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15/08/2026 6:15 pm
TL;DR: Device intelligence has shifted from a niche signal source to a core fraud-control layer, with Fingerprint saying it now analyzes 100+ browser, device, and network signals and identified more than 4 billion unique browsers and mobile devices in 2024. The identity boundary is widening as fraud teams must distinguish humans, bots, and AI agents without degrading user experience.
NHIMG editorial — based on content published by Fingerprint: device intelligence, Smart Signals, and AI agent detection
By the numbers:
- Fingerprint says it identified more than 4 billion unique browsers and mobile devices in 2024 across 250 countries and territories.
- The platform analyses 100+ browser, device, and network signals to generate persistent identifiers and risk signals.
- Fingerprint says its system includes more than 20 Smart Signals for real-time fraud detection.
Questions worth separating out
Q: How should security teams use device intelligence in fraud prevention without overblocking users?
A: Use device intelligence as one input to risk-based decisions, not as a sole proof of identity.
Q: Why do AI agents complicate customer identity and fraud controls?
A: AI agents complicate customer identity because they can carry out actions that look legitimate while obscuring the actual decision-maker.
Q: What do security teams get wrong about device fingerprinting?
A: They often treat it as a definitive identity mechanism rather than a probabilistic signal.
Practitioner guidance
- Map device signals to identity decisions Document where browser, device, and network signals influence login, payment, step-up, or transaction approval so the control boundary is explicit.
- Create policy for sanctioned automation Define which AI agents, bots, and service automations are allowed, what actions they can perform, and how they are tagged in telemetry.
- Correlate evasion indicators before blocking Combine proxy, tampering, virtual machine, emulator, and developer-tools indicators into one decision path rather than reacting to each signal in isolation.
What's in the full article
Fingerprint's full article covers the operational detail this post intentionally leaves for the source:
- How Fingerprint describes its Smart Signals across bot detection, tampering detection, proxy intelligence, and emulator checks.
- The specific examples the vendor uses to separate human, bot, and AI agent behaviour in real time.
- Background on the company’s decade-long device intelligence roadmap and customer advisory model.
- The vendor’s own explanation of how its product aims to fit into existing fraud prevention stacks.
👉 Read Fingerprint's analysis of device intelligence and AI agent detection →
AI agent detection and device signals: what should teams watch?
Explore further
15/08/2026 6:21 pm
Device intelligence is becoming a governance layer, not just a fraud signal. Once browser and device telemetry is used to inform login security, payment decisions, and step-up checks, it influences who gets trusted at runtime. That means identity teams should treat it as a control with policy, evidence, and audit requirements, not as a purely technical fingerprinting exercise. The practitioner conclusion is simple: if the signal affects access or transaction approval, it belongs in governance.
A question worth separating out:
Q: How do organisations govern sanctioned bots and AI agents more safely?
A: Start by registering approved automation, defining allowed actions, and tagging those actors in logs and risk systems. Then separate their telemetry from human sessions so suspicious behaviour can be investigated without collapsing all machine activity into one category. Governance works when policy, detection, and audit trails are aligned.
👉 Read our full editorial: Device intelligence is becoming central to fraud prevention
