AWS Announces Amazon Security Lake
At AWS re:Invent, Amazon Web Services, Inc. (AWS), an Amazon.com, Inc. company (NASDAQ: AMZN), today announced Amazon Security Lake, a service that automatically centralizes an organization’s security data from cloud and on-premises sources into a purpose-built data lake in a customer’s AWS account so customers can act on security data faster. Amazon Security Lake manages data throughout its lifecycle with customizable data retention settings, converts incoming security data to the efficient Apache Parquet format, and conforms it to the Open Cybersecurity Schema Framework (OCSF) open standard to make it easier to automatically normalize security data from AWS and combine it with dozens of pre-integrated third-party enterprise security data sources. Security analysts and engineers can use Amazon Security Lake to aggregate, manage, and optimize large volumes of disparate log and event data to enable faster threat detection, investigation, and incident response to effectively address potential issues quickly, while continuing to utilize their preferred analytics tools. To learn more about Amazon Security Lake, visit aws.amazon.com/security-lake.
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Customers want greater visibility into security activity across their entire organizations to proactively identify potential threats and vulnerabilities, assess security alerts, respond accordingly, and help prevent future security events. To do this, most organizations rely on log and event data from many different sources (e.g., applications, firewalls, and identity systems) running in the cloud and on premises, each using a unique and often incompatible data format. To uncover security-related insights, like spotting unauthorized external data transfers for sensitive information or identifying the installation of malware across employee devices, organizations must first aggregate and normalize all this data into a consistent format. Once the data is formatted consistently, customers can analyze it and understand the current level of vulnerability, and then correlate and monitor threats for improved observability. Customers typically use different security solutions to address specific use cases, such as incident response and security analytics, which often means they duplicate and process the same data multiple times because each solution has its own data stores and format. This is time consuming and costly, slowing down security teams' ability to detect and respond to issues. As customers add new users, tools, and data sources, security teams must also spend time managing a complex set of data-access rules and security policies to track how data is used and ensure people can get the information they need. Some security teams create a central repository for all their security data in a data lake, but these systems require specialized skills and can take months to build due to the large amount of log data from different sources, which can run into petabyte scale.
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