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

D
Darknet – Hacking Tools, Hacker News & Cyber Security
爱范儿
爱范儿
GbyAI
GbyAI
A
About on SuperTechFans
阮一峰的网络日志
阮一峰的网络日志
U
Unit 42
博客园_首页
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
S
Secure Thoughts
Security Latest
Security Latest
Exploit-DB.com RSS Feed
Exploit-DB.com RSS Feed
Hacker News - Newest:
Hacker News - Newest: "LLM"
PCI Perspectives
PCI Perspectives
MyScale Blog
MyScale Blog
罗磊的独立博客
Y
Y Combinator Blog
Know Your Adversary
Know Your Adversary
月光博客
月光博客
C
CXSECURITY Database RSS Feed - CXSecurity.com
Recorded Future
Recorded Future
S
Securelist
T
Tor Project blog
Apple Machine Learning Research
Apple Machine Learning Research
人人都是产品经理
人人都是产品经理
WordPress大学
WordPress大学
The GitHub Blog
The GitHub Blog
P
Privacy & Cybersecurity Law Blog
Application and Cybersecurity Blog
Application and Cybersecurity Blog
雷峰网
雷峰网
Microsoft Security Blog
Microsoft Security 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
Project Zero
Project Zero
T
Tailwind CSS Blog
腾讯CDC
C
Cisco Blogs
T
The Exploit Database - CXSecurity.com
The Hacker News
The Hacker News
F
Full Disclosure
cs.CL updates on arXiv.org
cs.CL updates on arXiv.org
美团技术团队
N
Netflix TechBlog - Medium
云风的 BLOG
云风的 BLOG
N
News and Events Feed by Topic
C
Cybersecurity and Infrastructure Security Agency CISA
D
Docker
酷 壳 – CoolShell
酷 壳 – CoolShell
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
T
Threat Research - Cisco Blogs
O
OpenAI News
Cloudbric
Cloudbric

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 - February 2026 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 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 Move fast, don’t break things: Consistent testing standards at scale Enrich logs with ServiceNow CMDB context before routing to any SIEM or logging tool Monitor Lustre with Datadog Make faster, better product decisions with Datadog Product Analytics Surface and remediate runtime posture issues with Workload Protection Findings Protect agentic AI applications with Datadog AI Guard How to optimize JavaScript code with CSS Trace Google Pub/Sub workloads in Cloud Run with Datadog Detect human names in logs with ML in Sensitive Data Scanner How we cut our NLQ agent debugging time from hours to minutes with LLM Observability Debug PostgreSQL query latency faster with EXPLAIN ANALYZE in Datadog Database Monitoring Datadog acquires Propolis Unify and correlate frontend and backend data with retention filters Scale compliance across global frameworks with Datadog Cloud Security Monitor Arista VeloCloud SD-WAN performance with Datadog Building reliable dashboard agents with Datadog LLM Observability Simplify log collection and aggregation for MSSPs with Datadog Observability Pipelines Mitigation for Node.js denial-of-service vulnerability affecting Datadog APM Automate flaky test fixes with the Bits AI Dev Agent and Test Optimization How we built an AI SRE agent that investigates like a team of engineers Datadog integrations 2025 recap: Observability for AI, security, and hybrid cloud Design effective executive dashboards with Datadog Implement dbt data quality checks with dbt-expectations Bring faster visibility into AWS Lambda functions with remote instrumentation Troubleshoot faster with the GitLab Source Code integration in Datadog How Cambia Health Solutions saved $30,000 monthly with Cloud Cost Management and the Datadog Resource Catalog Normalize any logs for Cloud SIEM with Datadog's OCSF processor Optimizing Datadog at scale: Cost-efficient observability at Zendesk Detect, diagnose, and resolve network issues easily with CNM Network Health Connect engineering errors to user impact in early-stage products Cilium configuration for Kubernetes operations at scale Designing feedback loops for progressive delivery Ship features faster and safer with Datadog Feature Flags Choosing the right OpenTelemetry Collector distribution Route your monitor alerts with Datadog monitor notification rules Automate Cloud SIEM investigations with Bits AI Security Analyst Cloud threat detection: How to identify risky activity across control and data planes Collecting Kafka performance metrics Monitoring Kafka with Datadog Monitoring Kafka performance metrics
Signal Sciences + Datadog: improving web application security
Tyler Shields · 2016-07-27 · via Datadog | The Monitor blog
Tyler Shields

Tyler Shields

This is a guest post by Tyler Shields, Vice President Marketing, Partnerships, and Strategy at Signal Sciences.

Web application security has been a stagnant technology field for well over a decade. There just wasn’t enough change in application development processes to dictate an overhaul in how organizations execute their web application security initiatives. In recent years new development models such as agile and Devops, infrastructure changes including cloud and microservices, and a completely revamped build and deploy methodology where production pushes occur daily or weekly instead of semi-annually, have drastically changed traditional development programs. Web application security is past due for innovation and reinvention, and the Signal Sciences integration with Datadog helps bring an intelligent alerting engine for web application security into common practice.

Signal Sciences Next Generation Firewall (NGWAF) is a web protection platform that incorporates all of the core tenets of modern web application security: visibility into real time attack data, advanced attack detection and blocking techniques, and the ability to alert and react quickly to attempted and successful attacks. Visibility into attack signals and anomaly data are required for the enterprise to have any chance at executing a successful web application security program. The integration between Datadog and Signal Sciences provides an excellent platform to:

  • Understand the data collected by your security environment, in detail.

  • Create an alerting mechanism to take action on detected attacks and threat scenarios.

  • Correlate threat activities with their effects throughout your stack.

Signal Sciences web application security dashboard

Improve your security-data analytics and alerting

Web application security requires the correlation of multiple data sources including development, operations, and security integration points. Using Datadog and Signal Sciences together allows an unparalleled level of security insight to be augmented by an industry-leading rapid response, tooling, and alerting, system. A security operations center (SOC) analyst spends the majority of their day correlating data between disparate sources from development, operations, and security. Datadog can be used to present development-centered alerts, attack-based alerts, incident alerts, and outage alerts. These and many other data points help provide the context to make intelligent security decisions. The ability to have multiple data sources available in a single data analysis and alerting engine such as Datadog increases the rate at which a SOC analyst makes accurate assessments of events and determines remediation steps lowering the mean time to remediation (MTTR) and mean time to detection (MTTD) for security incidents.

Signal Sciences web application security weekly review

Effective security relies on data measurement

If you can measure it, you can be confident that you are improving. Datadog and Signal Sciences surface and alert on the most important metrics and data points, allowing joint customers to improve the speed and accuracy of their security decision-making process while having measurable data to demonstrate improvements in web application security.

Joint Datadog and Signal Sciences customers are on the forefront of the modern pragmatic approach to information security. Data-driven security integrations are helping Signal Sciences customers such as AirBnB, Under Armour, Vimeo, Etsy, Taser, and others to increase their security levels while informing and improving their overall security processes. If you are not yet a Signal Sciences customer but are interested in becoming one, please visit Signal Sciences web site and click the “Request a Demo” button. If you are not yet a Datadog customer, you can get a free Datadog trial account now.