This series of documents provides strategies for evaluating and mitigating data
risk in your organization. It also describes and compares two
Sensitive Data Protection services that help you learn more about your
current data security posture.

## Objectives of data risk management

Managing data risk involves storing, processing, and using your data within the
appropriate risk levels for your business. When you perform data risk
management, we recommend that you aim for the following objectives:

- Your data is properly discovered and classified.
- Risk of data exposure is properly understood.
- Data is protected by appropriate controls or de-risked through obfuscation.

As you evaluate your data workloads you can start by asking these
questions:

- What kind of data does this workload handle and is any of it sensitive?
- Is this data properly exposed? For example, is access to the data restricted to the right users, in the right environment, and for an approved purpose?
- Can the risk of this data be reduced through data minimization and obfuscation strategies?

Taking a well-informed and risk-based approach can help you make the most of
your data without compromising the privacy of your users.

## Example analysis

For this example, suppose your data team is trying to build a machine learning
model based on customer feedback in product reviews.

### What kind of data does this workload handle and is any of it sensitive?

In the data workload, you found that the primary key used is the customer email
address. Customer email addresses often contain the customers' names.
Additionally, the actual product reviews contain unstructured data (or *freeform
data*) submitted by the customer. Unstructured data can contain intermittent
instances of sensitive data like phone numbers and addresses.

### Is this data properly exposed?

You found that the data is accessible only to the product team. However, you
want to share the data to your data analytics team, so that they can use it to
build a machine learning model. Exposing the data to more people also means
exposing it to more development environments where this data will be stored and
processed. You determined that the exposure risk will increase.

### Can the risk of this data be reduced through data minimization and obfuscation strategies?

You know that the analytics team doesn't need any of the actual sensitive
personally identifiable information (PII) in the dataset. However, they need
to aggregate the data per customer. They need a way to determine which reviews
belong to the same customer. To address this need, you decide to tokenize all
the structured PII---the customer email addresses---to keep the
referential integrity of your data. You also decide to inspect the unstructured
data---the reviews---and mask any intermittent sensitive data within it.

## What's next

- [Compare Sensitive Data Protection services that help you learn about your
  data](https://docs.cloud.google.com/sensitive-data-protection/docs/learn-about-your-data) (next document in this series)