A data lake is a repository that stores large volumes of raw data in its original, native format until teams need it for analysis. Unlike a traditional database or warehouse, it does not force every record into a fixed schema before storage. That makes it useful for logs, network telemetry, endpoint events, cloud audit trails, and other high-volume security data.
In cyber security, data lakes matter because defenders can keep broad visibility without immediately normalizing every source. SOC and detection engineering teams use them for hunting, correlation, retrospective analysis, and building analytics across hybrid or multi-cloud environments. The tradeoff is governance: without strong access control, retention rules, metadata, and validation, a data lake can become hard to search, expensive to manage, and risky if sensitive data is exposed. Attackers also target poorly secured data lakes because they may contain identity records, secrets, or complete activity history. Strong permissions and audit logging are essential.