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Configuring the Sensor

Common configuration

Refer to the separate guide for security-related configuration options.

We use Helm for managing the deployment of Soveren Sensors. Refer to our Helm chart for all values that can be tuned up for the Soveren Sensor.

To customize values sent to your Soveren Sensor, you need to create the values.yaml file in the folder that you use for custom Helm configuration.

Don't forget to run a helm upgrade command after you've updated the values.yaml file, providing the -f path_to/values.yaml as a command line option (see the updating guide).

Only use values.yaml to override specific values!

Avoid using a complete copy of our values.yaml from the repository. This can lead to numerous issues in production that are difficult and time-consuming to resolve.

Sensor token

Use values.yaml

To save you some keystrokes when installing or updating the Sensor, we suggest placing the following snippet into the values.yaml:

  token: <TOKEN>
  token: <TOKEN>

Use unique tokens for different deployments

If you're managing multiple Soveren deployments, please create unique tokens for each one. Using the same token across different deployments can result in data being mixed and lead to interpretation errors that are difficult to track.

The token value is used to send metadata to the Soveren Cloud and to check for over-the-air updates of the detection model.

HashiCorp Vault

You can store the token value in HashiCorp Vault and retrieve it at runtime using various techniques. To do this, set the top-level useVault to true in your values.yaml. Then, establish communication with the Vault and export the necessary environment variables.

An example of how you could implement integration with Vault
useVault: true

  podAnnotations: 'true' soveren-app info secret/data/digger/token 'true'
    # -- Environment variable export template |
      {{ with secret "secret/data/digger/token" -}}
        export SVRN_DIGGER_STATSCLIENT_TOKEN="{{ }}"
      {{- end }}
    # -- Default entrypoint for digger: '/usr/local/bin/digger --config /etc/config.yaml'
    # -- Example for hashcorp/vault:
    command: [ '/bin/bash', '-c' ]
    args: [ 'source /vault/secrets/soverentokens && /usr/local/bin/digger --config /etc/config.yaml' ]

  podAnnotations: 'true' soveren-app debug secret/data/digger/token 'true'
    # -- Environment variable export template |
      {{ with secret "secret/data/digger/token" -}}
      {{- end }}
    # -- Default entrypoint for detection-tool: './'
    # -- Example for hashcorp/vault:
    command: [ '/bin/bash', '-c' ]
    args: [ 'source /vault/secrets/soverentokens && ./' ]

Binding components to nodes

The Soveren Sensor consists of two types of components:

  • Interceptors, which are distributed to each node via DaemonSet. Interceptors are exclusively used by the Data-in-motion (DIM) sensors.

  • Components instantiated only once per cluster via Deployments; these include digger, crawler, kafka, detectionTool and prometheusAgent. These can be thought of as the centralized components.

The centralized components consume a relatively large yet steady amount of resources. Their resource consumption is not significantly affected by variations in traffic volume and patterns. In contrast, the resource requirements for Interceptors can vary depending on traffic.

Given these considerations, it may be beneficial to isolate the centralized components on specific nodes. For example, you might choose nodes that are more focused on infrastructure monitoring rather than on business processes. Alternatively, you could select nodes that offer more resources than the average node.

If you know exactly which nodes host the workloads you wish to monitor with Soveren, you can also limit the deployment of Interceptors to those specific nodes.

First, you'll need to label the nodes that Soveren components will utilize:

kubectl label nodes <your-node-name> nodepool=soveren

After labeling, you have two options for directing the deployment of components: using nodeSelector or affinity.

To use nodeSelector, specify the following for each component you wish to bind to designated nodes:

  nodepool: soveren

To use affinity, specify the following:

      - matchExpressions:
        - key: nodepool
          operator: In
          - soveren

The affinity option is conceptually similar to nodeSelector but allows for a broader set of constraints.


We do not recommend changing the requests values. They are calibrated to ensure the minimum functionality required by the component with the allocated resources.

On the other hand, the limits for different containers can vary significantly and are dependent on the volume of collected data. There is no one-size-fits-all approach to determining them, but it's crucial to monitor actual usage and observe how quickly the data map is constructed by the product. The general trade-off here is: the more resources you allocate, the quicker the map is built.

It's important to note that the Soveren Sensor does not persist any data. It is normal for components to restart and virtual storage to be flushed. The ephemeral-storage values are set to prevent the overuse of virtual disk space.

Container CPU requests CPU limits MEM requests MEM limits Ephemeral storage limits
interceptor 50m 1000m 64Mi 1536Mi 100Mi
rpcapd 100m 250m 64Mi 256Mi 100Mi
digger 100m 1500m 100Mi 768Mi 100Mi
detection-tool 200m 2200m 2252Mi 2764Mi 200Mi
kafka 100m 400m 650Mi 1024Mi 10Gi
kafka-exporter 100m 400m 650Mi 1024Mi 10Gi
prometheus 75m 75m 192Mi 400Mi 100Mi

Pods containing interceptor and rpcapd are deployed as a DaemonSet. To estimate the required resources, you will need to multiply the values by the number of nodes.

Container CPU requests CPU limits MEM requests MEM limits Ephemeral storage limits
crawler 100m 1500m 100Mi 768Mi 100Mi
detection-tool 200m 2200m 2252Mi 4000Mi 200Mi
kafka 100m 400m 650Mi 1024Mi 10Gi
kafka-exporter 100m 400m 650Mi 1024Mi 10Gi
prometheus 75m 75m 192Mi 400Mi 100Mi



In our testing, Kafka was found to be somewhat heap-hungry. That's why we limited the heap usage separately from the main memory usage limits. Here's what is set as the default:

    - name: KAFKA_HEAP_OPTS
      value: -Xmx512m -Xms512m

The rule of thumb is this: if you increased the limits memory value for the kafka container ×N-fold, also increase the heap ×N-fold.

Persistent volume

The Soveren Sensor is designed to avoid persisting any information during runtime or between restarts. All containers are allocated a certain amount of ephemeral-storage to limit potential disk usage. Kafka is a significant consumer of ephemeral-storage as it temporarily holds collected information before further processing by other components.

There may be scenarios where you'd want to use persistentVolume for Kafka. For instance, the disk space might be shared among various workloads running on the same node, and your cloud provider may not differentiate between persistent and ephemeral storage usage.

Enabling persistent volume for Kafka
      # -- Create/use Persistent Volume Claim for server component.
      # -- Uses empty dir if set to false.
      enabled: false
      # -- Array of access modes.
      # -- Must match those of existing PV or dynamic provisioner.
      # -- Ref: [](
        - ReadWriteOnce
      annotations: {}
      storageClass: ""
      # -- Bind the Persistent Volume using labels.
      # -- Must match all labels of the targeted PV.
      matchLabels: {}
      # -- Size of the volume.
      # -- The size should be determined based on the metrics you collect and the retention policy you set.
      size: 10Gi

Local metrics

If you wish to collect metrics from the Soveren Sensor locally and create your own dashboards, follow these steps:

    enabled: "true"
    # -- The name that you want to assign to your local Prometheus
    name: "<PROMETHEUS_NAME>"
    # -- The URL which will be receiving the metrics
    url: "<PROMETHEUS_URL>"

Log level

By default, the log levels for all Soveren Sensor components are set to error. To tailor the verbosity of the logs to your monitoring needs, you can specify different log levels for individual components:

      level: info

You can adjust the log level for all components except Kafka, those are set to info by default.

DIM configuration

Multi-cluster deployment

For each Kubernetes cluster, you'll need a separate DIM sensor. When deploying DIM sensors across multiple clusters, they will be identified by the tokens and names assigned during their creation.

Use a separate sensor for each cluster

There may be instances where you want to automate the naming process for your clusters in Soveren during deployment. In this case, you can specify the following in your values.yaml file:

  clusterName: <NAME>

Here, Soveren will use <NAME> as the cluster's identifier when presenting data map information. If <NAME> isn't specified, Soveren will default to using the Sensor's name defined in the Soveren app.

Namespace filtering

At times, you may want to limit the Soveren Sensor to specific namespaces for monitoring. You can achieve this by either specifying allowed namespaces (the "allow list") or by excluding particular ones (the "exclude list").

The syntax is as follows:

  • If nothing is specified, all namespaces will be monitored.
  • An asterisk (*) represents "everything."
  • action: allow includes the specified namespace for monitoring.
  • action: deny excludes the specified namespace from monitoring.

Here's an example to demonstrate:

        # - namespace: default
        #   action: allow
        # - namespace: kube-system
        #   action: deny
        - namespace: "*"
          action: allow

When defining names, you can use wildcards and globs such as foo*, /dev/sd?, and devspace-[1-9], as defined in the Go path package.

The Sensor's default policy is to work only with explicitly mentioned namespaces, ignoring all others.

End with allow * if you have any deny definitions

If you've included deny definitions in your filter list and want to monitor all other namespaces, make sure to conclude the list with:

      - namespace: "*"
      action: allow

Failing to do so could result in the Sensor not monitoring any namespaces if only deny definitions are present.

Service mesh and encryption

Soveren can monitor connections encrypted through service meshes like Linkerd or Istio.

The Sensor will automatically detect if a service mesh is deployed on the node. Fine-tuning is only necessary if your mesh implementation uses non-standard ports.

For instance, with Linkerd, you may need to include the following in your values.yaml:

    # if the port of Linkerd differs from the default (4140)
      linkerdPort: <PORT>


You can adjust the update strategy of the DaemonSet:

    type: RollingUpdate
      maxUnavailable: 1

DAR configuration


We recommend creating a separate sensor for each type of asset that you want to monitor. For example, one sensor for S3 buckets, one for Kafka, and one for each database type.

We recommend using a separate sensor for each asset type

You can also have multiple sensors covering the same type of asset, for performance reasons. While it is possible to use one sensor for all types, this approach can complicate the resolution of potential performance bottlenecks and other issues.

S3 buckets

To enable S3 bucket discovery and scanning, you must provide the sensor with credentials for access. This can be done either directly by providing an access key or by configuring a specific role that the sensor will assume at runtime.

The S3 scanning configuration
      enabled: true
      accessKeyId: "<YOUR S3 ACCESS KEY ID>"
      secretAccessKey: "<YOUR S3 ACCESS KEY>"
        # -- Assume the role to access S3 storage
        enabled: false
        # -- The Amazon Resource Name (ARN) of the role to assume.
        rolearn: ""
        # -- The duration of the role session.
        # -- Min: 15 minutes.
        # -- Max: max session duration set for the role in the IAM.
        # -- If you specify a value higher than Max, the operation fails.
        duration: 15m0s # Duration


To enable Kafka scanning, you must provide the sensor with the instance name and address, as well as the necessary access credentials.

The Kafka scanning configuration
      enabled: true
        # -- Name of the Kafka instance
        - instancename: "<YOUR KAFKA INSTANCE NAME>"
          # -- Kafka broker network addresses
          tls: false
            # -- Skip server certificate verification
            insecureskipverify: true
          sasl: false
          user: "<YOUR SASL USER>"
          password: "<YOUR SASL PASSWORD>"


To enable database scanning, you must provide the sensor with the instance name and the connection string containing necessary access credentials.

The database scanning configuration
        enabled: true
            # -- postgresql://[user[:password]@][netloc][:port][/dbname][?param1=value1&...]