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Datadog | The Monitor blog

Introducing our open source AI-native SAST Instrument and monitor Boomi integration flows with OpenTelemetry and Datadog Not all index scans are equal: How we cut query latency by over 99% Platform engineering metrics: What to measure and what to ignore Integrate Recorded Future threat intelligence with Datadog Cloud SIEM CI/CD security: threat modeling using a MITRE-style threat matrix CI/CD security: How to secure your GitHub ecosystem Ingress NGINX is EOL: A practical guide for migrating to Kubernetes Gateway API Operating agentic AI with Amazon Bedrock AgentCore and Datadog LLM Observability: Lessons from NTT DATA Introducing the Datadog Code Security MCP Capture and analyze custom heatmaps in Session Replay Understand session replays faster with AI summaries and smart chapters Monitor ClickHouse query performance with Datadog Database Monitoring How we designed empathetic alert sounds for on-call engineers Search and act across Datadog to resolve issues faster with Bits Assistant Measure the business impact of every product change with Datadog Experiments Analyzing round trip query latency Configuring JavaScript caches for better performance Introducing Bits AI Dev Agent for Code Security Datadog achieves ISO 42001 certification for responsible AI Monitor Nutanix clusters, hosts, and VMs with Datadog Monitor Juniper Mist in Datadog A new Host Map for modern infrastructure Annotate traces to improve LLM quality with Datadog LLM Observability What’s new in Cloud SIEM: AI-powered investigations, enhanced threat intelligence, and scalable security operations Explore Kubernetes with native OpenTelemetry data Monitor Oracle Fusion Cloud Applications with Datadog Announcing the Datadog Terraform provider v4.0.0 Scaling Kubernetes workloads on custom metrics How to design cloud environments for AI-powered threat analysis Monitor Aruba Central in Datadog How we centralize and remediate risks with Datadog Case Management Accelerate incident response with Datadog and ServiceNow Monitor your application and network load balancer logs Understanding Karpenter architecture for Kubernetes autoscaling Tools for collecting metrics and logs from Karpenter Monitor Karpenter with Datadog What your product data is actually saying Key metrics for monitoring Karpenter Securing Datadog’s platform in the AI age: The role of observability data Four ways engineering teams use the Datadog MCP Server to power AI agents Approaching your observability migration with the right mindset Meet the new Bits AI SRE: Deeper reasoning, twice as fast Key learnings from the 2026 State of DevSecOps study Use plain English to query your multi-cloud infrastructure in Resource Catalog Simplifying troubleshooting across the user journey with Datadog Synthetic Monitoring Protect your OCI resources with Datadog Cloud Security This Month in Datadog - 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Automate remediation of threats detected by Datadog Cloud SIEM
Justin Massey, Jonathan Epstein · 2021-06-16 · via Datadog | The Monitor blog

When it comes to security threats, a few minutes additional response time can make the difference between a minor nuisance and a major problem. Datadog Cloud SIEM enables you to easily triage and alert on threats as they occur. In this post, we’ll look at how you can use Datadog’s webhooks integration to automate responses to common threats Datadog might detect across your environments. This way, Datadog can act as a centralized threat remediation platform that connects to and initiates behavior in your other services, shaving valuable time off your threat-response workflow.

Use Datadog and webhooks to automate security responses

Webhooks are HTTP callbacks (usually written in JSON) that send messages to applications whenever a certain condition is met. Those applications can then trigger further actions based on the data contained in the webhook payload. Webhooks act as script-based connectors that link Datadog to your other tools. By setting up webhooks that respond to your Datadog security notifications, you can create simple, automated remediation workflows that neutralize threats in real-time.

Datadog’s webhook integration makes it easy to set up webhook messages that deliver their payloads to the services you want to automate whenever a Detection Rule is broken. Every webhook payload contains information on the triggering event and a custom message that can be used to initiate services downstream. You can automate commands for any service that has a webhook URL. The following is a simple webhook that will send a payload with the relevant data from the Security Signal that triggered it.

{

"SECURITY_RULE_NAME": "$SECURITY_RULE_NAME",

"SECURITY_SIGNAL_ID": "$SECURITY_SIGNAL_ID",

"SECURITY_SIGNAL_ATTRIBUTES": $SECURITY_SIGNAL_ATTRIBUTES

}

In the following sections, we’ll walk through some more examples of how to use Datadog with webhooks to automate security responses and take a look at how to properly configure webhook payloads to respond to your Datadog alerts.

Delete misconfigured security groups

In order to secure your cloud environment, it’s important to delete a misconfigured resource as soon as it is created. You can do this by sending a webhook to your cloud provider’s API management service. For instance, the following diagram illustrates an automation workflow that uses a webhook to connect Datadog’s AWS integration with Amazon API Gateway to perform account management tasks.

Using webhooks with Datadog Cloud SIEM allows you to automate security procedures in your cloud environment.

If an AWS user creates a poorly configured resource (e.g., an overly permissive security group, user role, etc.) within your AWS environment, Datadog Log Management ingests the related log. That triggers a security group–based Detection Rule, which then sends the webhook’s JSON payload to the designated AWS API Gateway URL, which in turn activates a AWS Lambda function that automatically deletes the offending resource.

Ban a suspicious IP address

Your cloud resources might receive traffic from hundreds of IP addresses a day, most of which belong to known and trusted users. A sign-in from an unrecognized IP address, however, might represent an attacker manipulating a trusted user’s credentials, with which they can then access your data and gain persistence in your environment.

To spot these types of threats, you can use Datadog Cloud SIEM’s new term detection method, which analyzes your account’s historical data over a chosen period of time and alerts on previously unseen values in your cloud logs. To respond to anomalous activity as soon as it occurs, you can set up a new term–based Detection Rule that, when triggered, sends a webhook payload to your cloud’s identity and access management (IAM) service to ban the unknown IP. This way, no time is wasted between threat detection and resolution. The following example illustrates what the relevant webhook payload might look like when triggered by a new term-based detection rule from Datadog. It includes the relevant Security Signal attributes that our downstream service needs, which in this case is the IP address that Datadog has detected. Datadog then sends the payload to your IAM service, which can ban the offending address.

{

"SECURITY_RULE_NAME": "Request from unexpected IP address",

"SECURITY_SIGNAL_ID": "abcd1234",

"SECURITY_SIGNAL_ATTRIBUTES": {

"network": {

"client": {

"ip": [

"1.2.3.4"

]

}

}

}

}

Auto-remediate application abuse and fraud

Just as Datadog can help you spot anomalous activity in your logs, you can also use it to uncover patterns of abuse or fraud across your application. For example, you can set up a Detection Rule that gets triggered when a user repeatedly attempts to purchase something in your application with invalid credit card details. Datadog will then generate a Security Signal that details the offense as well as the suspicious user’s particulars, such as their IP address and user ID, and sends a webhook payload with remediation instructions to a service that disables the user. The following sample contains this identifying information from the Security Signal:

{

"SECURITY_RULE_NAME": "Fraudulent Credit Card Authorizations",

"SECURITY_SIGNAL_ID": "efgh5678",

"SECURITY_SIGNAL_ATTRIBUTES": {

"usr": {

"id": "john.doe@your_domain.com"

},

"evt": {

"name": "credit_card_authorization",

"outcome": "fail"

},

"network": {

"client": {

"ip": [

"1.2.3.4"

]

}

}

}

}

Webhook, line, and sinker

By using webhooks to integrate Datadog Cloud SIEM with other services across your stack, you can easily create Detection Rules that trigger auto-remediation workflows. For more information on securing your environment with Datadog, check out our security documentation. And if you’re not already a Datadog customer, get started today with a free 14-day trial.