How imper.ai Evaluates Risk and Makes Decisions

imper.ai evaluates identity risk in real time by correlating hundreds of contextual signals across device, network, behavior, and usage patterns. This article explains how those signals are translated   into decisions during live interactions.

In this article

• Risk signals and context

• Real-Time Risk Score

• Policy thresholds and decisions

• Verification is always active

Risk signals and context

imper.ai continuously ingests signals related to:

• Network and location characteristics

• Endpoint and device attributes

• Behavioral and usage patterns

Signals are contextual and weighted. No single signal is treated as a simple binary “allow” or “deny” indicator.

Real-Time Risk Score

All observed signals are correlated to generate a Real-Time Risk Score. This score represents the overall likelihood that an interaction involves impersonation, account compromise, or automated abuse.

A single critical signal (for example, a known malicious device fingerprint) may significantly raise the score, but decisions are based on the composite score, not individual flags.

Policy thresholds and decisions

imper.ai decisions are policy-driven. Administrators define thresholds that map risk scores to outcomes.

Example policies include:

• Allow interaction when risk score is below a defined threshold

• Require additional verification when risk score is elevated

• Block or terminate interactions when risk score exceeds a critical threshold

This approach ensures that blocking behavior is consistent, explainable, and configurable.

Verification is always active

imper.ai operates in a proactive verification model. Every supported workflow begins with verification and risk evaluation, rather than waiting for a suspicious event to occur.