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

月光博客
月光博客
C
Check Point Blog
J
Java Code Geeks
腾讯CDC
Apple Machine Learning Research
Apple Machine Learning Research
宝玉的分享
宝玉的分享
Microsoft Azure Blog
Microsoft Azure Blog
WordPress大学
WordPress大学
量子位
Google DeepMind News
Google DeepMind News
I
InfoQ
The GitHub Blog
The GitHub Blog
aimingoo的专栏
aimingoo的专栏
N
Netflix TechBlog - Medium
Hugging Face - Blog
Hugging Face - Blog
博客园 - Franky
V
V2EX
Blog — PlanetScale
Blog — PlanetScale
T
The Blog of Author Tim Ferriss
小众软件
小众软件
博客园_首页
人人都是产品经理
人人都是产品经理
博客园 - 聂微东
IT之家
IT之家

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
Accelerate investigations with AI-powered log parsing
2025-12-01 · via Datadog | The Monitor blog
Usman Khan

Usman Khan

When debugging production issues, investigating security incidents, or analyzing network traffic, engineers and analysts need not only to find the right logs but to make sense of all the dense, unstructured data generated by different systems. Logs rarely ship neatly laid out in a way that facilitates filtering, faceting, or graphing for every possible scenario. As a result, teams often find themselves writing regular expressions or custom parsers on the fly, which can be error-prone and time-consuming.

To help simplify the process of getting from raw text to relevant insight directly at query time, Datadog Log Management now offers a brand new one-click log parsing experience in the Log Explorer, powered by AI.

Given a raw log line, Datadog can analyze the content to suggest Grok parsing rules that extract important pieces of information (like IP addresses, user IDs, and actions) into structured fields, enabling deep analysis without having to modify global ingestion pipelines or rely on a regex testing tool.

In this post, we’ll show you how AI-powered log parsing in the Log Explorer helps you:

In distributed microservices environments, a single user request can generate dozens of complex log events across multiple services and systems. Traditionally, analyzing an unparsed log message has consisted of some combination of the following steps:

  • Reviewing and interpreting the content of the log to identify which components are relevant to the current task
  • Handcrafting patterns to capture this relevant info (e.g., date and time, severity, system info, user details, etc.)
  • Testing and verifying correctness, often by copying the parsing rule and other manually collected log samples into a testing tool
  • Contacting an admin to get the logic implemented within shared data processing pipelines

Datadog reduces this process to a single click by offloading this work to AI, which will analyze a selected log line and extract key details as calculated fields in a matter of seconds.

From here, you can opt to modify the generated rule before saving the parsing rules (which is useful if you want to rename the fields being extracted, for example), or ask Datadog to generate a new one altogether. Otherwise, you can continue on with your analysis by incorporating the calculated fields you’ve extracted by filtering, faceting, graphing, or simply displaying the new dimensions in your view.

Simplify investigation sprawl and reduce context switching

During an end-to-end investigation, you may be reviewing logs from a variety of sources, including network appliances and firewalls, VPNs and proxies, legacy services and third-party tools, AI agents, and more. Since these sources don’t share a common schema, tracking context across all of your telemetry can become challenging.

Because the Log Explorer now provides automated, AI-based parsing rule generation, you can rely on Datadog to determine what’s most important (e.g., status codes, transfer sizes, error messages, etc.), and where in the message it lives, for each individual log type. For example, a network engineer can pull up raw firewall logs, instantly receive a rule designed to parse the source and destination IP addresses, and then use those newly created facets to perform a geospatial analysis of blocked connections, all without ever leaving the Log Explorer or requiring any preconfiguration. This repositions log standardization as an exploratory, query-time activity tied to the task at hand, rather than a rigid, pre-ingestion requirement.

Take advantage of AI to parse logs faster

AI-powered log parsing removes the friction of manual rule creation and helps teams convert raw logs into structured insights in seconds. By providing automated rule suggestions, query-time transformations, and on-the-fly field extraction, Datadog enables engineers and analysts to fluidly explore complex log data, investigate incidents, debug issues, and get to insights faster.

To get started, access the Datadog Log Explorer today and visit our Calculated Fields documentation to learn more. If you’re new to Datadog, sign up for a 14-day free trial.