AWS Re:Invent Daily Update Blog - 11/29
Today at AWS re:Invent, AWS introduced a few interesting features for AWS Clean Rooms, a product that helps organizations collaborate without sharing raw data. Additionally, AWS announced improvements to the AWS OpenSearch Service, specifically around its integration with Amazon S3. These announcements could have a significant impact on the way you use those products, so we wanted to dive into them a bit more below.
First, AWS Clean Rooms Differential Privacy was introduced to help protect user privacy. Differential privacy is a mathematical framework for ensuring privacy of individuals in large datasets by introducing a calibrated amount of error ( “noise”) to query results. Additionally, a “privacy budget” is used to ensure that additional queries cannot be run to identify individual user data. With the new feature release, AWS Clean Rooms Differential Privacy brings that capability to AWS Clean Rooms, obfuscating individuals’ data when running queries and performing other data analysis. With just a few steps, this sophisticated functionality can be introduced in your AWS Clean Rooms environments. Additionally, AWS allows you to carefully define your privacy budget and the amount of noise to introduce into your AWS Clean Rooms. This new feature allows you to further tailor to your organizational privacy requirements, especially when dealing with large datasets containing sensitive data.
Additionally, AWS announced AWS Clean Rooms ML, a capability that allows you to apply machine learning models without sharing the underlying raw data. This capability allows you to generate valuable predictive insights without sharing potentially sensitive data. Currently, only lookalike modeling is available. This modeling allows you to apply an AWS managed model to generate lookalike data sets and produce valuable insights. For example, retailers can use AWS Clean Rooms ML to generate insights into potential customers of a new product, allowing for more specific and efficient advertising. Additionally, in the coming months AWS will release a healthcare model, with many more models to come as well. The generated data can also be exported and accessed via the AWS Clean Rooms API, making it available to your applications.
Although both of these AWS Clean Rooms updates are still in a preview stage, each of them can be leveraged by your organization today to increase the value you gain from your AWS Clean Rooms environments.
Amazon announced Amazon OpenSearch Service zero-ETL integration with Amazon S3, allowing for significantly easier evaluation of log data stored in Amazon S3 via the Amazon OpenSearch Service. This new functionality allows you to leverage Amazon OpenSearch Service without managing multiple analytics tools. Also available are log type templates with predefined dashboards to further increase the speed with which logs can be evaluated. This can assist security teams in quickly investigating security incidents and service outages. In addition to viewing data in OpenSearch Dashboards, data can be accessed via the OpenSearch API to make use of the data across other tools as well. Furthermore, measures such as skipping indexes or using aggregations can be used to even further accelerate the data integration. While in a preview stage just like the AWS Clean Rooms features, this new AWS OpenSearch Service capability will allow data and security teams to access and utilize log data stored in Amazon S3 with much less tedious manual configuration.
While these new features sound great, implementing them may be complex. Should you have questions around these products, or any of the other hundreds of updates from AWS re:Invent, please feel free to call ScaleSec for guidance. Additionally, if you are specifically overwhelmed by the plethora of generative AI announcements being made at AWS re:Invent, look no further than our eBook, Maximize Generative AI to Accelerate Your Business Securely.