Understanding Lightbeam: Characterizing Data with Learning & Linking
Lightbeam uses AI learning & linking to map and secure data—connect disparate points, detect patterns, and strengthen data security posture.
Understanding Lightbeam: Characterizing Data with Learning & Linking
How do you truly understand and secure your data?
In this Understanding Lightbeam episode, explore how AI-driven learning and linking builds a complete picture of your information landscape:
Operates entirely within your network to keep sensitive data protected
Identifies and connects disparate data points into a unified map
Recognizes patterns to link random data back to known sources
Strengthens your data security posture with intelligent, automated insights
Lightbeam transforms scattered data into connected intelligence, making governance and protection smarter than ever.
Transcript
Let's understand the concept of learning
and linking with Lightbeam ai.
One of the first things to understand is lightbeam,
even though it's an AI system,
it gets deployed in your own environment,
within your own network.
It means that whatever Lightbeam scans, none of that data
or metadata ever leaves your network or your environment.
Everything stays inside of the network.
With that, let's say you connect lively to one
of your data bases or data warehouses.
Now lively as is scanning.
It might figure out that this is a table
that contains a name, an account type.
So what this name is all about and a driver's license.
It looks at this table.
It actually goes through this table
and figures out that you got John Doe, Jane Doe,
Hannah Cooper, and perhaps millions
of other names within the table along
with their account type and their driver's licenses.
What Lightman is doing is actually learning from this table.
Internally. We call a table like this as a source of truth
because this is telling us what data you as a customer
uniquely consider important.
A different customer might have a different data set
as their source of truth.
They might consider different data set as important,
but for you, this is the data that you important
as might being has scanned this.
It has created its own metta map.
Now, as lightning goes
and maybe scans a random data set within your environment,
this could be your document repository.
This could be your ticketing system,
this could be your logs that you might have.
And let's assume that light comes across this
six digit alpha character called Y 1 3 6 5 0.
It doesn't really have any context around this,
so most systems will actually just drop this number
or if it matches a pattern, they will report this,
that this is matching maybe some sort of ID
or some sort of another information, not lightbeam.
What Lightbeam does, it actually recognizes that this,
it has come across this number somewhere else.
It has come across this number in the source of truth system
that it has scanned before.
Now see what happens is it Lightbeam actually links this
information that it came across in a random
data set of yours.
It links us over here
and it automatically figures out that this data belongs
to Jane Doe, who's an employee in within your system.
So now in life of reports on this number, it says
that I have found a file
or a ticket where Y 1 3 6 5 0 was detected.
It actually reports saying
that this data is actually a driver's license number
that belongs to Jane Doe,
who is an employee within your system.
So all of a sudden you can see how Lightbeam starts putting
identity at the center of data discovery
and it helps you strengthen
and harden your data security posture
because you're not just, uh, being reported on some random
Six digit alpha newer character.
You are actually being told that this number belongs
to an employee whose name is Jane Doe.
And you can actually put a policy
that says if an employee's driver's license
or if an employee's highly sensitive data is actually being
detected in a log, either report on that
or redact that information or delete that file
or archive that file
and light can do all of those things for you.
So you can see that this learning
and linking this is a unique Lightroom capability using
learning, which is the AI based learning
that Lightroom is doing and linking,
which is linking the random dataset information
that's detected with, with source of truth data that,
that you have trained lightbeam on.
Uh, it is able to characterize any random data
that it is detecting with much more information
that helps you make automated decisions within your
environment.
and linking with Lightbeam ai.
One of the first things to understand is lightbeam,
even though it's an AI system,
it gets deployed in your own environment,
within your own network.
It means that whatever Lightbeam scans, none of that data
or metadata ever leaves your network or your environment.
Everything stays inside of the network.
With that, let's say you connect lively to one
of your data bases or data warehouses.
Now lively as is scanning.
It might figure out that this is a table
that contains a name, an account type.
So what this name is all about and a driver's license.
It looks at this table.
It actually goes through this table
and figures out that you got John Doe, Jane Doe,
Hannah Cooper, and perhaps millions
of other names within the table along
with their account type and their driver's licenses.
What Lightman is doing is actually learning from this table.
Internally. We call a table like this as a source of truth
because this is telling us what data you as a customer
uniquely consider important.
A different customer might have a different data set
as their source of truth.
They might consider different data set as important,
but for you, this is the data that you important
as might being has scanned this.
It has created its own metta map.
Now, as lightning goes
and maybe scans a random data set within your environment,
this could be your document repository.
This could be your ticketing system,
this could be your logs that you might have.
And let's assume that light comes across this
six digit alpha character called Y 1 3 6 5 0.
It doesn't really have any context around this,
so most systems will actually just drop this number
or if it matches a pattern, they will report this,
that this is matching maybe some sort of ID
or some sort of another information, not lightbeam.
What Lightbeam does, it actually recognizes that this,
it has come across this number somewhere else.
It has come across this number in the source of truth system
that it has scanned before.
Now see what happens is it Lightbeam actually links this
information that it came across in a random
data set of yours.
It links us over here
and it automatically figures out that this data belongs
to Jane Doe, who's an employee in within your system.
So now in life of reports on this number, it says
that I have found a file
or a ticket where Y 1 3 6 5 0 was detected.
It actually reports saying
that this data is actually a driver's license number
that belongs to Jane Doe,
who is an employee within your system.
So all of a sudden you can see how Lightbeam starts putting
identity at the center of data discovery
and it helps you strengthen
and harden your data security posture
because you're not just, uh, being reported on some random
Six digit alpha newer character.
You are actually being told that this number belongs
to an employee whose name is Jane Doe.
And you can actually put a policy
that says if an employee's driver's license
or if an employee's highly sensitive data is actually being
detected in a log, either report on that
or redact that information or delete that file
or archive that file
and light can do all of those things for you.
So you can see that this learning
and linking this is a unique Lightroom capability using
learning, which is the AI based learning
that Lightroom is doing and linking,
which is linking the random dataset information
that's detected with, with source of truth data that,
that you have trained lightbeam on.
Uh, it is able to characterize any random data
that it is detecting with much more information
that helps you make automated decisions within your
environment.
