Lightbeam Basics: Identifying Identities at Risk for Enhanced Data Security
This video unveils Lightbeam's powerful entity risk identification features.
Lightbeam Basics: Identifying Identities at Risk for Enhanced Data Security
Find Entities at Risk in Seconds with Lightbeam!
Struggling to identify exposed identities in your data?
This video unveils Lightbeam's powerful entity risk identification features.
See how to:
Locate sensitive data subjects (like PII).
Identify policy violations for enhanced security.
LightBeam empowers you to proactively protect your data. ✨
Ready to find and safeguard your entities at risk?
Sign up for a free LightBeam trial and experience the difference - https://www.lightbeam.ai/
Transcript
Let's take a look at entities in the Lightbeam platform.
To lightbeam a person or data subject
Is an entity. It
Represents the data pulled together
and formed into one resolved entity from the entity widget.
On the dashboard, we can see 106,000 total entities have
been identified, of which 603 are showing a risk
or policy violation.
As we go into the entity screen, we will
See a list of everybody
That's been identified and the fields tracked for them.
We have the name, we have the risk
status, and you can see that
Some are at risk and some are at not
No risk. We can see the
number of attributes
For each person and the
Objects from which those attributes came,
and the entity type. In this
Case, human. We wanna
dig a Little deeper
and find out what attributes
Were found. We can go
to the next level. The entity
Detail screen breaks
Down all the information captured
for that particular person.
The number of attributes, the number
of data sources, and the number of
Policy violations are top three. We also see
The distribution
Amongst the attributes
and data sources, so we see that Social security number,
JIRA was the highest identified source.
We also contract the particular attributes,
the data sources, merged entities, alerts, and policies.
We can look at each one of these quickly.
This is a list of all the attributes that were captured
for Michael Hughes.
We see the medium status indicating the risk level,
the number of attribute instances for each one.
For data sources, we see that it was captured in AWS
and Jira, and we can drill into each one of these.
As we go further down the line,
we'll look at merged entities, merged entities
as a core piece of the entity resolution process.
In Lightbeam. We may see records
of Michael Hughes in different data sets
that have slightly variations on the information.
Here we see that Michael's generic name, Michael Hughes.
Brendan was in this dataset,
but not his first name and last name.
The Spectra engine has concluded from the rest
of the information around that.
These are all records relating to the same. Michael Hughes.
We also see the alerts
and policies that were violated for on Michael's behalf.
This was an information leakage policy
that captured 456 objects that violated this policy.
That is a look of entities in light.
To lightbeam a person or data subject
Is an entity. It
Represents the data pulled together
and formed into one resolved entity from the entity widget.
On the dashboard, we can see 106,000 total entities have
been identified, of which 603 are showing a risk
or policy violation.
As we go into the entity screen, we will
See a list of everybody
That's been identified and the fields tracked for them.
We have the name, we have the risk
status, and you can see that
Some are at risk and some are at not
No risk. We can see the
number of attributes
For each person and the
Objects from which those attributes came,
and the entity type. In this
Case, human. We wanna
dig a Little deeper
and find out what attributes
Were found. We can go
to the next level. The entity
Detail screen breaks
Down all the information captured
for that particular person.
The number of attributes, the number
of data sources, and the number of
Policy violations are top three. We also see
The distribution
Amongst the attributes
and data sources, so we see that Social security number,
JIRA was the highest identified source.
We also contract the particular attributes,
the data sources, merged entities, alerts, and policies.
We can look at each one of these quickly.
This is a list of all the attributes that were captured
for Michael Hughes.
We see the medium status indicating the risk level,
the number of attribute instances for each one.
For data sources, we see that it was captured in AWS
and Jira, and we can drill into each one of these.
As we go further down the line,
we'll look at merged entities, merged entities
as a core piece of the entity resolution process.
In Lightbeam. We may see records
of Michael Hughes in different data sets
that have slightly variations on the information.
Here we see that Michael's generic name, Michael Hughes.
Brendan was in this dataset,
but not his first name and last name.
The Spectra engine has concluded from the rest
of the information around that.
These are all records relating to the same. Michael Hughes.
We also see the alerts
and policies that were violated for on Michael's behalf.
This was an information leakage policy
that captured 456 objects that violated this policy.
That is a look of entities in light.
