AI-Driven Alert Management Using AWS SSM Contacts

Question

Pulumi · Accepted Answer

AI-driven alert management involves setting up a system that can automatically respond to and manage incidents or alerts based on predefined rules and machine learning models. AWS Systems Manager (SSM) Contacts is a service that allows you to define contact information and escalation plans that AWS will use to engage your teams during an operational incident.

In this context, we'll use Pulumi to define a contact and an escalation plan using AWS SSM Contacts. The following Pulumi program in Python will set up a basic contact and an escalation plan that automatically manages alerts.

Before diving into the Pulumi program, let's go through the resources and their purpose:

1. `Contact`: Represents a single point of contact, which could be an individual, a team, or an automated system that will be notified when an incident occurs.

2. `ContactChannel`: The method through which the contact will be notified (e.g., SMS, email).

3. `Plan`: Defines an escalation plan, including stages and targets, that AWS will use to notify contacts based on the severity of the incident and other criteria.

Here's a Pulumi program that sets up an AI-driven alert management system using AWS SSM Contacts:

```python
import pulumi
import pulumi_aws as aws

# Create an SSM Contact, which represents the individual or team to contact in case of an incident.
contact = aws.ssmcontacts.Contact("aiAlertContact",
    alias="ai-alert-contact",
    type="PERSONAL",
    display_name="AI Alert Contact",
    # Provide the contact details (additional options are available).
)

# Define a Contact Channel, which is the method through which the contact will be notified.
# For the sake of this example, we're setting it up as an SMS.
contact_channel = aws.ssmcontacts.ContactChannel("aiAlertContactChannel",
    name="ai-alert-sms",
    type="SMS",
    contact_id=contact.id,
    delivery_address={
        "simple_address": "+1234567890"  # The phone number to receive SMS notifications.
    },
    opts=pulumi.ResourceOptions(depends_on=[contact]))  # Ensure the contact is created before the contact channel.

# Create an Engagement Plan, detailing how and when to engage contacts in case of an incident.
plan = aws.ssmcontacts.Plan("aiAlertPlan",
    contact_id=contact.id,
    stages=[{
        "duration_in_minutes": 20,
        "targets": [{
            "channel_target_info": {
                "contact_channel_id": contact_channel.id,
                "retry_interval_in_minutes": 5
            },
            "contact_target_info": {
                "contact_id": contact.id,
                "is_essential": True
            }
        }]
    }],
    opts=pulumi.ResourceOptions(depends_on=[contact, contact_channel]))  # Make sure all resources are created in the correct order.

# Output the contact and plan ARNs that you can use within AWS Systems Manager or Incident Manager.
pulumi.export('contact_arn', contact.arn)
pulumi.export('plan_arn', plan.arn)
```

The program starts by importing the required Pulumi and AWS modules. We define a contact using `aws.ssmcontacts.Contact` and specify the contact type as `PERSONAL`. Next, we create a contact channel of type `SMS` with `aws.ssmcontacts.ContactChannel` and link it to our contact. We then set up an engagement plan using `aws.ssmcontacts.Plan`, which specifies how the contact should be notified.

In the `Plan`, we define a stage with a duration and targets, representing the first line of responders. The target specifies the `contact_channel_id` and the contact to engage, as well as whether the contact is essential.

Finally, we export the Amazon Resource Names (ARNs) of the contact and the plan so they can be referenced outside of Pulumi if necessary.

This program gives you a foundation to start building AI-driven alert management on AWS. It can be extended with additional logic for AI-driven decision-making, such as triggering AWS Lambda functions that evaluate the severity of an incident and choose the appropriate response.