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

酷 壳 – CoolShell
酷 壳 – CoolShell
H
Hacker News: Front Page
P
Palo Alto Networks Blog
T
ThreatConnect
Apple Machine Learning Research
Apple Machine Learning Research
博客园_首页
T
True Tiger Recordings
P
Privacy & Cybersecurity Law Blog
B
Blog
IT之家
IT之家
Last Week in AI
Last Week in AI
F
Full Disclosure
Hacker News: Ask HN
Hacker News: Ask HN
C
Comments on: Blog
Microsoft Azure Blog
Microsoft Azure Blog
C
Cybersecurity and Infrastructure Security Agency CISA
Microsoft Security Blog
Microsoft Security Blog
博客园 - 【当耐特】
N
News and Events Feed by Topic
NISL@THU
NISL@THU
腾讯CDC
雷峰网
雷峰网
Security Latest
Security Latest
李成银的技术随笔
M
Microsoft Research Blog - Microsoft Research
L
LangChain Blog
L
Lohrmann on Cybersecurity
cs.CL updates on arXiv.org
cs.CL updates on arXiv.org
C
Check Point Blog
Y
Y Combinator Blog
Recent Announcements
Recent Announcements
博客园 - Franky
N
News | PayPal Newsroom
V
V2EX
A
About on SuperTechFans
The Register - Security
The Register - Security
月光博客
月光博客
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
Google Online Security Blog
Google Online Security Blog
MyScale Blog
MyScale Blog
Cisco Talos Blog
Cisco Talos Blog
Vercel News
Vercel News
WordPress大学
WordPress大学
C
Cyber Attacks, Cyber Crime and Cyber Security
The Hacker News
The Hacker News
IntelliJ IDEA : IntelliJ IDEA – the Leading IDE for Professional Development in Java and Kotlin | The JetBrains Blog
IntelliJ IDEA : IntelliJ IDEA – the Leading IDE for Professional Development in Java and Kotlin | The JetBrains Blog
爱范儿
爱范儿
A
Arctic Wolf
L
LINUX DO - 最新话题
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More

Datadog | The Monitor blog

Reduce CVE noise with OpenVEX assessments in Datadog How we made a SQL query optimization agent 59% more accurate using autoresearch and LLM Observability How to audit and clean up monitors effectively Diagnose slow PostgreSQL queries faster with explain plan correlation Explore Datadog metrics with Natural Language Queries Toto 2.0: Time series forecasting enters the scaling era Simplify micro-frontend observability with Datadog RUM Attribute AI costs across providers with Datadog Cloud Cost Management Diagnose and resolve database performance issues faster with Database Investigator Datadog for Government achieves FedRAMP® High certification Analyze cloud costs with flexible spreadsheets in Datadog Sheets Inside Datadog’s AI Research Lab: Meet two PhD candidates behind Toto Connect triage and investigation in a single workflow with Datadog Cloud SIEM This Month in Datadog - April 2026 Monitor and optimize Supabase query performance with Datadog Database Monitoring Add dynamically updating context to logs with Reference Tables and Observability Pipelines Introducing ARFBench: A time series question-answering benchmark based on real incidents The product signal latency gap slowing your growth Test network paths with TCP, UDP, and ICMP in Datadog Turn developer feedback into operational insight with Datadog Forms and Sheets How to investigate cloud credential compromise with Bits AI Security Analyst Evaluate, optimize, and secure your Google Cloud AI stack with Datadog Bringing observability data hosting to the UK on AWS Identify and fix code issues faster with Datadog’s Azure DevOps Source Code integration Steganography at scale: Embedding share URLs in Datadog widget screenshots Every team should be A/B testing Centralize observability management with Datadog Governance Console Spotting CI/CD misconfigurations before the bots do: Securing GitHub Actions with Datadog IaC Security Route OTel data from AI apps to ClickHouse and Datadog using Observability Pipelines Manage service tracing across hosts with Single Step Instrumentation rules Offline evaluation for AI agents: Best practices Detect runtime threats in Python Lambda functions with Datadog AAP 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 How we built a real-world evaluation platform for autonomous SRE agents at scale 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 When upserts don't update but still write: Debugging Postgres performance at scale 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 Closing the verification loop: Observability-driven harnesses for building with agents When an AI agent came knocking: Catching malicious contributions in Datadog’s open source repos Closing the verification loop, Part 2: Fully autonomous optimization 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 Designing MCP tools for agents: Lessons from building Datadog's MCP server 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 - February 2026 Fine-tune Toto for turbocharged forecasts Amazon EC2 security: How misconfigured and public AMIs expand your cloud attack surface Enable end-to-end visibility into your Java apps with a single command Measure and improve mobile app startup performance with Datadog RUM Evaluating our AI Guard application to improve quality and control cost Identify untested code across every level of your codebase Make use of guardrail metrics and stop babysitting your releases Monitor Versa Networks SD-WAN performance in Datadog How we reduced the size of our Agent Go binaries by up to 77% Improve performance and reliability with APM Recommendations Remediate transitive vulnerabilities faster with Datadog Software Composition Analysis Generate audit-ready vulnerability and compliance reports with Datadog Sheets Monitor Fortinet FortiManager performance in Datadog Improve test coverage across codebases with Datadog Code Coverage
Datadog CoTerm: Never run the wrong terminal command again
2024-06-26 · via Datadog | The Monitor blog

For engineering teams investigating and resolving incidents together, it’s a common practice to use terminals alongside IDEs and applications like the AWS Console and Datadog. No matter how much tooling and automation a team builds, sometimes there’s just no substitute for dropping down to bash (or zsh, or fish). However, this power comes with peril—even the most careful engineers occasionally mistype commands, with serious consequences. Mistyped commands can lead to sticky situations such as programs executing in the wrong environment or infrastructure changes made without the correct approvals.

That’s why we’re excited to announce the Preview of CoTerm, Datadog’s solution for error-proof, team-powered command-line workflows. CoTerm provides real-time checks on sensitive commands directly in your terminal, enabling you to avoid missteps that can have major ramifications. You can try CoTerm today on macOS and Linux.

Check commands for safety

CoTerm can quickly and easily check for common errors in commands. Imagine you’re running kubectl to scale down a Kubernetes cluster used for development. You assume you’re in the development context and run kubectl scale. CoTerm intercepts the command, notices that you tried to run a risky operation without explicitly specifying --context, and tells you that you’re actually running the command against production:

CoTerm intercepts a kubectl scale command, prompting for --context.

CoTerm makes it easy to prevent mistakes like this, and it’s not limited to kubectl; you can write your own rules for any command.

Check commands for proper approvals

CoTerm can also be used for lightweight approval workflows, when commands are so sensitive that they need a second pair of eyes. Imagine that you are intentionally running kubectl in production because you are decommissioning a namespace that is no longer used. This is potentially dangerous—typing the wrong namespace could cause a major outage.

To avoid this, you configure CoTerm to require approval for kubectl delete or similarly sensitive operations in production. CoTerm intercepts the command, warns that it’s dangerous, and requires approval before execution:

CoTerm intercepts a kubectl delete command in order to get the required approval.

CoTerm also automatically creates an approval request in Datadog Case Management:

An approval request in Datadog Case Management.

In this scenario, kubectl delete will only run after it’s been approved by a coworker. Approvals can also be linked to incidents—command approval is a great way to lessen risk during high-stakes, time-sensitive incident remediation.

Record, browse, and replay terminal output in Datadog

CoTerm automatically records terminal output when a command is intercepted. In the previous examples, the full terminal session is browsable and replayable in Datadog. You can quickly and easily search through recordings to answer questions like, “Who ran this command in production recently?“

You can rest assured that terminal recordings won’t contain any PII or other sensitive information because CoTerm redacts sensitive data, such as keys and passwords, before sending the recordings to Datadog.

Recordings can also be useful for knowledge sharing with coworkers. Simply run ddcoterm and CoTerm will record your interactive shell session, which you or others can then watch in Datadog:

You can watch a terminal session recording in Datadog.

How CoTerm works

CoTerm shims commands by using the PATH environment variable. For example, let’s say you want to shim a dangerous command named foo. You would run ddcoterm shim create foo, which does the following:

  1. Creates a lightweight script for launching CoTerm at ~/.ddcoterm/overrides/foo.
  2. Adjusts PATH in your shell configuration so that ~/.ddcoterm/overrides/foo takes priority over the usual foo executable if necessary.

The next time you run foo, it will launch CoTerm, which quickly evaluates any user-defined rules for foo, generates warnings and approval requests if necessary, and then runs foo as usual.

CoTerm rules are Lua snippets embedded in CoTerm’s YAML configuration file, like this:

- command: "foo"

rules:

- rule: has_arg("bar")

actions: ["approval", "record", "logs", "process_info"]

This rule tells CoTerm, “If foo is intercepted with a bar argument, require approval and record it.” This would apply to someone running foo bar but not foo baz.

Shimming commands is fast, and we aim for it to be unnoticeable. If the Lua rules for a command determine that an approval and a recording aren’t necessary, CoTerm can get out of the way in single-digit milliseconds.

Try CoTerm today

CoTerm is available in Preview today. For details about installation, see our CoTerm docs. To get started even faster, follow these steps:

  1. Run brew install coterm on macOS or our install script on Linux.
  2. Run ddcoterm init to log in, create some default rules, and shim kubectl.
  3. Try running ddcoterm to record an interactive session, or kubectl test-coterm to test shimming and approvals.

If you’re new to Datadog, you can sign up for a 14-day free trial.