惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

U
Unit 42
Blog — PlanetScale
Blog — PlanetScale
H
Help Net Security
The GitHub Blog
The GitHub Blog
博客园 - Franky
酷 壳 – CoolShell
酷 壳 – CoolShell
Recent Announcements
Recent Announcements
量子位
aimingoo的专栏
aimingoo的专栏
大猫的无限游戏
大猫的无限游戏
博客园 - 叶小钗
Microsoft Azure Blog
Microsoft Azure Blog
Martin Fowler
Martin Fowler
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
A
About on SuperTechFans
T
Tailwind CSS Blog
V
V2EX
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
B
Blog
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
S
SegmentFault 最新的问题
G
Google Developers Blog
M
MIT News - Artificial intelligence

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
Turn fragmented runtime signals into coherent attack stor...
2025-11-05 · via Datadog | The Monitor blog

Security teams face a constant trade-off between detection coverage and alert fatigue. Broad, rule-based detection approaches surface every possible indicator of compromise (IoC) but generate unmanageable alert volumes. Narrow, tightly scoped rules reduce noise but risk missing critical signals. And while individual indicators of compromise can highlight suspicious behavior, they often lack the surrounding context needed to tell a complete story of how an attack unfolded. For example, detecting a container that is installing curl could suggest data exfiltration or it could just be a routine operation. Investigators need context, correlation, and a clear narrative to distinguish between benign activity and real compromise.

The latest evolution of Datadog Workload Protection addresses this challenge by introducing new features that transform disconnected runtime signals into unified, narrative-driven investigations. The first two pillars of this new experience—Execution Contexts and an all-new Investigation Graph and Threat Timeline (currently in Preview)—help security analysts connect related events, prioritize genuine threats, and visualize the full attack path from initial exploit to impact.

In this post, we’ll explore how you can:

Runtime security signals are most useful when they can be understood in relation to one another. Workload Protection’s new Execution Context automatically groups runtime events that belong to the same process lineage or exploitation chain, helping you see how an attack evolves over time rather than responding to each alert in isolation.

For example, if an attacker exploits a shell injection vulnerability and then modifies cron jobs or query instance metadata, those actions are now grouped together under a single Execution Context. This grouping eliminates fragmented alerts and enables investigators to focus on the broader compromise attempt rather than chasing individual anomalies.

How Execution Context works

Each event detected by Workload Protection is tagged with a correlation key that associates it with others in the same execution chain. This correlation key can be in two forms:

  • A UUID, ensuring no conflicts between unrelated events across hosts
  • A combination of attributes (such as process lineage), which enables automatic chaining within a process tree

To ensure accuracy, Workload Protection intelligently handles context inheritance (for example, by propagating the same correlation key from a compromised process to all its child processes) and prioritizes overlapping contexts when needed.

Built-in contextual layers capture common runtime scenarios

Starting with the Datadog Agent version 7.68, Workload Protection supports five built-in Execution Context layers designed to capture the most common runtime scenarios:

  • Generic cgroup context: Fallback context for unrelated events
  • Generic auid context: Groups by user session
  • Service context: Isolates runtime activity within service boundaries
  • Interactive shell context: Correlates commands from the same shell session
  • Kubernetes user session context: Provides fine-grained tracking of Kubernetes user actions

By grouping events intelligently, these contexts dramatically improve alert fidelity, helping analysts quickly identify real compromises and dismiss noise.

Visualize complex attacks with the Investigation Graph

Once related events are correlated, investigators need a way to explore them visually. The Investigation Graph provides a dynamic, visual map of processes, resources, and runtime events that shows how an attack unfolded step by step across services, hosts, and containers.

Investigation Graph in Datadog Workload Protection showing process relationships and contextual resource information.

To give analysts a complete picture of what’s happening in an environment, the Investigation Graph merges process trees, deduplicates repetitive activity, and surfaces contextual details such as:

  • The status of impacted infrastructure resources (such as whether a host or container is still active)
  • Related AWS CloudTrail or Kubernetes audit logs
  • Links to misconfigurations or known vulnerabilities detected by other Datadog products

You can also directly pivot to other telemetry data, such as that surfaced by Datadog Cloud SIEM or Infrastructure Monitoring, to validate configuration weaknesses or operational anomalies connected to the same incident. With this unified visualization, the Investigation Graph turns a collection of runtime alerts into an interactive threat map, helping teams move from detection to understanding much faster.

Reconstruct incidents chronologically with the Threat Timeline

While the Investigation Graph shows relationships, analysts also need to understand when each step occurred. The new Threat Timeline presents a chronological narrative of every event within a correlated threat story.

Threat Timeline displaying the chronological sequence of correlated security events and triage actions.

The Threat Timeline combines all correlated events, triage statuses, and recommended actions into a single view. This helps teams retrace an attacker’s movements from the initial exploit to subsequent lateral actions, without needing to toggle between dashboards or tools. Each event includes contextual details and links to correlated metrics, logs, and traces. This gives responders the information they need to make fast, informed decisions.

What’s next: Expanding beyond runtime data

These new investigation capabilities mark the beginning of a broader shift in how Datadog Workload Protection helps security teams analyze threats. Soon, correlation will extend beyond runtime telemetry data to include context from infrastructure, cloud configurations, and even application performance data. By connecting runtime signals to higher-level operational and configuration insights, Datadog will help you understand not just what happened but how and why it happened, and where the next threat might emerge.

Accelerate investigations and reduce noise with Datadog Workload Protection

The new investigation experience in Datadog Workload Protection helps analysts focus on what matters most by transforming fragmented signals into coherent attack stories. With correlated contexts, interactive visualizations, and narrative timelines, security teams can accelerate investigation, reduce noise, and make data-driven triage decisions across their environments.

To learn more, see the Workload Protection documentation. The Investigation Graph and Threat Timeline are currently in Preview; if you’re interested in early access, contact your Datadog representative. If you’re new to Datadog, sign up for a 14-day free trial.