefer proximity signals that cannot be reverse-engineered into exact coordinates and are scoped per customer account. That reduces privacy risk and limits cross-customer correlation. The goal is to detect suspicious co-location without exposing unnecessary location detail or creating a reusable location identifier that could be misused beyond the original fraud-control purpose.
Privacy and fraud prevention are competing design goals, not a binary choice
Device proximity signals can help spot account sharing, collusion, mule activity, or coordinated abuse, but the same signals can also reveal sensitive patterns about where people are and who they are near. That makes the privacy question inseparable from the fraud question. The practical aim is to detect suspicious co-location or repeated device association without turning proximity data into a durable location trail or a cross-account tracking mechanism. For teams building this capability, the real decision is not whether to use proximity at all, but how narrowly to define its purpose, retention, and scope. The GDPR’s proportionality and data minimisation principles are a useful reference point here, even when local law differs. In practice, many security teams discover the privacy failure only after the fraud control has already been repurposed for broader analytics.
How proximity signals should be shaped for fraud use
Proximity signals work best when they are treated as coarse risk indicators rather than identity-grade location evidence. A useful signal can show that two devices were plausibly near one another within a defined window, or that a device repeatedly appears in an abnormal cluster, without exposing exact latitude and longitude. That distinction matters because exact location data is far easier to misuse, correlate, or subpoena than a purpose-built proximity token.
Organisations should also scope the signal to the account or relationship being assessed. If the same proximity artefact can be reused across customers, it can become a hidden identifier that links otherwise separate users. That creates both privacy exposure and fraud-model contamination. The strongest designs therefore keep the signal narrow, contextual, and difficult to reverse engineer into a reusable behavioural fingerprint.
- Use the minimum precision needed to support the fraud rule.
- Keep proximity evidence separate from general analytics datasets.
- Limit retention to the shortest period that still supports review and dispute handling.
- Restrict access to staff who need the signal for fraud operations or investigations.
Where teams integrate proximity into broader risk scoring, they should make sure the surrounding model does not silently turn a narrow signal into a de facto tracking system. NIST SP 800-53 Rev. 5 is a relevant control reference because it ties privacy-aware handling to access control, monitoring, and data minimisation choices. The guidance breaks down when organisations assume that pseudonymised or aggregated proximity data is automatically safe, because correlation at scale can still re-identify behaviour.
Where privacy safeguards create trade-offs, and where they do not
Tighter privacy controls often reduce the richness of the fraud signal, so organisations must balance investigative value against unnecessary exposure. The trade-off is real: if a signal is too coarse, false negatives rise; if it is too precise, the organisation may collect more personal data than the fraud use case justifies. There is not complete consensus on the ideal precision threshold, because it depends on the fraud pattern, the jurisdiction, and the organisation’s tolerance for investigation friction.
Another edge case is consent or notice. In some fraud contexts, disclosure may be required or expected, but a notice alone does not solve the underlying proportionality problem. Teams should not treat user consent as a substitute for minimisation, especially when the signal could reveal habits, routines, or association patterns beyond fraud prevention.
Proximity signals also become more sensitive when combined with device fingerprinting, IP intelligence, or behavioural biometrics. Each layer can be useful on its own, but together they can create a more persistent profile than any single team intended. The user-value boundary should therefore be tested at the design stage, not after deployment. In practice, the most common failure is not a dramatic privacy breach, but gradual expansion of a fraud signal into a broader surveillance capability.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
NIST CSF 2.0 and CIS Controls v8 set the technical controls, while EU AI Act define the regulatory obligations.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST CSF 2.0 | GV.RM-03 — Legal and Regulatory Requirements | Privacy-friction trade-offs require governance aligned to legal obligations. |
| ID.IM-01 — Improvement Identification | Fraud controls need periodic reassessment as signal use expands or drifts. | |
| Recommendation — Align proximity-signal design with privacy obligations and risk acceptance criteria. Review whether proximity signals still match the fraud purpose after deployment. | ||
| CIS Controls v8 | 3.4 — Access Management | Scoped proximity data should be restricted to authorised fraud workflows. |
| 3.1 — Data Management | The question centers on minimising sensitive location detail and retention. | |
| Recommendation — Restrict proximity-signal access to the minimum fraud-investigation users. Minimise retention and sharing of proximity data to reduce privacy exposure. | ||
| EU AI Act | Art. 9 — Risk Management System | If proximity feeds automated fraud decisions, governance should control misuse and drift. |
| Recommendation — Document and test fraud-model risk controls when proximity signals affect decisions. | ||
Practitioner Guidance
What to prioritise: Define the fraud question first, then choose the least revealing proximity measure that can answer it. If the control needs exact coordinates to work, the design is probably too invasive for the stated purpose.
What to verify: Check whether the signal can be linked across customers, products, or time in a way that creates a persistent identifier. Also verify that retention, access, and downstream sharing are all constrained to the fraud workflow rather than the wider data stack.
Decision rule: If a proximity signal improves detection mainly because it is highly precise or widely reusable, treat that as a privacy warning, not a success criterion. If the same outcome can be achieved with coarser evidence and similar review quality, prefer the coarser design.
Practitioner takeaway: The safest fraud design is usually the one that preserves enough signal to support a decision while removing as much location specificity and cross-user linkage as possible.
Related resources from NHI Mgmt Group
- How do organisations balance fraud prevention and user experience in identity flows?
- How can teams balance privacy expectations with fraud prevention controls?
- How should organisations design KYB onboarding to balance compliance, fraud prevention, and conversion rates?
- How should customer service teams use identity risk signals to balance fast resolution with fraud prevention?
Deepen Your Knowledge
Reviewed and updated by the NHIMG editorial team on September 7, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org
