Bringing Your Cloud- Managed Kubernetes Audit Logs into Elasticsearch

Introduction:

Kubernetes audit logs are essential for ensuring the security, compliance, and transparency of Kubernetes clusters. However, with managed Kubernetes infrastructure, traditional audit file-based log shipping is often not supported, and audit logs are only available via the control plane API or the Cloud Provider logging facility. In this blog, we will show you how to ingest the audit logs from these other sources and still take advantage of the Elastic Kubernetes Audit Log Integration.

In this blog we will be focusing on AWS as our cloud provider and when ingesting logs from AWS you have several options:

• AWS Custom Logs integration (which we will utilize in this blog)
• AWS Firehose to send logs from Cloudwatch to Elastic
• AWS General integration which supports many AWS sources

In part 1 of this two-part series, we will focus on properly ingesting Kubernetes Audit, and part 2 will focus on investigation, analytics, and alerting.

Kubernetes auditing documentation describes the need for auditing in order to get answers to the questions below:

• What happened?
• When did it happen?
• Who initiated it?
• What resource did it occur on?
• Where was it observed?
• From where was it initiated (Source IP)?
• Where was it going (Destination IP)?

Answers to the above questions become important when an incident occurs and an investigation follows. Alternatively, it could just be a log retention use case for a regulated company trying to fulfill compliance requirements. 

We are giving special importance to audit logs in Kubernetes because audit logs are not enabled by default. Audit logs can take up a large amount of memory and storage. So, usually, it’s a balance between retaining/investigating audit logs against giving up resources budgeted otherwise for workloads to be hosted on the Kubernetes cluster. Another reason we’re talking about audit logs in Kubernetes is that, unlike usual container logs, after being turned on, these logs are orchestrated to write to the cloud provider’s logging service. This is true for most cloud providers because the Kubernetes control plane is managed by the cloud providers. It makes sense for cloud providers to use their built-in orchestration workflows involving the control plane for a managed service backed by their implementation of a logging framework.

Kubernetes audit logs can be quite verbose by default. Hence, it becomes important to selectively choose how much logging needs to be done so that all the audit requirements are met for the organization. This is done in the audit policy file. The audit policy file is submitted against the

In this blog we will be using Elastic Kubernetes Service (Amazon EKS) on AWS with the Kubernetes Audit Logs that are automatically shipped to AWS CloudWatch.

A sample audit log for a secret by the name “empty-secret” created by an admin user on EKS  is logged on AWS CloudWatch in the following format: 

Once the audit logs show up on CloudWatch, it is time to consider how to transfer them to Elasticsearch. Elasticsearch is a great platform for creating dashboards that visualize different audit events recorded in a Kubernetes cluster. It is also a powerful tool for analyzing various audit events. For example, how many secret object creation attempts were made in an hour? 

Now that we established the Kubernetes audit logs are being logged in CloudWatch, let’s discuss how to get the logs ingested into Elasticsearch. Elasticsearch has an integration to consume logs written on CloudWatch. Just using this integration by default is going to get the JSON from CloudWatch as is i.e. the real audit log JSON is nested inside the wrapper CloudWatch JSON. When bringing logs to Elasticsearch, it is important that we use the Elastic Common Schema(ECS) to get the best search and analytics performance. This means that there needs to  be an ingest pipeline that parses a standard Kubernetes audit JSON message and creates an ECS Compliant document in Elasticsearch. Let’s dive into how to achieve this.

Elasticsearch has a Kubernetes integration using Elastic Agent to consume Kubernetes container logs from the console and audit logs written to a file path. For a cloud-provider use case, as described above, it may not be feasible to write audit logs to a path on the Kubernetes cluster. So, how do we leverage the ECS designed for parsing the Kubernetes audit logs already implemented in the Kubernetes integration to work on the CloudWatch audit logs? That is the most exciting plumbing piece! Let’s see how to do it.

What we’re going to do is:

• Read the Kubernetes audit logs from the cloud provider’s logging module, in our case, AWS CloudWatch since this is where logs reside. We will use Elastic Agent and Elasticsearch AWS Custom Logs integration to read from logs from CloudWatch. Note: please be aware, there are several Elastic AWS integration, we are specifically using the AWS Custom Logs integration.

• Create two simple ingest pipelines (we do this for best practices of isolation and composability) 

• The first pipeline looks for Kubernetes audit JSON messages and then redirects them to the second pipeline

• The second custom pipeline will associate the JSON

  message
  field with the correct field expected by the Elasticsearch Kubernetes Audit managed pipeline (aka the Integration) and then
  reroute
  the message to the correct data stream,
  kubernetes.audit_logs-default,
  which in turn applies all the proper mapping and ingest pipelines for the incoming message

• The overall flow will be

1. Create an AWS CloudWatch integration:

a.  Populate the AWS access key and secret pair values

b. In the logs section, populate the log ARN, Tags and Preserve the original event if you want to, and then Save this integration and exit from the page

2. Next, we will configure the custom ingest pipeline

We are doing this because we want to override what the generic managed pipeline does. We will retrieve the custom component name by searching for managed pipeline created as an asset when we install the AWS CloudWatch integration. In this case we will be adding the custom ingest pipeline

From the Dev tools console, run below. Here, we are extracting the message field from the CloudWatch JSON and putting the value in a field called kubernetes.audit. Then, we are rerouting this message to the default Kubernetes audit dataset or ECS that comes with Kubernetes integration

PUT _ingest/pipeline/logs-aws_logs.generic@custom
{
    "processors": [
      {
        "pipeline": {
          "if": "ctx.message.contains('audit.k8s.io')",
          "name": "logs-aws-process-k8s-audit"
        }
      }
    ]
}

PUT _ingest/pipeline/logs-aws-process-k8s-audit
{
  "processors": [
    {
      "json": {
        "field": "message",
        "target_field": "kubernetes.audit"
      }
    },
    {
      "remove": {
        "field": "message"
      }
    },
    {
      "reroute": {
        "dataset": "kubernetes.audit_logs",
        "namespace": "default"
      }
    }
  ]
}

Let’s understand this further:

• When we create a Kubernetes integration, we get a managed index template called

  logs-kubernetes.audit_logs
  that writes to the pipeline called
  logs-kubernetes.audit_logs-1.62.2
  by default

• If we look into the pipeline

  logs-kubernetes.audit_logs-1.62.2
  , we see that all the processor logic is working against the field
  kubernetes.audit
  . This is the reason why our json processor in the above code snippet is creating a field called
  kubernetes.audit
  before dropping the original message field and rerouting. Rerouting is directed to the
  kubernetes.audit_logs
  dataset that backs the
  logs-kubernetes.audit_logs-1.62.2
  pipeline (dataset name is derived from the pipeline name convention that’s in the format
  logs-<datasetname>-version
  )

3. Now let’s verify that the logs are actually flowing through and the audit message is being parsed

a. We will use Elastic Agent and enroll using Fleet and the integration policy we created in the Step 1. There are a number of ways to deploy Elastic Agent and for this exercise we will deploy using docker which is quick and easy.

% docker run --env FLEET_ENROLL=1 --env FLEET_URL=<<fleet_URL>> --env FLEET_ENROLLMENT_TOKEN=<<fleet_enrollment_token>>  --rm docker.elastic.co/beats/elastic-agent:8.15.3

b. Check the messages in Discover. In 8.15 there is also a new feature called Logs Explorer which provides an ability to see Kubernetes Audit logs (and container logs) with a few clicks (see image below). Voila! We can see the Kubernetes audit messages parsed!

4. Let's do a quick recap of what we did

We configured CloudWatch integration in ElasticSearch to read Kubernetes audit logs from CloudWatch. Then, we created custom ingest pipelines to reroute the audit messages to the correct data stream and all the OOTB mappings and parsing that come with the Kubernetes Audit Logs integration. 

In the next part, we’ll look at how to analyze the ingested Kubernetes Audit log data.