Put Data Protection Policy

cloudwatchlogs_put_data_protection_policy

R Documentation

Creates a data protection policy for the specified log group

Description

Creates a data protection policy for the specified log group. A data protection policy can help safeguard sensitive data that's ingested by the log group by auditing and masking the sensitive log data.

Sensitive data is detected and masked when it is ingested into the log group. When you set a data protection policy, log events ingested into the log group before that time are not masked.

By default, when a user views a log event that includes masked data, the sensitive data is replaced by asterisks. A user who has the logs:Unmask permission can use a get_log_events or filter_log_events operation with the unmask parameter set to true to view the unmasked log events. Users with the logs:Unmask can also view unmasked data in the CloudWatch Logs console by running a CloudWatch Logs Insights query with the unmask query command.

For more information, including a list of types of data that can be audited and masked, see Protect sensitive log data with masking.

The put_data_protection_policy operation applies to only the specified log group. You can also use put_account_policy to create an account-level data protection policy that applies to all log groups in the account, including both existing log groups and log groups that are created level. If a log group has its own data protection policy and the account also has an account-level data protection policy, then the two policies are cumulative. Any sensitive term specified in either policy is masked.

Usage

cloudwatchlogs_put_data_protection_policy(logGroupIdentifier,
  policyDocument)

Arguments

logGroupIdentifier

[required] Specify either the log group name or log group ARN.

policyDocument

[required] Specify the data protection policy, in JSON. This policy must include two JSON blocks: The first block must include both a DataIdentifer array and an Operation property with an Audit action. The DataIdentifer array lists the types of sensitive data that you want to mask. For more information about the available options, see Types of data that you can mask. The Operation property with an Audit action is required to find the sensitive data terms. This Audit action must contain a FindingsDestination object. You can optionally use that FindingsDestination object to list one or more destinations to send audit findings to. If you specify destinations such as log groups, Firehose streams, and S3 buckets, they must already exist. The second block must include both a DataIdentifer array and an Operation property with an Deidentify action. The DataIdentifer array must exactly match the DataIdentifer array in the first block of the policy. The Operation property with the Deidentify action is what actually masks the data, and it must contain the "MaskConfig": {} object. The "MaskConfig": {} object must be empty. For an example data protection policy, see the Examples section on this page. The contents of the two DataIdentifer arrays must match exactly. In addition to the two JSON blocks, the policyDocument can also include Name, Description, and Version fields. The Name is used as a dimension when CloudWatch Logs reports audit findings metrics to CloudWatch. The JSON specified in policyDocument can be up to 30,720 characters.

Value

A list with the following syntax:

list(
  logGroupIdentifier = "string",
  policyDocument = "string",
  lastUpdatedTime = 123
)

Request syntax

svc$put_data_protection_policy(
  logGroupIdentifier = "string",
  policyDocument = "string"
)