Not to be confused with Vault tokens, Tokenization exchanges a sensitive value for an unrelated value called a token. The original sensitive value cannot be recovered from a token alone, they are irreversible. Instead, unlike format preserving encryption, tokenization is stateful. To decode the original value, the token must be submitted to Vault where it is retrieved from a cryptographic mapping in storage.
On encode, Vault generates a random, signed token and stores a mapping of a
version of that token to encrypted versions of the plaintext and metadata, as
well as a fingerprint of the original plaintext which facilitates the
endpoint that lets one query whether a plaintext exists in the system.
Depending on the mapping mode, the plaintext may be decoded only with possession of the distributed token, or may be recoverable in the export operation. See Security Considerations for more.
By default, tokenization produces a unique token for every encode operation. This makes the resulting token fully independent of its plaintext and expiration. Sometimes, though, it may be beneficial if the tokenization of a plaintext/expiration pair tokenizes consistently to the same value. For example if one wants to do a statistical analysis of the tokens as they relate to some other field in a database (without decoding the token), or if one needed to tokenize in two different systems but be able relate the results. In this case, one can create a tokenization transformation that is convergent.
When enabled at transformation creation time, Vault alters the calculation so that encoding a plaintext and expiration tokenizes to the same value every time, and storage keeps only a single entry of that token. Like the exportable mapping mode, convergence should only be enabled if needed. Convergent tokenization has a small performance penalty in external stores and a larger one in the built in store due to the need to avoid duplicate entries and to update metadata when convergently encoding. It is recommended that if one has some use cases that require convergence and some that do not, one should create two different tokenization transforms with convergence enabled on only one.
Some use cases may want to lookup the value of a token given its plaintext. Ordinarily this is contrary to the nature of tokenization where we want to prevent the ability of an attacker to determine that a token corresponds to a plaintext value (a known plaintext attack). But for use cases that require it, the token lookup operation is supported, but only in some configurations of the tokenization transformation. Token lookup is supported when convergence is enabled, or if the mapping mode is exportable and the storage backend is external.
Builtin (Internal) Store
As tokenization is stateful, the encode operation necessarily writes values to storage. By default, that storage is the Vault backend store itself. This differs from some secret engines in that the encode and decode operations require an access of storage per operation. Other engines use storage for configuration but can process operations largely without accessing any storage.
Since these operations involve writes to storage, and therefore must be performed on primary nodes, the scalability of the encode operation is limited by the primary's storage performance.
Additionally, using internal storage, since writes must be performed on primary nodes, the scalability of the encode operation will be limited by the performance of the primary and its storage subsystem. All other operations can be performed on secondaries.
Finally, due to replication, writes to the primary may take some time to reach secondaries, so other read operations like decode or metadata may not succeed on the secondaries until this happens. In other words, tokenization is eventually consistent.
All nodes (except DRs) can participate in all operations using external storage, but one must take care to monitor and scale the external storage for the level of traffic experienced. The storage schema is simple however and well known approaches should be effective.
The goal of Tokenization is to let end users' devices store the token rather than their sensitive values (such as credit card numbers) and still participate in transactions where the token is a stand-in for the sensitive value. For this reason the token Vault generates is completely unrelated (e.g. irreversible) to the sensitive value.
Furthermore, the Tokenization transform is designed to resist a number of attacks on the values produced during encode. In particular it is designed so that attackers cannot recover plaintext even if they steal the tokenization values from Vault itself. In the default mapping mode, even stealing the underlying transform key does not allow them to recover the plaintext without also possessing the encoded token. An attacker must have gotten access to all values in the construct.
exportable mapping mode however, the plaintext values are encrypted
in a way that can be decrypted within Vault. If the attacker possesses the
transform key and the tokenization mapping values, the plaintext can be
recovered. This mode is available for the case where operators prioritize the
ability to export all of the plaintext values in an emergency, via the
Since tokenization isn't format preserving and requires storage, one can associate arbitrary metadata with a token. Metadata is considered less sensitive than the original plaintext value. As it has it's own retrieval endpoint, operators can configure policies that may allow access to the metadata of a token but not its decoded value to enable workflows that operate just on the metadata.
TTLs and Tidying
By default, tokens are long lived, and the storage for them will be maintained indefinitely. Where there is a concept of time-to-live, it is strongly encouraged that the tokens be generated with a TTL. For example, as credit cards have an expiration date, it is recommended that tokenizing a credit card primary account number (PAN) be done with a TTL that corresponds to the time after which the PAN is invalid.
This allows such values to be tidied and removed from storage once expired. Tokens themselves encode the expiration time, so decode and other operations can immediately reject the operation when presented with an expired token.
External SQL Stores
Currently the PostgreSQL, MySQL, and MSSQL relational databases are supported as external storage backends for tokenization. The Schema Endpoint may be used to initialize and upgrade the necessary database tables. Vault uses a schema versioning table to determine if it needs to create or modify the tables when using that endpoint. If you make changes to those tables yourself, the automatic schema management may become out of sync and may fail in the future.
External stores may often be preferred due to their ability to achieve a much higher scale of performance, especially when used with batch operations.
Snapshot allows one to iteratively retrieve the tokenization state, for backup or migration purposes. The resulting data can be fed to the restore endpoint of the same or a different tokenization store. Note that the state is only useable by the tokenization transform that created it, as state is encrypted via keys in that configured transform.
For stores configured with the
exportable mapping mode, the export decoded
endpoint allows operators to retrieve the decoded contents of tokenization
state, which includes tokens and their decoded, sensitive values. The
exportable mode is only recommended if this use case is required, as the default
cannot be decoded by attackers even if they gain access to Vault's storage and
Tokenization stores are configured separately from the tokenization transform, and the transform can point to multiple stores. The primary use case for this one-to-many relationship is to facilitate migration between two tokenization stores.
When multiple stores are configured, Vault writes new tokenization state to all configured stores, and reads from each store in the order they were configured. Thus, one can use multiple configured stores along with the snapshot/restore functionality to perform a zero-downtime migration to a new store:
- Configure the new tokenization store in the API.
- Modify the existing tokenization transform to use both the existing and new store.
- Snapshot the old store.
- Restore the snapshot to the new store.
- Perform any desired validations.
- Modify the tokenization transform to use only the new store.
Tokenization supports key rotation. Keys are tied to transforms, so key
names are the same as the name of the corresponding tokenization transform.
Keys can be rotated to a new version, with backward compatibility for
decoding. Encoding is always performed with the newest key version. Keys versions
can be tidied as well. Keys may also be rotated automatically on a user-defined
time interval, specified by the
auto_rotate_field of the key config. For more
information, see the transform api docs.
Refer to Tokenize Data with Transform Secrets Engine for a step-by-step tutorial.