1. AI Incident Management with Log Analytics Alerts


    Creating an incident management system with log analytics alerts in a cloud environment involves several steps, from creating log analytics solutions that collect and analyze logs, to setting up alert rules that trigger incidents based on certain conditions in the log data. For this example, we will use Azure as the cloud provider, leveraging Azure Log Analytics for collecting and analyzing logs, and Azure Monitor Alert Rules for incident detection.

    In Azure, Log Analytics workspaces are used to collect and aggregate data from various sources. You can then write queries against the data, and set up alert rules to detect and respond to specific conditions. When an alert rule fires, it can trigger actions such as sending emails, firing webhooks, or executing Azure Functions.

    The following Pulumi program in Python sets up a basic incident management system:

    • It creates a Log Analytics Workspace where logs can be collected and analyzed.
    • It sets up an Action Group, which defines the actions to take when an alert condition is met. For the purpose of this example, we'll assume that the action is sending an email notification.
    • It creates an Alert Rule in Azure Monitor, which watches for specific conditions in the log data and triggers the actions defined in the Action Group when the conditions are met.

    Please note that for simplicity, this example doesn't actually specify detailed alert conditions and assumes that the necessary configurations such as the recipient email addresses for the Action Group have been provided. In a real-world scenario, you will need to replace placeholders with actual values and write a detailed Kusto Query Language (KQL) expression for the alert condition.

    Let's proceed with the program:

    import pulumi import pulumi_azure_native as azure_native # Create an Azure Resource Group resource_group = azure_native.resources.ResourceGroup('resource_group') # Create an Azure Log Analytics Workspace log_analytics_workspace = azure_native.operationalinsights.Workspace( 'log_analytics_workspace', resource_group_name=resource_group.name, location=resource_group.location, sku=azure_native.operationalinsights.SkuArgs( name="PerGB2018" ) ) # Create an Action Group to define actions taken when an alert triggers action_group = azure_native.insights.ActionGroup( 'action_group', resource_group_name=resource_group.name, location="Global", group_short_name="IncidentMgmt", enabled=True, email_receivers=[ # Placeholder email receivers, replace with actual email addresses azure_native.insights.EmailReceiverArgs( name="Primary Incident Manager", email_address="incident@manager.com", use_common_alert_schema=True, ) ] ) # Create an Alert Rule for detecting incidents based on log data alert_rule = azure_native.insights.MetricAlertResource( 'alert_rule', resource_group_name=resource_group.name, location="Global", actions=[azure_native.insights.MetricAlertActionArgs( action_group_id=action_group.id )], # This is a placeholder for the actual criteria you would input based on KQL query criteria=azure_native.insights.MetricAlertCriteriaArgs( metric_name="PLACEHOLDER", # Replace with actual metric metric_namespace="PLACEHOLDER", # Replace with actual namespace operator="GreaterThan", threshold=100, time_aggregation="Total", ), description="Alert when log analytics detect incident-worthy events", severity=3, # Severity level of the alert from 0 (critical) to 4 (verbose) enabled=True, scopes=[log_analytics_workspace.id], ) # Export the IDs of the created resources pulumi.export('resource_group_id', resource_group.id) pulumi.export('log_analytics_workspace_id', log_analytics_workspace.id) pulumi.export('action_group_id', action_group.id) pulumi.export('alert_rule_id', alert_rule.id)

    This program performs the following actions:

    1. It starts by importing the required Pulumi modules for Azure.
    2. Next, it defines a new Azure resource group to hold all the resources.
    3. It then creates a Log Analytics Workspace, which is needed to collect and analyze logs from different sources.
    4. An Action Group is defined, which specifies the actions to take when our alert triggers — in this case, sending an email to a designated address.
    5. Then, it sets up a Metric Alert Rule within Azure Monitor. This rule is currently a placeholder and should be replaced with the actual conditions that define an incident in your environment.
    6. Finally, the IDs of the created resources are exported as stack outputs.

    Keep in mind that you will need to replace placeholder values with actual email addresses and criteria to reflect what an incident means for your system. Additionally, you should explore creating more sophisticated alert rules using Kusto Query Language (KQL) based on your specific log analytics needs.