NOTE: Transform secrets engine requires Vault Enterprise Advanced Data Protection (ADP) license. To explore Vault Enterprise features, you can sign up for a free 30-day trial from here.
»Challenge
When encrypting sensitive data, preservation of the original data format or length may be required to meet certain industry standards such as HIPAA or PCI. To fulfill this requirement, the transform secrets engine performs format preserving encryption (FPE).
However, there are organizations that care more about the irreversibility of the tokenized data and not so much about preserving the original data format. Therefore, the transform secrets engine's FPE transformation may not meet the governance, risk and compliance (GRC) strategy they are looking for due to the use of reversible cryptography to perform FPE.
»Solution
Transform secrets engine has a data transformation method to tokenize sensitive data stored outside of Vault. Tokenization replaces sensitive data with unique values (tokens) that are unrelated to the original value in any algorithmic sense. Therefore, those tokens cannot risk exposing the plaintext satisfying the PCI-DSS guidance.
»Characteristics of the tokenization transformation:
Non-reversible identification: Protect data pursuant to requirements for data irreversibility (PCI-DSS, GDPR, etc.)
Integrated Metadata: Supports metadata for identifying data type and purpose
Extreme scale and performance: Support for performantly managing billions of tokens across clouds as well as on-premise
»Prerequisites
To perform the tasks described in this tutorial, you need to have a Vault Enterprise v1.6 or later with Advanced Data Protection module.
NOTE: To explore Vault Enterprise features, you can sign up for a free 30-day trial.
»Policy requirements
NOTE: For the purpose of this tutorial, you can use root
token to work
with Vault. However, it is recommended that root tokens are only used for just
enough initial setup or in emergencies. As a best practice, use tokens with
appropriate set of policies based on your role in the organization.
To perform all tasks demonstrated in this tutorial, your policy must include the following permissions:
If you are not familiar with policies, refer to the policies tutorial.
»Setup the Transform secrets engine
Create a role named, mobile-pay
which is attached to credit-card
transformation. The tokenized value has a fixed maximum time-to-live (TTL) of 24
hours.
Enable the
transform
secrets engine attransform/
.Create a role named
mobile-pay
with a transformation namedcredit-card
.The
mobile-pay
role is created.The role is created but the
credit-card
transformation does not exist, yet.Create a transformation named
credit-card
which sets the generated token's time-to-live (TTL) to 24 hours.Output:
The
max_ttl
is an optional parameter which allows you to control how long the token should stay valid.NOTE: Set the
allowed_roles
parameter to a wildcard (*
) to allow all roles or with globs at the end for pattern matching (e.g.mobile-*
).Display details about the
credit-card
transformation.Notice that the
type
is set totokenization
.
»Tokenize secrets
The Vault client applications must have the following in their policy to perform
tokenization transformation using the Transform secrets engine enabled at
transform/
.
Encode a value with the
mobile-pay
role with some metadata.The
ttl
value is an optional parameter. Remember that themax_ttl
was set to 24 hours when you created thecredit-card
transformation. You can overwrite that value to make the token's TTL to be shorter.The output displays the encoded value.
Set the generated token value in a
MY_TOKEN
environment variable for testing.Example:
Retrieve the metadata of the token.
Notice that
expiration_time
is displayed. Since you have overwritten themax_ttl
, thettl
is set to 8 hours.Validate the token value.
Validate that the credit card number has been tokenized already.
Retrieve the original plaintext credit card value.
»Convergent tokenization
Requirement: This feature requires Vault 1.11.0 or later.
If you run the command multiple times, you would notice that it returns a different encoded value every time.
Example:
In some use cases, you may want to have the same encoded value for a given input so that you can query your database to count the number of entries for a given secret.
Key derivation is supported to allow the same key to be used for multiple purposes by deriving a new key based on a user-supplied context value. In this mode, convergent encryption can optionally be supported, which allows the same input values to produce the same ciphertext.
Create a transformation named
credit-card-convergent
which sets the enables the convergent encryption. When you define a transformation, setconvergent=true
.Output:
Add the
credit-card-convergent
to themobile-pay
role.Encode a value using the
credit-card-convergent
transformation.Example output:
Run the command again.
Example output:
The same encrypted value is returned.
»Lookup token
When the transformation is configured with convergent encryption, you can look up the tokenized value (token).
Encode the value using the
credit-card-convergent
transformation with time-to-live (TTL) of 8 hours.Example output:
Notice that the encoded value (token) is longer than the one without TTL.Look up the token for a card number, "5555-6666-7777-8888".
Output:
Now, look up with expiration of "any".
Output:
This returns two tokens. In absence of the "expiration" paramter, the command returns token with no expiration. When the expiration is set to "any", it returns tokens with any expiration.
Look up token that are expiration between a given range using
min_expiration
andmin_expiration
which are RFC3339 formatted time and date.Example:
»Setup external token storage
Unlike format preserving encryption (FPE) transformation, tokenization is a stateful procedure to facilitate mapping between tokens and various cryptographic values (one way HMAC of the token, encrypted metadata, etc.) including the encrypted plaintext itself which must be persisted.
At scale, this could put a lot of additional load on the Vault's storage backend. To avoid this, you have an option to use external storage to persist data for tokenization transformation.
NOTE: Currently, PostgreSQL and MySQL are supported as external storage for tokenization.
To demonstrate, run a PostgreSQL database in a Docker container. Create a new transformation named, "passport" which uses this PostgreSQL as its storage rather than using the Vault's storage backend.
Run PostgreSQL Docker image in a container.
Start a postgres
instance which listens to port 5432
, and the superuser
(root
) password is set to rootpassword
.
You can verify that the postgres container is running.
Create a new role, "global-id".
Create a store which points to the postgres.
Create a schema in postgres to store tokenization artifacts.
Create a new transformation named, "passport" which points to the postgres store.
Open another terminal and connect to the
postgres
container.Start
psql
.Check to verify that there is no entry.
Return to the terminal you were running Vault CLI, and encode some test data.
Example output:
Return to the postgres container, and check the data entry.
Example output:
As you encode more data, the table entry grows.
Enter
\q
to quit the psql session.Enter
exit
to exit out of the Docker container.
»Summary
Transformation secrets engine introduced tokenization transformation feature which replaces sensitive data with unique value (token) that are unrelated to the original value in any algorithmic sense. This can help organizations to meet certain industry standards.
If retaining the original data format is important, refer to the Transform Secrets Engine to learn about the format preserving encryption (FPE) transformation.