1. Alert Prioritization for AI Incident Management


    Alert prioritization for AI incident management involves automatically identifying, categorizing, and managing potential incidents in cloud resources to ensure that high-priority incidents are addressed promptly. This involves a combination of proactive monitoring, alert rules, incident management services, and possibly integrating with third-party incident response services like PagerDuty or Opsgenie.

    To implement such a system, you'd use various resources from cloud provider services and incident response tools, and Pulumi can help define and manage these resources as Infrastructure as Code (IaC). Below is an example of how you might use Pulumi to automate alert prioritization for AI incident management in a cloud environment.

    The program will:

    • Use Azure's Security Insights for setting up scheduled alert rules to monitor for specific conditions and generate alerts.
    • Incorporate PagerDuty to handle the incident workflow triggered by the alerts.
    • Define escalation plans using AWS SSM Contacts if required to manage the incident resolution process actively.

    Let's write a sample Pulumi program that sets up some of these components:

    import pulumi import pulumi_azure_native as azure_native import pulumi_pagerduty as pagerduty import pulumi_aws as aws # Configure Azure Security Insights scheduled alert rule scheduled_alert_rule = azure_native.securityinsights.ScheduledAlertRule( "scheduledAlertRule", # Required properties like the resource group name, workspace name, and rule ID would be provided here. # ... display_name="High Priority Alert Rule", severity="High", query="SecurityEvent | where TimeGenerated > ago(1d) and AlertLevel == 'High'", query_frequency="PT5M", # Run the query every 5 minutes query_period="P1D", # Analyze data from the last day enabled=True, suppression_enabled=False, # Do not suppress alerts by default trigger_operator="GreaterThan", trigger_threshold=5, # Trigger the alert if there are more than 5 events matching the criteria ) # Configure PagerDuty to trigger an incident response workflow incident_workflow = pagerduty.IncidentWorkflowTrigger( "incidentWorkflow", services=[pagerduty.Service.get("myService").id], # Replace with your actual service ID workflow="my_workflow", # Replace with your actual workflow ID subscribed_to_all_services=False, type="trigger", ) # If the workflow includes involving a team from AWS SSM Contacts, we would set up an escalation plan. # Assuming you have AWS SSM Contacts already configured with communication channels and contacts, you can set up an escalation plan like so: escalation_plan = aws.ssmcontacts.Plan( "escalationPlan", contact_id=aws.ssmcontacts.Contact.get("myContact").id, # Replace with your actual contact ID stages=[ aws.ssmcontacts.PlanStageArgs( duration_in_minutes=30, targets=[ aws.ssmcontacts.PlanTargetArgs( channel_target_info=aws.ssmcontacts.PlanChannelTargetInfoArgs( contact_channel_id=aws.ssmcontacts.ContactChannel.get("myContactChannel").id, # Replace with your actual contact channel ID retry_interval_in_minutes=10, ), contact_target_info=aws.ssmcontacts.PlanContactTargetInfoArgs( contact_id=aws.ssmcontacts.Contact.get("myContact").id, # Replace with your actual contact ID is_essential=True, ), ), ], ), ], ) # Export the resource names pulumi.export("scheduled_alert_rule_name", scheduled_alert_rule.display_name) pulumi.export("incident_workflow_id", incident_workflow.id) pulumi.export("escalation_plan_name", escalation_plan.name)

    In this program, we’re defining three key resources:

    1. ScheduledAlertRule: This resource represents an alert rule in Azure Security Insights. It specifies the criteria under which alerts will be generated and how often the logs should be evaluated against this rule.

    2. IncidentWorkflowTrigger: A PagerDuty resource that defines the connection between the alerts generated and the incident response workflows that must be triggered as a result.

    3. Plan: An AWS SSM Contacts resource that represents an escalation plan to manage the incident resolution process.

    Each of these resources requires specific mandatory information to be provided, such as the workspace name, rule ID, subscription ID, etc. Make sure to replace placeholder comments with actual values you'd obtain when setting up your infrastructure.

    Remember, these resources alone do not complete your AI incident management system. They are the building blocks, and you would need to build additional logic and integrations around these basics depending on your actual architecture and requirements. For instance, you might need more integration with system monitoring, or maybe other services like Opsgenie or Dynatrace, depending on what alert data and response activities you decide to automate.

    When considering alert prioritization and incident management, the key is to understand the alerts that have the most significant impact on your operations and ensure that the system prioritizes and addresses those alerts first. This often involves configuring your alert rules with different severity levels and ensuring that your incident management workflows are designed to escalate high-priority incidents to the right team or individual promptly.