By 2021, organizations had visibility, anomaly detection, and increasingly sophisticated models of their environments.
But many teams still struggled with the same thing: risk scoring felt disconnected from reality.
The problem wasn’t that teams lacked data.
The problem was that risk was being calculated without context.
Why Traditional Risk Scoring Failed
Most risk models treated devices as isolated entities.
They looked at:
- vulnerabilities
- operating systems
- known threats
- exposure
But in connected environments, a device isn’t risky simply because it has a vulnerability.
A device is risky because of what it can reach.
Risk depends on:
- relationships
- communications
- privileges
- dependencies
- access paths
And without understanding those relationships, risk scoring becomes misleading.
The Breakthrough: ContextGraph
This is where ContextGraph changed the model.
Instead of assigning risk in isolation, teams began assessing risk through connection patterns.
ContextGraph represented environments as living systems, capturing:
- who talks to whom
- how frequently
- for what purpose
- with what dependencies
- across what segments
It made risk measurable in a way that aligned with reality.
When Risk Reflected Reality
With ContextGraph, teams could understand:
- which devices mattered most
- which vulnerabilities created real exposure
- where risk concentrated
- how threats could propagate
- which actions would reduce risk fastest
This allowed teams to prioritize remediation based on impact, not just severity.
It also made Zero Trust enforcement smarter.
Because policies could be aligned to real dependencies and real exposure.
The Shift to Actionable Risk
ContextGraph transformed risk from a static score into a decision-making tool.
Teams could:
- focus remediation efforts where it mattered most
- reduce attack paths, not just patch counts
- segment based on real communication patterns
- prove security improvement with measurable outcomes
This was the moment risk became actionable.
Not theoretical.
Not generic.
Real.

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