Activity Audit

Activity Audit takes the high-value data from captures and makes it always-on, searchable, and indexed against your cloud-native assets. This stream includes executed commands, network activity, file activity, and kube exec requests to the Kubernetes API.

Understanding How Activity Audit is Used

Activity Audit allows users to view different data sources in-depth for monitoring, troubleshooting, diagnostics, or to meet regulatory controls.

Using to Investigate Events

A system investigation may be triggered by an event generated by Sysdig, or by an alert from another tool or person.

• Find contextualized, relevant data Activity Audit allows easy access to the underlying data to help trace the event, evaluate its impact, and resolve the issue.

  From Policy Events in Sysdig Secure, jump directly to the relevant Activity Audit to investigate details.

  

• Trace commands and connections back to users Activity Audit can correlate the interactive requests from a Kubernetes user with the commands and network connections performed inside the container, allowing an operator to trace this activity back to a user identity.

Using for Regulatory Audits

The Activity Audit can also provide data about the infrastructure to help prove to auditors that proper data visibility and security measures are in place

Activity Audit is a critical requisite for many compliance standards:

• SOC2

• PCI

• HIPAA

• NIST 800-53

Navigate the Audit Interface

Activity Audit displays a continuously updated list of activities. Use the UI features to find and filter the information you need.

Select Investigate > Activity Audit to access.

Filtering

Filtering is the heart of Activity Audit’s power. Filters allow you to search, sort, parse, and surface meaningful data and connections as they are needed.

Ways to filter Activity Data:

• Scope: Reduce the scope of your investigation by focusing on a more specific area of your infrastructure. By default, your scope will be set to Everywhere, unless a team scope is defined for your currenly selected team.

• Data Source: Choose a data source from the right side of the graph. Currently available data sources are: network activity, commands, kubectl exec, or file. You can also select more than one source.

• Attribute (=/!=): Choose = or -!= next to an attribute, either from the list or from the detail view, to include/exclude that attribute from the filter

• Attribute (manual): If you know the attribute, you can type it into the filter box manually, with the following syntax:

  Include an attribute

  attribute_name="attribute_value" e.g. comm="grep"

  Exclude an attribute

  attribute_name!="attribute_value" e.g. comm!="grep"

• Trace Trace entries to see all relevant activity in that session from that user. See the Trace Button paragraph for more detailed information.

• Frequency graph: Select a section of the graph to zoom in on a time frame and see detailed activity. More info in the Frequency Graph paragraph

• Combine: These methods can be combined as needed.

  For example, the filter below surfaces activity on a particular pod, while excluding activity from one IP address that is known to be normal.

  resource="pods" name="woocommerce-6877958" sourceaddresses!="172.20.41.2

Frequency Graph

The graph shows the activity frequency for each data source, allowing users easily to zero-in on anomalies.

The image above shows a spike in network activity (purple line) between 12:00 - 3:00 pm.

Drag the mouse over the peak to auto-zoom on the time frame and see more detail.

Data Sources

Use the legend at the right side of the graph to filter information from one particular data set. The currently available data sources are:

• User commands

• Network connections

• Kube exec commands

• File activities

Time Navigation Buttons

Use the time window navigation bar to show only activities run within that window.

Activity Row and Details

Select an activity row to see its details on the right panel, including all the collected attributes. See Review Activity Details for the attributes of each data source. Some attributes allow you to quickly add filters including = or excluding != such a value.

You can also perform quick filtering by selecting attribute and filter type directly from the activity row.

Trace Button for kube exec and Shell Activities

Beside each activity originating a trace there is a Trace button

. Such a button is available only for the following events:

• kube exec
• kube attach
• ssh
• dropbear
• shells (*sh)

This feature allows you to correlate activities from the originator to each single performed operation. See Follow a kubectl exec Trace use case to see it in action.

This button does not appear if you are running on a GKE cluster.

kubectl run

The Kubernetes event in the activity audit list labeled kube run is received from the AC in the following cases:

• A kubectl run is performed
• A kubectl attach is performed

Review Activity Details

Command Details

Name	Description
Time	The date and time the command was executed.
Command	The command executed.
Full Command Line	The complete command, including all variables/options.
Working Directory	The directory the command was executed in.
Scope	The entities within the infrastructure impacted by the command, including Kubernetes namespace name Kubernetes deployment name Kubernetes cluster name Kubernetes pod name
Host	The hostname and MAC address of the host the command was executed on.
Additional Details	Detailed user/host information: The Process ID (PID) of the command. The Parent Process ID (PPID) of the command. The user ID of the user that executed the command. The Shell ID.

Network Connection Details

Name	Description
Time	The date and time of the network connection
Connection Direction	Incoming or outgoing connection
Connection Details	Including: Transport-level protocol (lp4) Client address, server address (lp4) Client port, server port
Scope	The entities impacted by the network connection, including Kubernetes namespace name Kubernetes cluster name Kubernetes pod name
Host	The host name and MAC address of the host where the connection was made
Additional Details	The process name and ID (Parent Process ID/PID) that launched or received the network connection

Kubectl Exec Details

Name	Description
Time	The date and time of the kubectl command
Kubernetes resource	Including: resource: The kind of Kubernetes resource affected (currently only pods) name: name of the resource (pod name) subresource: currently exec command: the command executed container: the high-level name in the Kubernetes defintion
Kubernetes user and group	Including: user: user name performing the kubectl command. Can be either a service account or a human user. groups: groups the user belongs to userAgent: client userAgent
Sources addresses	External IP address that initiated the connection
Scope	Including Kubernetes namespace name Kubernetes deployment name Kubernetes cluster name Kubernetes pod name
Host	Host name and MAC address of the host where the kubectl exec was made

File Activity Details

Name	Description
Time	Date and time the file was modified
File access details	- File name - File directory - Command used to access the file - Access mode
Scope	Entities impacted by the file activity, including Kubernetes namespace name Kubernetes deployment name Kubernetes cluster name Kubernetes pod name
Host	The host name and MAC address of the host where the file activity occurred

Sample Use Cases

Look for Suspicious Commands

During an investigation you usually need to find suspicious activities among the most normal and recurring ones.

In this example, we have a ps command being executed very frequently, being noisy and making investigations harder.

In order facilitate focusing on other, more suspicious commands, filters can be used to reduce noise and make the investigation easier.

In this case, quick filters can be used to exclude the ps command, so that other more interesting commands emerge instantly.

Filtering for Incident Response

A Policy Event reports a dangerous peak in network connections coming from a specific pod. This example describes one way to search for the root cause.

What user and what activity triggered this issue?

1. Use the Respond button next to the policy event to jump directly to the relevant data.

   

2. Here one can determine at a glance:

   • The pod/namespace on which the heightened activity is occurring

   • The process related to the activity (in this case, ab, or the Apache Benchmark tool)

   • Related activities in the graph (cmd and kube exec lines)

   • Repetitive entries that can be screened out

3. Refine the view through filtering:

   1. Switch from network data source to cmd and kube exe.

   2. Filter out noisy, repetitive entries (e.g. comm!="bash")

   3. Investigate details of a kube exec item for user information.

   

4. After filtering, you have a focused incident report detailing:

   • The Kubernetes user “johndoe”

   • The external IP he used to connect

   • The set of commands he used to install and launch the Apache Benchmark stress-testing tool.

Follow a kubectl exec Trace

In a production environment, kubectl exec commands are typically suspicious. Also, because such commands are interactive sessions, it can be difficult to pinpoint which individual has issued the command(s) and what other activities the individual performed. This is where Sysdig’s Trace functionality comes in, correlating kubectl exec commands with a specific user and the network and command activities performed in that user’s session.

In this example, suspicious activity has been detected and you want to determine whether someone has downloaded and executed a Trojan horse.

1. Use the Groups to display your Kubernetes hierarchy by namespace and deployment. Focus on the pod displaying unexpected high levels of activity (based on the number in parentheses).

2. Checking the corresponding activity graph, you zero in on a time frame and see kube exec activity among the hundreds of commands and network events.

   

3. Select the kube exec item and click the Trace button on left.

   This session trace will display a formatted report of any container activity (network, commands) that the user performed inside the container.