Ask Sophia: Data Classification
Classify and tag files. Link files to owners and access. Govern risk across cloud, SaaS, and OnPrem without busywork.
Ask Sophia: Data Classification
Classify and tag files. Link files to owners and access. Govern risk across cloud, SaaS, and OnPrem without busywork.
Transcript
Hi, I'm Sophia, LightBeam's AI product guide.
I help security teams understand where sensitive data exists, who can
access it, and where it may be exposed.
In this short walkthrough, I'll show you how LightBeam uses AI to discover
sensitive data, connect it to identities, and help reduce risk across your
environment. What would you like to explore?
The real problem is that labels without identity context do not tell you whose
sensitive data you hold or who can actually reach it, which is where exposure and
audit gaps usually hide. LightBeam classifies sensitive data across
global environments, ties each label to identity and access through its
data identity graph, and makes those findings usable for revoking risky
access and producing GDPR or PCI audit evidence.
Let me pull up how sensitive data gets classified with identity context
so the risky access paths become obvious.
Going ahead, the entity-centered view flips the script from files to the
people they describe, instantly surfacing every piece of sensitive data
attached to a customer or partner, which cuts the time spent hunting for hidden
exposure.
Entity view pulls together all attributes, systems, and policy
flags for that individual, giving you a single risk score that tells you why the
data matters and where remediation is needed.
Once you're done with that, the attributes pane breaks down exactly which
PII or PCI elements LightBeam has identified, so you can
prioritize controls based on the most regulated data types.
From here, the locations view maps each attribute to the repositories that
hold it, turning abstract classification into a concrete list of where
data lives and what residency rules apply.
Right after this, merged entities stitches together those disparate records
into a unified identity, eliminating the blind spot where a single person's
data is scattered across siloed systems.
Moving on, we drill into a specific document linked to that merged identity,
showing exactly which file contains the regulated fields and which user accessed
it.
Following that, the classification detail explains why the file is flagged, what
data types it holds, and how that label drives automated remediation,
audit reporting, and compliance proof.
I help security teams understand where sensitive data exists, who can
access it, and where it may be exposed.
In this short walkthrough, I'll show you how LightBeam uses AI to discover
sensitive data, connect it to identities, and help reduce risk across your
environment. What would you like to explore?
The real problem is that labels without identity context do not tell you whose
sensitive data you hold or who can actually reach it, which is where exposure and
audit gaps usually hide. LightBeam classifies sensitive data across
global environments, ties each label to identity and access through its
data identity graph, and makes those findings usable for revoking risky
access and producing GDPR or PCI audit evidence.
Let me pull up how sensitive data gets classified with identity context
so the risky access paths become obvious.
Going ahead, the entity-centered view flips the script from files to the
people they describe, instantly surfacing every piece of sensitive data
attached to a customer or partner, which cuts the time spent hunting for hidden
exposure.
Entity view pulls together all attributes, systems, and policy
flags for that individual, giving you a single risk score that tells you why the
data matters and where remediation is needed.
Once you're done with that, the attributes pane breaks down exactly which
PII or PCI elements LightBeam has identified, so you can
prioritize controls based on the most regulated data types.
From here, the locations view maps each attribute to the repositories that
hold it, turning abstract classification into a concrete list of where
data lives and what residency rules apply.
Right after this, merged entities stitches together those disparate records
into a unified identity, eliminating the blind spot where a single person's
data is scattered across siloed systems.
Moving on, we drill into a specific document linked to that merged identity,
showing exactly which file contains the regulated fields and which user accessed
it.
Following that, the classification detail explains why the file is flagged, what
data types it holds, and how that label drives automated remediation,
audit reporting, and compliance proof.
