Automating the Security Lifecycle

A Deep Dive into Datadog Cloud SIEM and Workflow Automation

900+ Integrations
700+ Detection Rules
2000+ Automation Actions

Executive Summary

Paradigm Shift

From traditional reactive security monitoring to proactive, automated DevSecOps model

Unified Platform

Merges observability and security data into a single, actionable plane of control

SOAR Capabilities

Security Orchestration, Automation, and Response with multi-step workflows

Modern Security Challenges

Data Volume & Velocity

Terabytes of log data from microservices, containers, and serverless functions overwhelm legacy SIEMs

Impact: Costly overages, performance degradation, missed critical logs

Siloed Visibility

Security tools operate in isolation from development and operations tools

Impact: Critical blind spots, slow investigations, friction between teams

Alert Fatigue

Overwhelming volume of low-fidelity alerts flood security teams with noise

Impact: Analyst burnout, missed genuine threats, delayed response

Datadog's Integrated Solution

Cloud SIEM Features

Real-time Processing

Built on cloud-scale log management with Logging Without Limits™

MITRE ATT&CK® Mapping

700+ detection rules aligned with industry frameworks

Visual Investigation

Investigator tool for graphical relationship analysis

AI-Powered Triage

Agentic AI for autonomous signal analysis and risk scoring

Workflow Automation Features

Visual Builder

Low-code/no-code interface for creating complex workflows

AI Generation

Generate workflows from natural language descriptions

150+ Blueprints

Pre-built templates for common security and operations tasks

Human-in-the-Loop

Approval gates for critical actions with governance controls

Security Automation Playbooks

Threat Intel Enrichment

Scenario: Suspicious IP communication detected

Trigger: Manual from Security Signal

Extract IP from signal
Query VirusTotal API
Post enrichment to Case
Notify analyst
VirusTotal Slack

Identity-Based Response

Scenario: Credential stuffing behavior detected

Trigger: Automated via Notification Rule

Extract user email
Disable account in Okta
Create Jira ticket
Notify security team
Okta Jira Slack

Host Containment

Scenario: Malware execution on host

Trigger: Manual with approval gate

Extract host ID
Human approval via Slack
Isolate host from network
Declare incident
CrowdStrike Slack

Perimeter Defense

Scenario: DDoS attack from IP range

Trigger: Fully automated

Extract attacking IP
Add to Cloudflare blocklist
Create analysis case
Notify on-call SRE
Cloudflare PagerDuty

Impact Metrics

Mean Time to Detect (MTTD)

Average time to identify a security threat from onset

Continuous improvement through automation feedback

Mean Time to Acknowledge (MTTA)

Time from alert generation to analyst engagement

Near-zero with automated workflows

Mean Time to Resolve (MTTR)

Total time from detection to full incident resolution

Orders of magnitude reduction

Qualitative Benefits

Reduced analyst burnout through automation
Improved cross-team collaboration
Consistent, error-free response procedures
Focus on high-value threat hunting

Implementation Best Practices

1

Start with Observability

Build comprehensive data foundation with all critical log sources before automation

2

Begin with Low-Risk Automation

Start with investigation and enrichment tasks before high-impact remediation

3

Embrace Human-in-the-Loop

Include approval gates for production-impacting actions until confidence builds

4

Automation as Code

Treat security automation with same rigor as production code - version control, testing, peer review

5

Measure and Iterate

Continuously monitor MTTD, MTTR, and false positive rates for optimization