Sensitive data exposure prevention encompasses the controls that protect confidential information -- credentials, personal data, financial records, health information, and intellectual property -- from unauthorized disclosure throughout its lifecycle. This includes protection in transit across networks, at rest in storage systems, and in use during processing, as well as preventing accidental exposure through application logic flaws, logging, and error handling.
Data classification establishes what information requires protection and at what level. Encryption in transit uses TLS 1.2 or higher for all communications, with HSTS headers preventing protocol downgrade attacks. Encryption at rest protects stored data through database-level encryption, file system encryption, and application-level field encryption for particularly sensitive values. Key management practices ensure encryption keys are stored separately from encrypted data, rotated regularly, and protected by hardware security modules when warranted. Application-level controls prevent sensitive data from leaking through verbose error messages, debug logs, URL parameters, browser caches, and API responses. Data minimization reduces exposure by collecting and retaining only necessary information. Tokenization replaces sensitive values with non-sensitive tokens for processing scenarios that do not require the original data.
Data breaches exposing sensitive information result in regulatory penalties (GDPR fines reaching 4 percent of global revenue), litigation costs, customer notification expenses, credit monitoring obligations, and lasting reputational damage. Many breaches stem not from sophisticated attacks but from basic failures: unencrypted data transmission, credentials in source code, verbose API responses exposing internal data, and insufficient access controls on data stores.
CDA centers sensitive data protection within the DPS (Data Protection and Sovereignty) domain. Theater missions implement data classification frameworks, deploy encryption controls across all data states, and conduct exposure assessments that identify sensitive data leaking through unexpected channels -- logs, error messages, API over-sharing, and cached responses.