Risk signals in imper.ai do not act in isolation. They are continuously evaluated, correlated, and translated into decisions that determine how an interaction proceeds. This article explains how risk signals move from detection to remediation.
In this article
From signal to decision
A risk signal represents a single observed indicator related to network conditions, device characteristics, or behavior.
On its own, a signal does not determine outcome. imper.ai evaluates signals as part of a broader, real-time decision process designed to assess whether an interaction can safely continue.
Risk correlation and scoring
As an interaction progresses, imper.ai continuously correlates multiple signals across time and context to generate a Real-Time Risk Score.
This score reflects:
The severity of observed signals
The consistency of those signals over time
How closely the interaction aligns with known user baselines
Signals that appear benign in isolation may become meaningful when combined with other indicators or repeated patterns.
Policy evaluation
The Real-Time Risk Score is evaluated against administrator-defined policies. These policies define how imper.ai should respond at different risk levels.
Typical policy outcomes include:
Allow the interaction to continue without interruption
Require additional verification or user confirmation
Escalate the interaction for review
Block or terminate the interaction
This policy-based approach ensures that decisions are consistent, explainable, and aligned with organizational risk tolerance.
Remediation actions
When a policy threshold is reached, imper.ai executes a remediation action appropriate to the interaction channel.
Remediation actions are always performed within the native environment, such as a meeting platform, chat interface, or helpdesk workflow.
Examples include:
Ending or restricting a live meeting
Blocking completion of a sensitive helpdesk request
Preventing further interaction until verification succeeds
Manual vs automated response
imper.ai supports both automated and human-in-the-loop remediation.
In automated workflows, actions are triggered immediately when policy thresholds are exceeded.
In manual workflows, risk context is surfaced to an operator or analyst, allowing a human decision before action is taken.
Note: Available remediation actions depend on the communication channel and the permissions granted to imper.ai.