Architecture and Design Principles

Question 1

Full operation within the company network with …

Answer 1

no data leaving

Question 2

Minimal copying and storage of

Answer 2

personal data within the platform

Question 3

No transmitting of personal information over a network in

Answer 3

the clear

Question 4

No mandated sharing of

Answer 4

personal data with other systems

Question 5

Easy horizontal

Answer 5

scalability

Question 6

Modern technology, portability, and ease of deployment. Leverage …

Answer 6

Docker containers, readiness for Kubernetes

Question 7

BigID supports 2-node and

Answer 7

multiple node deployment model based on containers.

Question 8

Scanning and correlation are

Answer 8

horizontally scalable.

Question 9

In other words, scanners can be deployed remotely for … (4 items)

Answer 9

• Faster scanning because the scanning is local
• Reduces data traffic volume from remote locations
• Easier network configuration (only inbound rule needed)
• Safer operation since sensitive information doesn’t travel over far networks

Question 10

RabbitMQ is listening on 2 ports, which are the default ports when SSL is enabled

Answer 10

• Port: 5671: amqp/ssl, purpose: AMQP 0-9-1 and AMQP 1.0 over TLS
• Port: 15671: https, purpose: HTTP API over TLS (HTTPS)

Question 11

The BigID user interface is SSL-enabled, and uses

Answer 11

HTTPS on port 443 by default

Question 12

The default SSL certificate is self-signed and will

Answer 12

generate a browser warning

Question 13

The web application runs client-side JavaScript code which communicates

Answer 13

with the server via REST API

Question 14

What is the Fundamental Processing Flow?

Answer 14

Scan ➤ Correlate ➤ Visualize ➤ Benefit

Question 15

Fundamental Processing Flow - Scan

Answer 15

Scan for personal information in various data sources, leveraging matching, classification and enrichment algorithms.

Question 16

Fundamental Processing Flow :: Correlate

Answer 16

Correlate personal information across data sources, and connect it back to the data subject identity.

Question 17

Fundamental Processing Flow - Visualize

Answer 17

Visualize relationships between data elements,

as well as the flow and processing of personal information across systems.

Question 18

Fundamental Processing Flow :: Benefit

Answer 18

Manage data subject consents and
on-demand access requests, and
reveal critical correlations in data breach scenarios.

Question 19

Discovery Methods :: Data Classification

Answer 19

Pattern matching on data values using regular expressions

Question 20

Discovery Methods :: Metadata Classification

Answer 20

Pattern matching on column names

Question 21

Discovery Methods :: Document Classification

Answer 21

Machine learning algorithms classifying and labeling documents by their type

Question 22

Discovery Methods :: Advanced Classification (Named Entity Recognition)

Answer 22

Machine learning algorithms looking for names, phones etc in unstructured data

Question 23

Discovery Methods :: Metadata Patterns

Answer 23

Analysis of permissions and metadata to identify over-exposed or suspect objects

Question 24

Discovery Methods - Correlation - Value Matching

Answer 24

• Also called reference set, IDSoR

* Value-matching and ML correlation between identifying attributes in entity sources and actual data

Question 25

Discovery Methods - Correlation - Enrichment

Answer 25

Proximity analysis to look at nearby data

Question 26

Discovery Methods - Cluster Analysis

Answer 26

Identify duplicate and similar files by topic

Question 27

When this process reveals unknown personal data (i.e. “dark data”), the BigID ML automatically correlates this data to an entity based on parameters like uniqueness, proximity, frequency, etc, and then calculates the quality
of the correlation using only metadata and not the private data itself. This quality is reported as

Answer 27

Confidence level. Confidence levels are only calculated for structured data, not unstructured data.