Data protection in AWS Clean Rooms
The AWS shared responsibility model
For data protection purposes, we recommend that you protect AWS account credentials and set up individual users with AWS IAM Identity Center or AWS Identity and Access Management (IAM). That way, each user is given only the permissions necessary to fulfill their job duties. We also recommend that you secure your data in the following ways:
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Use multi-factor authentication (MFA) with each account.
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Use SSL/TLS to communicate with AWS resources. We require TLS 1.2 and recommend TLS 1.3.
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Set up API and user activity logging with AWS CloudTrail. For information about using CloudTrail trails to capture AWS activities, see Working with CloudTrail trails in the AWS CloudTrail User Guide.
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Use AWS encryption solutions, along with all default security controls within AWS services.
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Use advanced managed security services such as Amazon Macie, which assists in discovering and securing sensitive data that is stored in Amazon S3.
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If you require FIPS 140-3 validated cryptographic modules when accessing AWS through a command line interface or an API, use a FIPS endpoint. For more information about the available FIPS endpoints, see Federal Information Processing Standard (FIPS) 140-3
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We strongly recommend that you never put confidential or sensitive information, such as your customers' email addresses, into tags or free-form text fields such as a Name field. This includes when you work with AWS Clean Rooms or other AWS services using the console, API, AWS CLI, or AWS SDKs. Any data that you enter into tags or free-form text fields used for names may be used for billing or diagnostic logs. If you provide a URL to an external server, we strongly recommend that you do not include credentials information in the URL to validate your request to that server.
Encryption at rest
AWS Clean Rooms always encrypts all service metadata at rest without requiring any additional configuration. This encryption is automatic when you use AWS Clean Rooms.
Clean Rooms ML encrypts all data stored within the service at rest with AWS KMS. If you choose to provide your own KMS key, the contents of your lookalike models and lookalike segment generation jobs are encrypted at rest with your KMS key.
When using AWS Clean Rooms custom ML models, the service encrypts all data stored at rest with AWS KMS. AWS Clean Rooms supports the use of symmetric customer managed keys that you create, own, and manage to encrypt data at rest. If customer managed keys are not specified, AWS owned keys are used by default.
AWS Clean Rooms uses grants and key policies to access customer managed keys. You can revoke access to the grant, or remove the service's access to the customer managed key at any time. If you do, AWS Clean Rooms won't be able to access any of the data encrypted by the customer managed key, which affects operations that are dependent on that data. For example, if you attempt to create a trained model from an encrypted ML input channel that AWS Clean Rooms can't access, then the operation would return a ValidationException error.
Note
You can use the encryption options in Amazon S3 to protect your data at rest.
For more information, see Specifying Amazon S3 encryption in the Amazon S3 User Guide.
When using an ID mapping table within AWS Clean Rooms, the service encrypts all data stored at rest with AWS KMS. If you choose to provide your own KMS key, the contents of your ID mapping table are encrypted at rest with your KMS key via AWS Entity Resolution. For more details about the required permissions for working with encryptions with an ID mapping workflow, see Create a workflow job role for AWS Entity Resolution in the AWS Entity Resolution User Guide.
Encryption in transit
AWS Clean Rooms uses Transport Layer Security (TLS) for encryption in transit. Communication with AWS Clean Rooms is always done over HTTPS so your data is always encrypted in transit, regardless of whether it's stored in Amazon S3, Amazon Athena, or Snowflake. This includes all data in transit when using Clean Rooms ML.
Encrypting underlying data
For more information about how to encrypt your underlying data, see Cryptographic Computing for Clean Rooms.
Key policy
Key policies control access to your customer managed key. Every customer managed key must have exactly one key policy, which contains statements that determine who can use the key and how they can use it. When you create your customer managed key, you can specify a key policy. For more information, see Managing access to customer managed keys in the AWS Key Management Service Developer Guide.
To use your customer managed key with your AWS Clean Rooms Custom ML models, the following API operations must be permitted in the key policy:
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kms:DescribeKey– Provides the customer managed key details to allow AWS Clean Rooms to validate the key. -
kms:Decrypt– Provides access to AWS Clean Rooms to decrypt the encrypted data and use it in related jobs. -
kms:CreateGrant- Clean Rooms ML encrypts training and inference images at rest in Amazon ECR by creating grants for Amazon ECR. To learn more, see Encryption at Rest in Amazon ECR. Clean Rooms ML also uses Amazon SageMaker AI to run training and inference jobs, and creates grants for SageMaker AI to encrypt the Amazon EBS volumes attached to the instances as well as the output data in Amazon S3. To learn more, see Protect Data at Rest Using Encryption in Amazon SageMaker AI. -
kms:GenerateDataKey- Clean Rooms ML encrypts data at rest stored in Amazon S3 using server-side encryption with AWS KMS keys. To learn more, see Using server-side encryption with AWS KMS keys (SSE-KMS) in Amazon S3.
The following are policy statement examples you can add for AWS Clean Rooms for the following resources:
ML input channel
{ "Version": "2012-10-17", "Statement": [ { "Sid": "Allow access to principals authorized to use Clean Rooms ML", "Effect": "Allow", "Principal": { "AWS": "arn:aws:iam::444455556666:role/ExampleRole" }, "Action": [ "kms:DescribeKey", "kms:GenerateDataKey", "kms:Decrypt" ], "Resource": "*", "Condition": { "StringEquals": { "kms:ViaService": "cleanrooms-ml.region.amazonaws.com" } } }, { "Sid": "Allow access to Clean Rooms ML service principal", "Effect": "Allow", "Principal": { "Service": "cleanrooms-ml.amazonaws.com" }, "Action": [ "kms:DescribeKey", "kms:GenerateDataKey", "kms:Decrypt" ], "Resource": "*" } ] }
Trained model job or Trained model inference job
{ "Version": "2012-10-17", "Statement": [ { "Sid": "Allow access to principals authorized to use Clean Rooms ML", "Effect": "Allow", "Principal": { "AWS": "arn:aws:iam::444455556666:role/ExampleRole" }, "Action": [ "kms:GenerateDataKey", "kms:DescribeKey", "kms:CreateGrant", "kms:Decrypt" ], "Resource": "*", "Condition": { "StringEquals": { "kms:ViaService": "cleanrooms-ml.region.amazonaws.com" } "ForAllValues:StringEquals": { "kms:GrantOperations": [ "Decrypt", "Encrypt", "GenerateDataKeyWithoutPlaintext", "ReEncryptFrom", "ReEncryptTo", "CreateGrant", "DescribeKey", "RetireGrant", "GenerateDataKey" ] }, "Bool": { "kms:GrantIsForAWSResource": true } } }, { "Sid": "Allow access to Clean Rooms ML service principal", "Effect": "Allow", "Principal": { "Service": "cleanrooms-ml.amazonaws.com" }, "Action": [ "kms:GenerateDataKey", "kms:DescribeKey", "kms:CreateGrant", "kms:Decrypt" ], "Resource": "*", "Condition": { "ForAllValues:StringEquals": { "kms:GrantOperations": [ "Decrypt", "Encrypt", "GenerateDataKeyWithoutPlaintext", "ReEncryptFrom", "ReEncryptTo", "CreateGrant", "DescribeKey", "RetireGrant", "GenerateDataKey" ] } } } ] }
Clean Rooms ML does not support specifying service encryption context or source context in customer managed key policies. Encryption context used by the service internally is visible to customers in CloudTrail.
