Understanding Lightbeam: Context-Aware Data Discovery Explained
Lightbeam’s context-aware data discovery uses natural language understanding to identify sensitive data with unmatched accuracy.
Understanding Lightbeam: Context-Aware Data Discovery Explained
How can you find sensitive data when it hides in plain sight?
In this Understanding Lightbeam episode, discover how context-aware data discovery changes the game:
Uses natural language understanding to analyze meaning and intent
Accurately identifies sensitive data, even in ambiguous cases
Delivers precise, reliable classification for stronger governance
With Lightbeam, data discovery goes beyond keywords—giving you context-driven accuracy to manage risk and compliance with confidence.
Transcript
So what does context look like?
We are talking about this nineish number,
and most systems might report this number
as a nineish number, which might be put
as a social security number,
but context where data discovery means that you need
to understand what the context is in which this nineish
number is appearing.
Now, this number might be appearing in a chat, maybe a Slack
or a Team chat message.
All this might be appearing in a log
of a customer, uh, ticketing system.
So if you look at the context here,
you're gonna actually understand that the discussion
that's going on in this thread is not about a social
security number, but about a medical record number.
So what Lightroom does using its national language
understanding techniques, is it actually understands that,
oh, the context over here is a medical record number.
People are asking question to share,
and then someone is saying, why do you need that?
And so on and so forth. But then eventually in this context,
this nineties number was shared,
and that's where Lightning figure out that this 90 s number
has a higher probability
of being a medical record number than being a social
security number or any other id.
So it gives it a higher weight to say this might be an MRN.
Now, like we might actually come across this exact same
number in a different context.
And again, to look at the context to figure out is
that a medical record number?
And as it comes across more
and more of these, uh, this number, with this context,
it'll have a better understanding of this data being MRN,
and then it will get reported as a medical record number.
So you can see that the context of a data discovery,
the advantage of that is,
is a lot fewer false positives when Lightbeam reports
as data has a particular type of sensory data element.
We are talking about this nineish number,
and most systems might report this number
as a nineish number, which might be put
as a social security number,
but context where data discovery means that you need
to understand what the context is in which this nineish
number is appearing.
Now, this number might be appearing in a chat, maybe a Slack
or a Team chat message.
All this might be appearing in a log
of a customer, uh, ticketing system.
So if you look at the context here,
you're gonna actually understand that the discussion
that's going on in this thread is not about a social
security number, but about a medical record number.
So what Lightroom does using its national language
understanding techniques, is it actually understands that,
oh, the context over here is a medical record number.
People are asking question to share,
and then someone is saying, why do you need that?
And so on and so forth. But then eventually in this context,
this nineties number was shared,
and that's where Lightning figure out that this 90 s number
has a higher probability
of being a medical record number than being a social
security number or any other id.
So it gives it a higher weight to say this might be an MRN.
Now, like we might actually come across this exact same
number in a different context.
And again, to look at the context to figure out is
that a medical record number?
And as it comes across more
and more of these, uh, this number, with this context,
it'll have a better understanding of this data being MRN,
and then it will get reported as a medical record number.
So you can see that the context of a data discovery,
the advantage of that is,
is a lot fewer false positives when Lightbeam reports
as data has a particular type of sensory data element.
