Explain Data Discovery

Question 1

Generally speaking, there are several discovery approaches. The simplest one is looking at

Answer 1

metadata conditions, like the permissions of a file, to see if it’s accessible to all. This is a simple check that can help companies identify over-exposed objects.

Question 2

Another discovery approaches is going into the data itself and is pattern-based, which

Answer 2

usually leverages regular expressions to find PII. You cannot use it to connect the data to its owner. You can however use it to see if a certain data source contains a certain type of data, and it produces significant value with very little effort.

Question 3

Going one level up, we can leverage NLP (natural language processing) to

Answer 3

identify names, addresses, phone numbers and other contextual data. It also cannot connect data to its owner but would be enough for some
legacy regulations.

Question 4

the highest level of data discovery, which is the most complex to implement but also the most
extensive, uses

Answer 4

smart value matching and machine learning to correlate entities with their data. This level is required for modern privacy
egulations like GDPR and CCPA and really the only way to fulfil use cases like
DSAR and breach response.

Question 5

On top of all discovery approaches, a company would greatly benefit from

Answer 5

a catalog/registry that shows all the results

from all those discovery levels in one place.

Question 6

BigID Discovery Types - Correlation:

Answer 6

find connected and associated data to an entity or person

Question 7

BigID Discovery Types - Classification:

Answer 7

locate specific format or type of data

Question 8

BigID Discovery Types - Clustering:

Answer 8

find duplicate and related data by topic

Question 9

BigID Discovery Types - Catalog:

Answer 9

metadata collection for fast PII catalog view

Question 10

BigID Discovery Methods - Reference Set (IDSoR)

Answer 10

Discovery Algorithm? Value Matching

When? Scan

Correlated? Yes

Question 11

BigID Discovery Methods - Enrichment

Answer 11

Discovery Algorithm? Proximity Analysis

When? Scan

Correlated? Yes

Question 12

BigID Discovery Methods - Data Classification

Answer 12

Discovery Algorithm? Pattern Matching

When? Scan

Correlated? No

Question 13

BigID Discovery Methods - Advanced Classification

Answer 13

Discovery Algorithm? Machine Learning (NLP)

When? Scan

Correlated? No

Question 14

BigID Discovery Methods - Document Classification

Answer 14

Discovery Algorithm? Machine Learning

When? Scan

Correlated? No

Question 15

BigID Discovery Methods - Subject Access Request

Answer 15

Discovery Algorithm? Index, Value Matching and Proximity

When? Report

Correlated? Yes

Question 16

There are different ways in which BigID could identify personal data. The default
method is

Answer 16

using value matching and leveraging correlation. For each data source one
can also choose to enable enrichment and/or classification.

Question 17

Machine learning is used in different areas of the platform:

Answer 17

• Correlation
• Cleansing the information we find
• Advanced Classification
• Document classification

Question 18

The value matching method requires

Answer 18

the use of attributes from entity sources.

Question 19

When correlation process revealsunknown personal data (i.e. “dark data”), the BigID ML automatically correlates this data to an entity based on

Answer 19

parameters like uniqueness, proximity, frequency, etc, and then calculates the quality of the correlation using only metadata and not the private data itself.

Question 20

BigID uses intelligent correlation algorithms utilizing entity sources to

Answer 20

understand basic

identifiers, relationships, and distributions in other data stores.

Question 21

confidence levels

are only calculated for

Answer 21

structured data, not unstructured data.

Question 22

For unstructured data, we do rely on what we discovered during the initial scan because with unstructured data we

Answer 22

try not to sample

Question 23

Value Matching Logic

Answer 23

Break the data field into segments (a
segment starts at string start or a
delimiter, and ends at string end or a
delimiter).

A delimiter is a whitespace or a punctuation
character.

Segments of 4 characters or less are ignored.

Perform case-insensitive match of the
(as-is) entity field to each segment.

Question 24

Enrichment is based on ____ , and is only applicable to ____.

Answer 24

proximity - structured data.

Question 25

Enrichment is an option that can be enabled for each data source. It enables BigID to

Answer 25

surface additional potential personal data findings that are highly identifiable in the proximity of attributes identified via value matching

Question 26

The results from data classification are not correlated to ____, but they will still
show as standalone findings in the ____ on the BigID UI.

Answer 26

entities - inventory

Question 27

Data classification results can also be reviewed using

Answer 27

Scan Results Details report and in the Entity Correlation

page.

Question 28

Data Subject Access Request is another method to identify and report more potential
personal information beyond

Answer 28

the default reference set scan and Enrichment.

Question 29

Discovery Flow

Answer 29

Configure Data Sources ➤ Configure Entity Sources ➤ Scan Entity Sources ➤ Scan Data Sources ➤ Correlate ➤ Scan Results ➤ Inventory ➤ Apps / Use Cases

Question 30

Configure Entity Sources, set relevant data sources as entity sources,
testing first (on first 1000 records) to get
initial identifiability, select

Answer 30

entity attributes, override sure match, and set sampling.