AWS prescriptive-guidance documentation change
Summary
Removed hyperlink from 'Privacy by Design' text reference
Security assessment
Change is purely editorial - removes an external link while keeping the concept reference. No security implications as content about privacy-enhancing operations remains unchanged.
Diff
diff --git a/prescriptive-guidance/latest/privacy-reference-architecture/personal-data-account.md b/prescriptive-guidance/latest/privacy-reference-architecture/personal-data-account.md index 3d6097c89..5b8feb53a 100644 --- a//prescriptive-guidance/latest/privacy-reference-architecture/personal-data-account.md +++ b//prescriptive-guidance/latest/privacy-reference-architecture/personal-data-account.md @@ -160 +160 @@ In the AWS PRA, you can connect the data in the shared Amazon S3 bucket to Amazo -Maintaining datasets that contain personal data is a key component of [Privacy by Design](https://iapp.org/resources/article/privacy-by-design/). An organization's data might exist in structured, semi-structured, or unstructured forms. Personal datasets without structure can make it difficult to perform a number of privacy-enhancing operations, including data minimization, tracking down data attributed to a single data subject as a part of a data subject request, ensuring consistent data quality, and overall segmentation of datasets. [AWS Glue](https://docs.aws.amazon.com/glue/latest/dg/what-is-glue.html) is a fully managed extract, transform, and load (ETL) service. It can help you categorize, clean, enrich, and move data between data stores and data streams. AWS Glue features are designed to help you discover, prepare, structure, and combine datasets for analytics, machine learning, and application development. You can use AWS Glue to create a predictable and common structure on top of your existing datasets. AWS Glue Data Catalog, AWS Glue DataBrew, and AWS Glue Data Quality are AWS Glue features that can help support your organization's privacy requirements. +Maintaining datasets that contain personal data is a key component of Privacy by Design. An organization's data might exist in structured, semi-structured, or unstructured forms. Personal datasets without structure can make it difficult to perform a number of privacy-enhancing operations, including data minimization, tracking down data attributed to a single data subject as a part of a data subject request, ensuring consistent data quality, and overall segmentation of datasets. [AWS Glue](https://docs.aws.amazon.com/glue/latest/dg/what-is-glue.html) is a fully managed extract, transform, and load (ETL) service. It can help you categorize, clean, enrich, and move data between data stores and data streams. AWS Glue features are designed to help you discover, prepare, structure, and combine datasets for analytics, machine learning, and application development. You can use AWS Glue to create a predictable and common structure on top of your existing datasets. AWS Glue Data Catalog, AWS Glue DataBrew, and AWS Glue Data Quality are AWS Glue features that can help support your organization's privacy requirements.