Proactive Incident Management for AI Services with AWS

Question

Pulumi · Accepted Answer

To implement proactive incident management for AI services with AWS using Pulumi, you can take advantage of several AWS services like Amazon SageMaker, AWS SSM Incident Manager, AWS Shield, and AWS Security Hub. Here's how you can use these services in a Pulumi program:

- **Amazon SageMaker MonitoringSchedule**: This resource helps you automatically detect and remediate anomalies in machine learning models hosted with Amazon SageMaker. You can set up schedule based monitoring to receive alerts and take action when inconsistencies are found within your data or model predictions.

- **AWS Systems Manager Incident Manager (SSM Incident Manager)**: With the SSM Incident Manager, you can define response plans which specify the incident response process for a given type of incident. This can involve specifying contacts, engagement plans, and action plans, which allows teams to respond to and resolve incidents following a predefined process.

- **AWS Shield Advanced**: This is a managed Distributed Denial of Service (DDoS) protection service that safeguards applications running on AWS. You can enable proactive engagement, so AWS will engage with your team when a DDoS incident is detected.

- **AWS Security Hub**: It provides you with a comprehensive view of your security state within AWS and can be used to automate security checks. Through custom actions and integrations, you can automate the response to different findings.

The following Pulumi Python program outlines how you can set up these services for proactive incident management of AI services with AWS:

```python
import pulumi
import pulumi_aws as aws

# Create an Amazon SageMaker Monitoring Schedule for your model.
monitoring_schedule = aws.sagemaker.MonitoringSchedule("aiModelMonitoringSchedule",
    # Monitoring job definition
    monitoring_schedule_config=aws.sagemaker.MonitoringScheduleMonitoringScheduleConfigArgs(
        monitoring_job_definition_name="my-mlops-monitoring-job",
        monitoring_type="DataQuality",
        schedule_config=aws.sagemaker.MonitoringScheduleMonitoringScheduleConfigScheduleConfigArgs(
            schedule_expression="cron(0 */4 * * ? *)"
        )
    ),
    # The name for the monitoring schedule
    name="MyAIModelMonitoringSchedule"
)

# Define an SSM Incident Manager Response Plan
response_plan = aws.ssmcontacts.Plan("incidentResponsePlan",
    stages=[
        # First stage in the response
        aws.ssmcontacts.PlanStageArgs(
            targets=[
                # Configure targets like on-call engineers or specific teams
                aws.ssmcontacts.PlanStageTargetArgs(
                    contact_target_info=aws.ssmcontacts.PlanStageTargetContactTargetInfoArgs(
                        contact_id="contact_id",
                        is_essential=True
                    ),
                    # Time interval to contact another target if the issue isn't acknowledged
                    retry_interval_in_minutes=20
                )
            ],
            # Duration of this stage before escalation
            duration_in_minutes=120
        )
    ],
    contact_id="example-contact-id"
)

# Enable proactive engagement for AWS Shield to automatically receive DDoS support
shield_proactive_engagement = aws.shield.Protection("proactiveDdosProtection",
    # Enable proactive engagement
    proactive_engagement_status="ENABLED",
    emergency_contact_list=[
        aws.shield.ProtectionEmergencyContactListArgs(
            contact_notes="Primary emergency contact",
            email_address="alert@example.com",
            phone_number="+11234567890"
        )
    ],
    # Resource ARN that needs DDoS protection
    resource_arn="arn:aws:elasticloadbalancing:region:account-id:loadbalancer/app/my-load-balancer"
)

# Setup AWS Security Hub custom action
security_hub_action = aws.securityhub.ActionTarget("customIncidentAction",
    name="MyCustomIncidentAction",
    identifier="customIdentifier",
    description="My custom action that triggers proactive incident response."
)

# Output the URLs and IDs for the resources we've just created
pulumi.export("monitoring_schedule_arn", monitoring_schedule.arn)
pulumi.export("response_plan_id", response_plan.id)
pulumi.export("shield_proactive_engagement_arn", shield_proactive_engagement.arn)
pulumi.export("security_hub_action_target_arn", security_hub_action.arn)
```

In this program:
- First, we create a SageMaker Monitoring Schedule that regularly checks your AI model's performance and sends an alert if issues are detected.
- Then, an SSM Incident Manager Response Plan is defined, which will coordinate the actions taken when an incident occurs.
- AWS Shield is configured with proactive engagement enabled, which allows AWS to collaborate with you immediately when a DDoS is detected on your resources.
- A custom action is set up in AWS Security Hub to initiate a proactive incident response.

With these resources in place, you have an automated infrastructure that actively monitors and responds to incidents, reducing the manual effort required in incident management and improving your AI service's reliability and uptime.