Sensitive Data Redaction for the Splunk HTTP Event Collector (HEC)

Overview

Sensitive data, such as credit card numbers, bank routing numbers, and API keys, can be revealed unintentionally in your logs, which can expose your organization to financial and privacy risks.

Use the Observability Pipelines to identify, tag, and optionally redact or hash sensitive information before routing logs to different destinations and outside of your infrastructure. You can use out-of-the-box scanning rules to detect common patterns such as email addresses, credit card numbers, API keys, authorization tokens, and more. Or, create custom scanning rules using regex patterns to match sensitive information.

The log sources, processors, and destinations available for this use case

This document walks through the following steps:

  1. The prerequisites needed to set up Observability Pipelines
  2. Setting up Observability Pipelines
  3. Sending logs to the Worker over Splunk HEC

Prerequisites

To use Observability Pipelines’s Splunk HTTP Event Collector (HEC) source, you have applications sending data to Splunk in the expected HEC format.

To use Observability Pipelines’s Splunk HEC destination, you have a Splunk Enterprise or Cloud instance configured with an HTTP Event Collector (HEC) input. You also have the following information available:

  • The Splunk HEC token.
  • The bind address that your Observability Pipelines Worker will listen on to receive logs from your applications. For example, 0.0.0.0:8080. Later on, you configure your applications to send logs to this address.
  • The base URL of the Splunk instance that the Worker will send processed logs to. This URL should include the port that is globally configured for Splunk HTTP Event Collectors on your Splunk instance. For example, for Splunk Cloud: https://prd-p-0mupp.splunkcloud.com:8088.
  • If your HECs are globally configured to enable SSL, then you also need the appropriate TLS certificates and password you used to create your private key file.

See Configure HTTP Event Collector on Splunk Web for more information about setting up Splunk HEC.

Note: Observability Pipelines does not support HEC Indexer Acknowledgement.

Set up Observability Pipelines

  1. Navigate to Observability Pipelines.
  2. Select the Sensitive Data Redaction template to create a new pipeline.
  3. Select Splunk HEC as the source.

Set up the source

Optionally, toggle the switch to enable TLS. If you enable TLS, the following certificate and key files are required:

  • Server Certificate Path: The path to the certificate file that has been signed by your Certificate Authority (CA) Root File in DER or PEM (X.509).
  • CA Certificate Path: The path to the certificate file that is your Certificate Authority (CA) Root File in DER or PEM (X.509).
  • Private Key Path: The path to the .key private key file that belongs to your Server Certificate Path in DER or PEM (PKCS#8) format.

Set up the destination

Enter the following information based on your selected logs destination.

    There are no configuration steps for your Datadog destination.

    The following fields are optional:

    1. Enter the name of the Splunk index you want your data in. This has to be an allowed index for your HEC.
    2. Select whether the timestamp should be auto-extracted. If set to true, Splunk extracts the timestamp from the message with the expected format of yyyy-mm-dd hh:mm:ss.
    3. Set the sourcetype to override Splunk’s default value, which is httpevent for HEC data.

    The following fields are optional:

    1. In the Encoding dropdown menu, select whether you want to encode your pipeline’s output in JSON, Logfmt, or Raw text. If no decoding is selected, the decoding defaults to JSON.
    2. Enter a source name to override the default name value configured for your Sumo Logic collector’s source.
    3. Enter a host name to override the default host value configured for your Sumo Logic collector’s source.
    4. Enter a category name to override the default category value configured for your Sumo Logic collector’s source.
    5. Click Add Header to add any custom header fields and values.

    Set up processors

    There are pre-selected processors added to your processor group out of the box. You can add additional processors or delete any existing ones based on your processing needs.

    Processor groups are executed from top to bottom. The order of the processors is important because logs are checked by each processor, but only logs that match the processor’s filters are processed. To modify the order of the processors, use the drag handle on the top left corner of the processor you want to move.

    Filter query syntax

    Each processor has a corresponding filter query in their fields. Note: Processors only process logs that match their filter query.

    For any attribute, tag, or key:value pair that is not a reserved attribute, your query must start with @. Conversely, to filter reserved attributes, you do not need to append @ in front of your filter query.

    For example, to filter out and drop status:info logs, your filter can be set as NOT (status:info). To filter out and drop system-status:info, your filter must be set as NOT (@system-status:info).

    Filter query examples:

    • NOT (status:debug): This filters for only logs that do not have the status DEBUG.
    • status:ok service:flask-web-app: This filters for all logs with the status OK from your flask-web-app service.
      • This query can also be written as: status:ok AND service:flask-web-app.
    • host:COMP-A9JNGYK OR host:COMP-J58KAS: This filter query only matches logs from the labeled hosts.

    Learn more about writing filter queries in Datadog’s Log Search Syntax.

    Add processors

    Enter the information for the processors you want to use. Click the Add button to add additional processors. To delete a processor, click the kebab on the right side of the processor and select Delete.

    The log processors available

      This processor filters for logs that match the specified filter query and drops all non-matching logs. If a log is dropped at this processor, then none of the processors below this one receives that log. This processor can filter out unnecessary logs, such as debug or warning logs.

      To set up the filter processor:

      • Define a filter query. The query you specify filters for and passes on only logs that match it, dropping all other logs.

      This processor samples your logging traffic for a representative subset at the rate that you define, dropping the remaining logs. As an example, you can use this processor to sample 20% of logs from a noisy non-critical service.

      The sampling only applies to logs that match your filter query and does not impact other logs. If a log is dropped at this processor, none of the processors below receives that log.

      To set up the sample processor:

      1. Define a filter query. Only logs that match the specified filter query are sampled at the specified retention rate below.
      2. Set the retain field with your desired sampling rate expressed as a percentage. For example, entering 1 means 1% of logs is retained out of all the logs that match the filter query.

      The quota processor measures the logging traffic for logs that match the filter you specify. When the configured daily quota is met inside the 24-hour rolling window, the processor can either drop additional logs or send an alert using a Datadog monitor. You can configure the processor to track the total volume or the total number of events.

      As an example, you can configure this processor to drop new logs or trigger an alert without dropping logs after the processor has received 10 million events from a certain service in the last 24 hours.

      To set up the quota processor:

      1. Enter a name for the processor.
      2. Define a filter query. Only logs that match the specified filter query are counted towards the daily limit.
      3. In the Unit for quota dropdown menu, select if you want to measure the quota by the number of Events or by the Volume in bytes.
      4. Set the daily quota limit and select the unit of magnitude for your desired quota.
      5. Check the Drop events checkbox if you want to drop all events when your quota is met. Leave it unchecked if you plan to set up a monitor that sends an alert when the quota is met.

      The dedupe processor removes copies of data to reduce volume and noise. It caches 5000 messages at a time and compares your incoming logs traffic against the cached messages. For example, this processor can be used to keep only unique warning logs in the case where multiple identical warning logs are sent in succession.

      To set up the deduplicate processor:

      1. Define a filter query. Only logs that match the specified filter query are checked for deduplication based on the rules below.
      2. In the Type of deduplication dropdown menu, select whether you want to Match on or Ignore the fields specified below.
        • If Match is selected, then after a log passes through, future logs that have the same values for all of the fields you specify below are removed.
        • If Ignore is selected, then after a log passes through, future logs that have the same values for all of their fields, except the ones you specify below, are removed.
      3. Enter the fields you want to match on, or ignore. At least one field is required, and you can specify a maximum of three fields.
        • Use the path notation <OUTER_FIELD>.<INNER_FIELD> to match subfields. See example.
      4. Click Add field to add additional fields you want to filter on.
      Path notation example

      For the following message structure, use outer_key.inner_key.double_inner_key to refer to the key with the value double_inner_value.

      {
    "outer_key": {
        "inner_key": "inner_value",
            "a": {
                    "double_inner_key": "double_inner_value",
                    "b": "b value"
                },
            "c": "c value"
        },
        "d": "d value"
    }

      The remap processor can add, drop, or rename fields within your individual log data. Use this processor to enrich your logs with additional context, remove low-value fields to reduce volume, and standardize naming across important attributes. Select add field, drop field, or rename field in the dropdown menu to get started.

      Add field

      Use add field to append a new key-value field to your log.

      To set up the add field processor:

      1. Define a filter query. Only logs that match the specified filter query are processed.
      2. Enter the key and value you want to add. To specify a nested field for your key, use the path notation: <OUTER_FIELD>.<INNER_FIELD>. All values are stored as strings.
        Note: If the key you specify already exists, that key’s original value is overwritten.
      Drop field

      Use drop field to drop a field from logging data that matches the filter you specify below.

      To set up the drop field processor:

      1. Define a filter query. Only logs that match the specified filter query are processed.
      2. Enter the key of the field you want to drop. To specify a nested field for your specified key, use the path notation: <OUTER_FIELD>.<INNER_FIELD>.
        Note: If your specified key does not exist, your log will be unimpacted.
      Rename field

      Use rename field to rename a field within your log.

      To set up the rename field processor:

      1. Define a filter query. Only logs that match the specified filter query are processed.
      2. Enter the name of the field you want to rename in the Source key field. To specify a nested field for your key, use the path notation: <OUTER_FIELD>.<INNER_FIELD>. Once renamed, your original field is deleted unless you enable the Preserve source tag checkbox described below.
        Note: If the source key you specify doesn’t exist, a default null value is applied to your target.
      3. In the Target key field, enter the name you want the source field to be renamed to. To specify a nested field for your specified key, use the path notation: <OUTER_FIELD>.<INNER_FIELD>.
        Note: If the target key you specify already exists, then that key’s original value is overwritten.
      4. Optionally, check the Preserve source tag box if you want to retain the original source field and duplicate the information from your source key to your specified target key. If this box is not checked, the source key is dropped after it is renamed.
      Path notation example

      For the following message structure, use outer_key.inner_key.double_inner_key to refer to the key with the value double_inner_value.

      {
    "outer_key": {
        "inner_key": "inner_value",
            "a": {
                    "double_inner_key": "double_inner_value",
                    "b": "b value"
                },
            "c": "c value"
        },
        "d": "d value"
    }

      The Sensitive Data Scanner processor scans logs to detect and redact or hash sensitive information such as PII, PCI, and custom sensitive data. You can pick from our library of predefined rules, or input custom Regex rules to scan for sensitive data.

      To set up the sensitive data scanner processor:

      1. Define a filter query. Only logs that match the specified filter query are scanned and processed.
      2. Click Add Scanning Rule.
      3. Name your scanning rule.
      4. In the Select scanning rule type field, select whether you want to create a rule from the library or create a custom rule.
        • If you are creating a rule from the library, select the library pattern you want to use.
        • If you are creating a custom rule, enter the regex pattern to check against the data.
      5. In the Scan entire or part of event section, select if you want to scan the Entire Event, Specific Attributes, or Exclude Attributes in the dropdown menu.
        • If you selected Specific Attributes, click Add Field and enter the specific attributes you want to scan. You can add up to three fields. Use path notation (outer_key.inner_key) to access nested keys. For specified attributes with nested data, all nested data is scanned.
        • If you selected Exclude Attributes, click Add Field and enter the specific attributes you want to exclude from scanning. You can add up to three fields. Use path notation (outer_key.inner_key) to access nested keys. For specified attributes with nested data, all nested data is excluded.
      6. In the Define action on match section, select the action you want to take for the matched information. Redaction, partial redaction, and hashing are all irreversible actions.
        • If you are redacting the information, specify the text to replace the matched data.
        • If you are partially redacting the information, specify the number of characters you want to redact and whether to apply the partial redaction to the start or the end of your matched data.
        • Note: If you select hashing, the UTF-8 bytes of the match are hashed with the 64-bit fingerprint of FarmHash.
      7. Optionally, add tags to all events that match the regex, so that you can filter, analyze, and alert on the events.

      Install the Observability Pipelines Worker

      1. Select your platform in the Choose your installation platform dropdown menu.

      2. Enter the Splunk HEC address. This is the address and port where your applications are sending their logging data. The Observability Pipelines Worker listens to this address for incoming logs.

      3. Provide the environment variables for each of your selected destinations. See prerequisites for more information.

          There are no environment variables to configure for Datadog Log Management.

          Enter your Splunk HEC token and the base URL of the Splunk instance. See prerequisites for more information.

          The Worker passes the HEC token to the Splunk collection endpoint. After the Observability Pipelines Worker processes the logs, it sends the logs to the specified Splunk instance URL.

          Note: The Splunk HEC destination forwards all logs to the /services/collector/event endpoint regardless of whether you configure your Splunk HEC destination to encode your output in JSON or raw.

          Enter the Sumo Logic HTTP collector URL. See prerequisites for more information.

        • Follow the instructions for your environment to install the Worker.

            1. Click Select API key to choose the Datadog API key you want to use.
            2. Run the command provided in the UI to install the Worker. The command is automatically populated with the environment variables you entered earlier.
              docker run -i -e DD_API_KEY=<DATADOG_API_KEY> \
    -e DD_OP_PIPELINE_ID=<PIPELINE_ID> \
    -e DD_SITE=<DATADOG_SITE> \
    -e <SOURCE_ENV_VARIABLE> \
    -e <DESTINATION_ENV_VARIABLE> \
    -p 8088:8088 \
    datadog/observability-pipelines-worker run
              Note: By default, the docker run command exposes the same port the Worker is listening on. If you want to map the Worker’s container port to a different port on the Docker host, use the -p | --publish option in the command:
              -p 8282:8088 datadog/observability-pipelines-worker run
            3. Navigate back to the Observability Pipelines installation page and click Deploy.

            See Update Existing Pipelines if you want to make changes to your pipeline’s configuration.

            1. Download the Helm chart values file for Amazon EKS.
            2. Click Select API key to choose the Datadog API key you want to use.
            3. Add the Datadog chart repository to Helm:
              helm repo add datadog https://helm.datadoghq.com
              If you already have the Datadog chart repository, run the following command to make sure it is up to date:
              helm repo update
            4. Run the command provided in the UI to install the Worker. The command is automatically populated with the environment variables you entered earlier.
              helm upgrade --install opw \
-f aws_eks.yaml \
--set datadog.apiKey=<DATADOG_API_KEY> \
--set datadog.pipelineId=<PIPELINE_ID> \
--set <SOURCE_ENV_VARIABLES> \
--set <DESTINATION_ENV_VARIABLES> \
--set service.ports[0].protocol=TCP,service.ports[0].port=<SERVICE_PORT>,service.ports[0].targetPort=<TARGET_PORT> \
datadog/observability-pipelines-worker
              Note: By default, the Kubernetes Service maps incoming port <SERVICE_PORT> to the port the Worker is listening on (<TARGET_PORT>). If you want to map the Worker’s pod port to a different incoming port of the Kubernetes Service, use the following service.ports[0].port and service.ports[0].targetPort values in the command:
              --set service.ports[0].protocol=TCP,service.ports[0].port=8088,service.ports[0].targetPort=8282
            5. Navigate back to the Observability Pipelines installation page and click Deploy.

            See Update Existing Pipelines if you want to make changes to your pipeline’s configuration.

            1. Download the Helm chart values file for Azure AKS.
            2. Click Select API key to choose the Datadog API key you want to use.
            3. Add the Datadog chart repository to Helm:
              helm repo add datadog https://helm.datadoghq.com
              If you already have the Datadog chart repository, run the following command to make sure it is up to date:
              helm repo update
            4. Run the command provided in the UI to install the Worker. The command is automatically populated with the environment variables you entered earlier.
              helm upgrade --install opw \
-f azure_aks.yaml \
--set datadog.apiKey=<DATADOG_API_KEY> \
--set datadog.pipelineId=<PIPELINE_ID> \
--set <SOURCE_ENV_VARIABLES> \
--set <DESTINATION_ENV_VARIABLES> \
--set service.ports[0].protocol=TCP,service.ports[0].port=<SERVICE_PORT>,service.ports[0].targetPort=<TARGET_PORT> \
datadog/observability-pipelines-worker
              Note: By default, the Kubernetes Service maps incoming port <SERVICE_PORT> to the port the Worker is listening on (<TARGET_PORT>). If you want to map the Worker’s pod port to a different incoming port of the Kubernetes Service, use the following service.ports[0].port and service.ports[0].targetPort values in the command:
              --set service.ports[0].protocol=TCP,service.ports[0].port=8088,service.ports[0].targetPort=8282
            5. Navigate back to the Observability Pipelines installation page and click Deploy.

            See Update Existing Pipelines if you want to make changes to your pipeline’s configuration.

            1. Download the Helm chart values file for Google GKE.
            2. Click Select API key to choose the Datadog API key you want to use.
            3. Add the Datadog chart repository to Helm:
              helm repo add datadog https://helm.datadoghq.com
              If you already have the Datadog chart repository, run the following command to make sure it is up to date:
              helm repo update
            4. Run the command provided in the UI to install the Worker. The command is automatically populated with the environment variables you entered earlier.
              helm upgrade --install opw \
-f google_gke.yaml \
--set datadog.apiKey=<DATADOG_API_KEY> \
--set datadog.pipelineId=<PIPELINE_ID> \
--set <SOURCE_ENV_VARIABLES> \
--set <DESTINATION_ENV_VARIABLES> \
--set service.ports[0].protocol=TCP,service.ports[0].port=<SERVICE_PORT>,service.ports[0].targetPort=<TARGET_PORT> \
datadog/observability-pipelines-worker
              Note: By default, the Kubernetes Service maps incoming port <SERVICE_PORT> to the port the Worker is listening on (<TARGET_PORT>). If you want to map the Worker’s pod port to a different incoming port of the Kubernetes Service, use the following service.ports[0].port and service.ports[0].targetPort values in the command:
              --set service.ports[0].protocol=TCP,service.ports[0].port=8088,service.ports[0].targetPort=8282
            5. Navigate back to the Observability Pipelines installation page and click Deploy.

            See Update Existing Pipelines if you want to make changes to your pipeline’s configuration.

            1. Click Select API key to choose the Datadog API key you want to use.
            2. Run the one-step command provided in the UI to install the Worker:
              DD_API_KEY=<DATADOG_API_KEY> DD_OP_PIPELINE_ID=<PIPELINE_ID> DD_SITE=<DATADOG_SITE> <SOURCE_ENV_VARIABLES> <DESTINATION_ENV_VARIABLES> bash -c "$(curl -L https://s3.amazonaws.com/dd-agent/scripts/install_script_op_worker2.sh)"

            If you prefer not to use the one-line installation script, follow these step-by-step instructions:

            1. Set up APT transport for downloading using HTTPS:
              sudo apt-get update
sudo apt-get install apt-transport-https curl gnupg
            2. Run the following commands to set up the Datadog deb repo on your system and create a Datadog archive keyring:
              sudo sh -c "echo 'deb [signed-by=/usr/share/keyrings/datadog-archive-keyring.gpg] https://apt.datadoghq.com/ stable observability-pipelines-worker-2' > /etc/apt/sources.list.d/datadog-observability-pipelines-worker.list"
sudo touch /usr/share/keyrings/datadog-archive-keyring.gpg
sudo chmod a+r /usr/share/keyrings/datadog-archive-keyring.gpg
curl https://keys.datadoghq.com/DATADOG_APT_KEY_CURRENT.public | sudo gpg --no-default-keyring --keyring /usr/share/keyrings/datadog-archive-keyring.gpg --import --batch
curl https://keys.datadoghq.com/DATADOG_APT_KEY_F14F620E.public | sudo gpg --no-default-keyring --keyring /usr/share/keyrings/datadog-archive-keyring.gpg --import --batch
curl https://keys.datadoghq.com/DATADOG_APT_KEY_C0962C7D.public | sudo gpg --no-default-keyring --keyring /usr/share/keyrings/datadog-archive-keyring.gpg --import --batch
            3. Run the following commands to update your local apt repo and install the Worker:
              sudo apt-get update
sudo apt-get install observability-pipelines-worker datadog-signing-keys
            4. Add your keys, site (for example, datadoghq.com for US1), source, and destination environment variables to the Worker’s environment file:
              sudo cat <<EOF > /etc/default/observability-pipelines-worker
DD_API_KEY=<DATADOG_API_KEY>
DD_OP_PIPELINE_ID=<PIPELINE_ID>
DD_SITE=<DATADOG_SITE>
<SOURCE_ENV_VARIABLES>
<DESTINATION_ENV_VARIABLES>
EOF
            5. Start the worker:
              sudo systemctl restart observability-pipelines-worker

            See Update Existing Pipelines if you want to make changes to your pipeline’s configuration.

            1. Click Select API key to choose the Datadog API key you want to use.
            2. Run the one-step command provided in the UI to install the Worker:
              DD_API_KEY=<DATADOG_API_KEY> DD_OP_PIPELINE_ID=<PIPELINE_ID> DD_SITE=<DATADOG_SITE> bash -c "$(curl -L https://s3.amazonaws.com/dd-agent/scripts/install_script_op_worker2.sh)"

            If you prefer not to use the one-line installation script, follow these step-by-step instructions:

            1. Set up the Datadog rpm repo on your system with the below command. Note: If you are running RHEL 8.1 or CentOS 8.1, use repo_gpgcheck=0 instead of repo_gpgcheck=1 in the configuration below.
              cat <<EOF > /etc/yum.repos.d/datadog-observability-pipelines-worker.repo
[observability-pipelines-worker]
name = Observability Pipelines Worker
baseurl = https://yum.datadoghq.com/stable/observability-pipelines-worker-2/\$basearch/
enabled=1
gpgcheck=1
repo_gpgcheck=1
gpgkey=https://keys.datadoghq.com/DATADOG_RPM_KEY_CURRENT.public
    https://keys.datadoghq.com/DATADOG_RPM_KEY_B01082D3.public
EOF
            2. Update your packages and install the Worker:
              sudo yum makecache
sudo yum install observability-pipelines-worker
            3. Add your keys, site (for example, datadoghq.com for US1), source, and destination environment variables to the Worker’s environment file:
              sudo cat <<-EOF > /etc/default/observability-pipelines-worker
DD_API_KEY=<API_KEY>
DD_OP_PIPELINE_ID=<PIPELINE_ID>
DD_SITE=<SITE>
<SOURCE_ENV_VARIABLES>
<DESTINATION_ENV_VARIABLES>
EOF
            4. Start the worker:
              sudo systemctl restart observability-pipelines-worker
            5. Navigate back to the Observability Pipelines installation page and click Deploy.

            See Update Existing Pipelines if you want to make changes to your pipeline’s configuration.

            1. Select one of the options in the dropdown to provide the expected log volume for the pipeline:

              OptionDescription
              UnsureUse this option if you are not able to project the log volume or you want to test the Worker. This option provisions the EC2 Auto Scaling group with a maximum of 2 general purpose t4g.large instances.
              1-5 TB/dayThis option provisions the EC2 Auto Scaling group with a maximum of 2 compute optimized instances c6g.large.
              5-10 TB/dayThis option provisions the EC2 Auto Scaling group with a minimum of 2 and a maximum of 5 compute optimized c6g.large instances.
              >10 TB/dayDatadog recommends this option for large-scale production deployments. It provisions the EC2 Auto Scaling group with a minimum of 2 and a maximum of 10 compute optimized c6g.xlarge instances.

              Note: All other parameters are set to reasonable defaults for a Worker deployment, but you can adjust them for your use case as needed in the AWS Console before creating the stack.

            2. Select the AWS region you want to use to install the Worker.

            3. Click Select API key to choose the Datadog API key you want to use.

            4. Click Launch CloudFormation Template to navigate to the AWS Console to review the stack configuration and then launch it. Make sure the CloudFormation parameters are as expected.

            5. Select the VPC and subnet you want to use to install the Worker.

            6. Review and check the necessary permissions checkboxes for IAM. Click Submit to create the stack. CloudFormation handles the installation at this point; the Worker instances are launched, the necessary software is downloaded, and the Worker starts automatically.

            7. Navigate back to the Observability Pipelines installation page and click Deploy.

            See Update Existing Pipelines if you want to make changes to your pipeline’s configuration.

          Send logs to the Observability Pipelines Worker over Splunk HEC

          After you install the Observability Pipelines Worker and deploy the configuration, the Worker exposes three HTTP endpoints that uses the Splunk HEC API:

          • /services/collector/event
          • /services/collector/raw
          • /services/collector/health

          To send logs to your Splunk index, you must point your existing logs upstream to the Worker.

          curl http://<OPW_HOST>:8088/services/collector/event \
	-d '{"event": {"a": "value1", "b": ["value1_1", "value1_2"]}}'

          <OPW_HOST> is the IP/URL of the host (or load balancer) associated with the Observability Pipelines Worker. For CloudFormation installs, the LoadBalancerDNS CloudFormation output has the correct URL to use. For Kubernetes installs, the internal DNS record of the Observability Pipelines Worker service can be used, for example opw-observability-pipelines-worker.default.svc.cluster.local.

          At this point, your logs should be going to the Worker, processed by the pipeline, and delivered to the configured destination.