I'm often asked to share my thoughts about protecting sensitive data. The questions that typically come up include:
- Which types of data should be protected?
- Which data should be classified as "sensitive?"
- Where is this sensitive data located?
- Which groups of users should have access to this data?
Because these questions come up frequently, it seems ideal to share a few guidelines on this topic.
When protecting the confidentiality and integrity of data, the first level of defense is Authentication and access control. However, data with higher levels of sensitivity or confidentiality may require additional levels of protection, beyond regular authentication and authorization methods.
There are a number of control methods for securing sensitive data available in the market today, including:
- Persistent (Static) Data Masking
- Dynamic Data Masking
- Retention management and purging
Encryption is a cryptographic method of encoding data. There are generally, two methods of encryption: symmetric (using single secret key) and asymmetric (using public and private keys). Although there are methods of deciphering encrypted information without possessing the key, a good encryption algorithm makes it very difficult to decode the encrypted data without knowledge of the key. Key management is usually a key concern with this method of control. Encryption is ideal for mass protection of data (e.g. an entire data file, table, partition, etc.) against unauthorized users.
Persistent or static data masking obfuscates data at rest in storage. There is usually no way to retrieve the original data – the data is permanently masked. There are multiple techniques for masking data, including: shuffling, substitution, aging, encryption, domain-specific masking (e.g. email address, IP address, credit card, etc.), dictionary lookup, randomization, etc. Depending on the technique, there may be ways to perform reverse masking - this should be used sparingly. Persistent masking is ideal for cases where all users should not see the original sensitive data (e.g. for test / development environments) and field level data protection is required.
Dynamic data masking de-identifies data when it is accessed. The original data is still stored in the database. Dynamic data masking (DDM) acts as a proxy between the application and database and rewrites the user / application request against the database depending on whether the user has the privilege to view the data or not. If the requested data is not sensitive or the user is a privileged user who has the permission to access the sensitive data, then the DDM proxy passes the request to the database without modification, and the result set is returned to the user in the clear. If the data is sensitive and the user does not have the privilege to view the data, then the DDM proxy rewrites the request to include a masking function and passes the request to the database to execute. The result is returned to the user with the sensitive data masked. Dynamic data masking is ideal for protecting sensitive fields in production systems where application changes are difficult or disruptive to implement and performance / response time is of high importance.
Tokenization substitutes a sensitive data element with a non-sensitive data element or token. The first generation tokenization system requires a token server and a database to store the original sensitive data. The mapping from the clear text to the token makes it very difficult to reverse the token back to the original data without the token system. The existence of a token server and database storing the original sensitive data renders the token server and mapping database as a potential point of security vulnerability, bottleneck for scalability, and single point of failure. Next generation tokenization systems have addressed these weaknesses. However, tokenization does require changes to the application layer to tokenize and detokenize when the sensitive data is accessed. Tokenization can be used in production systems to protect sensitive data at rest in the database store, when changes to the application layer can be made relatively easily to perform the tokenization / detokenization operations.
Retention management and purging is more of a data management method to ensure that data is retained only as long as necessary. The best method of reducing data privacy risk is to eliminate the sensitive data. Therefore, appropriate retention, archiving, and purging policies should be applied to reduce the privacy and legal risks of holding on to sensitive data for too long. Retention management and purging is a data management best practices that should always be put to use.