Understanding Lightbeam: Cataloging Sensitive Data in Structured Repositories
Lightbeam catalogs sensitive data in structured repositories—scan databases, classify with AI/ML, and ensure compliance with ease.
Understanding Lightbeam: Cataloging Sensitive Data in Structured Repositories
Are your structured databases secure and compliant?
In this Understanding Lightbeam episode, see how our platform redefines structured data management:
Automated Scanning: Instantly analyze tables and columns across databases
AI/ML Insights: Use Named Entity Recognition (NER) to classify sensitive data accurately
Comprehensive Classification: Detect multiple sensitive types (credit cards, passports, etc.) within the same field
Lightbeam provides unmatched visibility into your structured data, helping you strengthen governance, compliance, and security.
Transcript
Let's talk about how Lightbeam helps you in cataloging all
kinds of sensitive information
that you might have in your structured data repositories,
like your databases and data warehouses.
One of the things that Lightbeam does is as soon
as you connect your databases
and data warehouses to lightbeam, it starts scanning
the tables that are present within your database
and data warehouses.
And it scans the tables and looks at the columns.
It looks at the column names,
also looks at the column contents,
what's the continent in those columns?
And it starts using its AI ml to figure out what kind
of data is present within these columns.
But for example, here you can see
that in column C one Lightroom has classified that as name.
It's using a technique called NER, named n TT resolution
to figure out that this column contains names
of maybe your patients, maybe your account holders,
maybe your students, uh, whatever the case might be.
You can also do first names
and last names if you want that kind of a distribution.
Similarly, in column two, it has figured out that, um,
you have credit card information,
but here, here's where it might become, uh,
even more interesting for you, which is the concept
of maybe PII, which is in a given column, right,
is actually detecting that while there are some credit card
numbers in that column, there might also be passports.
And so what Lightroom does is it actually
classifies this column C four as having
both credit cards and passports.
What it is actually allowing you
to do is actually allowing you to review
the data within this columns by pointing
and hinting at you
that you should actually take a look at this column.
This column also might contain credit card information.
Where, where might this be useful is if you're a bank
or a financial institution
and you're trying to become PCI compliant,
and you want to have a comprehensive view
of all cardholder data that you might have across thousands
and thousands of tables
and millions of columns that you have within your databases.
Library will not only go and scan and classify everything,
but it actually also bubble up the columns
that might have sensitive information using this maybe PII
construct.
kinds of sensitive information
that you might have in your structured data repositories,
like your databases and data warehouses.
One of the things that Lightbeam does is as soon
as you connect your databases
and data warehouses to lightbeam, it starts scanning
the tables that are present within your database
and data warehouses.
And it scans the tables and looks at the columns.
It looks at the column names,
also looks at the column contents,
what's the continent in those columns?
And it starts using its AI ml to figure out what kind
of data is present within these columns.
But for example, here you can see
that in column C one Lightroom has classified that as name.
It's using a technique called NER, named n TT resolution
to figure out that this column contains names
of maybe your patients, maybe your account holders,
maybe your students, uh, whatever the case might be.
You can also do first names
and last names if you want that kind of a distribution.
Similarly, in column two, it has figured out that, um,
you have credit card information,
but here, here's where it might become, uh,
even more interesting for you, which is the concept
of maybe PII, which is in a given column, right,
is actually detecting that while there are some credit card
numbers in that column, there might also be passports.
And so what Lightroom does is it actually
classifies this column C four as having
both credit cards and passports.
What it is actually allowing you
to do is actually allowing you to review
the data within this columns by pointing
and hinting at you
that you should actually take a look at this column.
This column also might contain credit card information.
Where, where might this be useful is if you're a bank
or a financial institution
and you're trying to become PCI compliant,
and you want to have a comprehensive view
of all cardholder data that you might have across thousands
and thousands of tables
and millions of columns that you have within your databases.
Library will not only go and scan and classify everything,
but it actually also bubble up the columns
that might have sensitive information using this maybe PII
construct.
