Lenses allows you share data and create policies to mask data at a field level. This is an important feature to protect sensitive data or meet requirements to comply with regulations such as GDPR, CCPA or HIPAA. This guide details how to apply and manage data policies.
Data policies are used to detect, classify and protect data. The best practice is to create a comprehensive data inventory which includes details about personal information, which data source holds this data and what applications are using it.
Lenses helps you automate this process by creating policies on a field level, which apply to all datasets or specific ones. When building a streaming platform and onboard multiple users and projects
with data policies you can:
Lenses data policies are influenced by the standards of the National Institute of Standards and Technology (NIST).
The governance is global, across all users and clients including API, CLI, UI, and SQL.
Policy permissions are under the Admin category so they are not scoped to the namespace.
That means that users authorized with this permission can create policies for all the known datasets to Lenses.
Access Management & permissions
A Data Policy is a rule to detect, classify and protect data with an associated redaction to mask the data.
For example the policy below describes how Lenses should handle Credit Cards.
For every dataset, across multiple connections, when a field is matching the declared fields in the policy,
the data will be masked with the Last-4 redaction, which means only the last 4 digits will appear.
The datasets are classified under the Financial category and of HIGH severity.
Lenses maintains an internal cache to identify fields for each dataset (ie. your Kafka topics). Review data types and schemas to understand more about this topic. As a result every time a new policy is created or a new field is added to an existing policy the matching mechanism applies and detects which datasets are going to be affected by the policy and also which applications known to Lenses are using them.
The governance is global and applies for all users. That means that there is no way to
“escape” the policy even if you are an admin user. In order to retrieve the actual data
you will have to remove the policy or the respective fields.
The underlying data is not affected by Lenses policies. That means that the applications
processing the affected datasets will be having full access to the data itself. The policies
apply to the Lenses interfaces.
For Kafka Topics, we apply the Policy to both Key and Value,
and the policy will apply to each of these if they contain the corresponding field.
The Data Policy’s principal properties are:
The rule to use to obfuscate a field. Lenses applies data obfuscation to all data access requests, and several data types/structures
are supported, including Strings, Numbers, Emails for every data format (JSON, XML, AVRO or Protobuf).
These rules can apply regardless of the field type:
These rules can apply only on alphanumeric fields:
These rules can apply to numeric fields:
Fields which are not numeric will not be affected by these Policies.
Strings that contain numbers will not be affected either.
What is your Data’s category for sensitivity? Any value can be entered here, based on what makes sense for your organisation to classify the policies. Every policy
belongs to one category.
Find more information about Data Classification. Also here are a few popular options.
How important is the Data for the Business? It refers to the sensitivity level of the information to be stored and processed.
You can choose to encapsulate your Policy, for a specific Dataset(s).
This is a wildcard option, and if not specified, it will apply to all Datasets.
Which field(s), should we target and obfuscate. This is a also a wildcard option. There are a few advanced fields specifications that we need to be careful with.
In the case of nested data, it is possible to specify nested fields using the “.” character.
For example, if your “customers” Dataset has a field called information which contains a field called name,
it is possible to specify the field information.name, so that only that particular field is obfuscated, instead of every field.
Note that obfuscation is only performed on nodes without children.
Continuing with the example above, information.name will be obfuscated, but if we attempt to apply it to information, it will not be affected, as it has child properties.
In the event of two policies matching a given field, the more specific one will be applied. For example, if there is a policy for name with a redaction of First-4 and a policy for customers.information.name with a redaction of Initials, the latter will be applied.
Please note that wildcards and dataset rules do not affect this.
It is also possible to specify wildcards using the * character so that i*n.name will match both information.name and installation.name. As . is considered a field separator, such that a wildcard will not match it. So i*n.name will match information.namebut will not match information.details.name.
To create a new Policy navigate to Data Policies and select New Policy.
Let’s create a Policy called Full Name,
which protects PII information by showing only the first Letter First-1 Redaction, for either first or last names.
The obfuscation is applied to all Datasets, with names that end with the word info and apply the obfuscation to the fields firstName and lastName.
Once the Policy Full Name is created, any data source in data catalog (Kafka, elasticsearch, etc), contains “firstName” or “lastName” will automatically be detected, irrispective the data format as Avro, JSON, XML and Protobuf.
Apart from identifying all the sensitive data at a field level, Lenses will also protect the data for you. That means that anyone accessing data via Lenses (UI/CLI/API/SQL) can access production data while respecting the underlying data’s sensitivity.
In the image below, you can see that the fields firstName and lastName are masked, and the First-1 policy is applied, just like we wanted.
We can now view the details of the Policy. By clicking the link in the Listing, you will be redirected to the Details Page.
There you will be able to see all the available information about the given policy. From Details, Applied Data and Detected Flows,
to quickly identify if an Application (SQL Processors, Kafka Connector, or Custom App) uses protected data.
This example shows the Policy we just created and that it affects 2 Kafka Topics and 1 Elasticsearch Index. You can see that all Datasets end with the word info, exactly what we wanted to achieve.
We can also see that we have detected some Data Flows producing/consuming from those Datasets. In our case,
we see that we have 3 Applications that are consuming from CustomersInfo:
Lenses Data catalog is Data Policies aware. Obfuscated fields are now highlighted together with their respected Policies Categories
Below you can see, that when we are searching for cu, we can see that the search API, is returning all the fields containing cu like customerFirstName, currency which are protected
with Data Policies. On the other hand, the field accuracy is not.
Out of the box, Lenses provides a set of data protection policies with matchers for the most common fields.
You can optionally load the default policies and Lenses will automatically scan the datasets with those fields
in their schemas and apply the policy while exploring data. If your schema is not detected make sure
you amend to match.
The default policies will load at once. Here is a list of what’s included:
You can edit and delete a Data Policy by clicking the actions button at the top right of the screen. You can also Delete a Policy by the Listing Page as well.
You can edit or add new fields to a default policy or even delete if not applicable.
Data policies are also supported by the CLI to enable automation scenarios.
Here is an example to export and import policies to a different Lenses setup:
# Export policies
export policies --resource-name policyName
# Import policies
import policies --dir /prod-dir --ignore-errors
CLI - API
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