Anomaly Detection for AI Systems via GCP Monitoring Alerts

Question

Pulumi · Accepted Answer

Anomaly detection is a critical aspect of maintaining AI systems, as it allows you to identify unexpected behavior that could indicate problems such as system glitches, unusual user activity, or potential security breaches. Google Cloud Platform (GCP) offers various services to monitor and alert on these potential anomalies. In the context of infrastructure management with Pulumi, you can programmatically define alerting policies that detect anomalies in the performance of your AI systems and notify you or take automated actions.

In this program, we will use the `gcp.monitoring.AlertPolicy` resource from the Pulumi GCP package to create an alert policy on GCP. This resource allows us to define conditions that determine when an alert should be triggered based on metrics collected by GCP Monitoring from your cloud resources.

Here's a high-level outline of the steps we will follow in our Pulumi program:

1. Import the necessary Pulumi GCP library.
2. Define an alert policy with conditions that detect anomalies. This could be based on metrics such as CPU usage, memory consumption, or custom metrics that your AI system reports to GCP Monitoring.
3. Specify notification channels to receive alerts when an anomaly is detected.
4. Export any important information, such as the IDs of created resources.

Below is a Pulumi program written in Python that defines an alert policy for anomaly detection:

```python
import pulumi
import pulumi_gcp as gcp

# Create a GCP Monitoring Alert Policy for Anomaly Detection
alert_policy = gcp.monitoring.AlertPolicy("ai-systems-anomaly-detection",
    # Display name for the alert policy
    display_name="AI Systems Anomaly Detection Alert",

# Combining multiple conditions to trigger the alert
    # 'AND' means all conditions must be met, 'OR' means any one can trigger
    combiner="AND",

# Conditions for the alert
    conditions=[{
        "displayName": "High CPU Usage",
        # Threshold condition for high CPU usage (threshold: 80% for 5 minutes)
        "conditionThreshold": {
            "filter": 'metric.type="compute.googleapis.com/instance/cpu/utilization"',
            "comparison": "COMPARISON_GT",
            "thresholdValue": 0.8,
            "duration": "300s",
            "trigger": {"count": 1},
            "aggregations": [{
                "alignmentPeriod": "60s",
                "perSeriesAligner": "ALIGN_RATE"
            }],
        },
    }, {
        "displayName": "Unusual Network Traffic",
        # Threshold condition for unusual increase in network traffic (threshold: 1 million bytes)
        "conditionThreshold": {
            "filter": 'metric.type="compute.googleapis.com/instance/network/received_bytes_count"',
            "comparison": "COMPARISON_GT",
            "thresholdValue": 1e6,
            "duration": "300s",
            "trigger": {"count": 1},
            "aggregations": [{
                "alignmentPeriod": "60s",
                "perSeriesAligner": "ALIGN_DELTA"
            }],
        },
    }],

# Here you would specify the notification channels (e.g., email, SMS, Slack)
    # This example assumes that the notification channels are already set up
    notification_channels=["your-notification-channel-id"],

# Additional documentation that could include some playbook links or steps to diagnose
    documentation={
        "content": "This policy detects potential anomalies in AI systems based on CPU usage and Network traffic.",
        "mimeType": "text/markdown"
    },
)

# Export the alert policy id
pulumi.export("alert_policy_id", alert_policy.id)
```

In this program, we have defined an alert policy with two conditions:

1. High CPU Usage: This condition checks if the CPU utilization goes above 80% for a duration of 5 minutes, which might indicate the AI system is over-utilized or experiencing a processing anomaly.
2. Unusual Network Traffic: This condition monitors for an unusual spike in received network traffic that could suggest a data breach or an anomaly in data flow.

The `combiner` property defines that both conditions must be met for the alert to trigger (`"AND"`). If you want to trigger an alert when either condition is met, you could change this value to `"OR"`.

We have also included placeholders for notification channels and documentation content which can be customized according to your operational needs.

Note the `notification_channels` is where you would put the ID of your pre-configured Notification Channel(s) on GCP, which can include email, SMS, Slack, etc. You need to set this up through the GCP console or using Pulumi to interact with the relevant GCP services.

After defining your alert policy, the program exports the ID of the created alert policy. This ID can be used to reference the alert policy in other operations or scripts.

This program gives you a foundational setup for anomaly detection using GCP Monitoring and Alerting and can be further customized to your specific use case and environment.