Data security can be implemented using a variety of different methods, each with their own advantages and disadvantages. Some of the most common methods for data protection include: Minimization: Data minimization is an important first step for any data security policy. By limiting data collection and storage to only what is necessary, an organization limits its data security risk and the volume of data that it needs to protect. Encryption: Data encryption ensures that only those with access to the secret key have the ability to decrypt and read the data, making it useless to an attacker. This is a strong protection for data that is in storage or being transmitted; however, encryption cannot currently be used effectively for data in active use. Masking: Data masking replaces some portion of sensitive data - like the first 12 digits of a credit card number or the first 5 digits of a Social Security Number - with a masking character (often an asterisk). This limits the utility of the sensitive data if it is accidentally exposed to an unauthorized party. Tokenization: Data tokenization replaces a piece of sensitive data with a unique identifier (or “token”) and uses that token in place of the data. The lookup table that maps these tokens to the real data is stored in a protected location, minimizing its probability of exposure. Without access to this table, it is much more difficult (and potentially impossible) to determine the real value associated with a particular token. Erasure: Erasure is the most effective method of securing sensitive data. If an organization securely deletes data from its systems when it is no longer necessary, there is no need to protect the data. All of these methods of data protection are largely passive. While they can be quite effective, the potential exists that an attacker can bypass or overcome them. For example, an attacker may gain access to the decryption keys for encrypted data or the lookup table mapping tokenized data to real values. Organizations also need to implement active protections for the sensitive data entrusted to them. Some examples of active data security solutions include: Access Controls: Limiting access to sensitive data is an essential part of protecting it against abuse or exposure. Access controls should be designed based on the principle of least privilege, which states that a user or application should only have the access and permissions needed to do their job and no more. This limits the impact of a compromised user account or application on enterprise data security. Data Monitoring: Access controls protect against unauthorized access to data, but insider risks are a significant data security risk. An organization should have solutions in place that monitor access to and usage of an organization’s data and alert security personnel of activities that would place data at risk. An effective data security strategy implements both passive and active data security controls. For example, encryption of data at rest and in transit ensures that the data is safe most of the time. The organization can then focus its active protections on when data is “at risk”, when it is decrypted and in active use.