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

cs.AI updates on arXiv.org
cs.AI updates on arXiv.org
Spread Privacy
Spread Privacy
T
Threat Research - Cisco Blogs
C
Cyber Attacks, Cyber Crime and Cyber Security
cs.CL updates on arXiv.org
cs.CL updates on arXiv.org
Cloudbric
Cloudbric
Threat Intelligence Blog | Flashpoint
Threat Intelligence Blog | Flashpoint
SecWiki News
SecWiki News
Schneier on Security
Schneier on Security
人人都是产品经理
人人都是产品经理
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
WordPress大学
WordPress大学
S
Secure Thoughts
V
Visual Studio Blog
Microsoft Azure Blog
Microsoft Azure Blog
Attack and Defense Labs
Attack and Defense Labs
T
The Blog of Author Tim Ferriss
Vercel News
Vercel News
The Last Watchdog
The Last Watchdog
L
LINUX DO - 最新话题
T
Tailwind CSS Blog
C
Cybersecurity and Infrastructure Security Agency CISA
Scott Helme
Scott Helme
博客园 - Franky
I
InfoQ
Cisco Talos Blog
Cisco Talos Blog
Stack Overflow Blog
Stack Overflow Blog
MongoDB | Blog
MongoDB | Blog
N
Netflix TechBlog - Medium
Help Net Security
Help Net Security
M
MIT News - Artificial intelligence
GbyAI
GbyAI
B
Blog
K
Kaspersky official blog
博客园 - 【当耐特】
AWS News Blog
AWS News Blog
O
OpenAI News
A
About on SuperTechFans
F
Fortinet All Blogs
PCI Perspectives
PCI Perspectives
G
Google Developers Blog
www.infosecurity-magazine.com
www.infosecurity-magazine.com
A
Arctic Wolf
酷 壳 – CoolShell
酷 壳 – CoolShell
Application and Cybersecurity Blog
Application and Cybersecurity Blog
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
C
CXSECURITY Database RSS Feed - CXSecurity.com
Apple Machine Learning Research
Apple Machine Learning Research
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
Google DeepMind News
Google DeepMind News

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
Monitor Falco with Datadog
2025-10-31 · via Datadog | The Monitor blog

Organizations running containerized environments face complex security challenges as they scale Kubernetes and adopt dynamic, ephemeral infrastructure. Traditional security tools often miss activity inside containers, making it difficult to detect policy violations or threats at runtime.

Falco is a runtime security monitoring tool for containerized infrastructure. Falco uses eBPF probes and a custom Linux kernel module to detect malicious behavior in hosts and containers by detecting system calls in real time.

Datadog now integrates with Falco, enabling you to forward Falco alerts into Datadog to visualize and analyze container security events alongside relevant infrastructure metrics, traces, and logs from across your environment. In this post, we’ll show how you can use the Falco integration to accelerate your investigations with the power of Datadog dashboards, alerts, and Cloud SIEM.

Correlate Falco health and performance metrics with infrastructure telemetry

Once you’ve configured the integration to send telemetry from your Falco instance to Datadog, you can track key task execution metrics and spot performance bottlenecks by using the included dashboard. For instance, it can be helpful to correlate Falco’s CPU usage with the running processes count to hypothesize whether elevated resource utilization is caused by increased load or a single bottleneck process.

Monitoring Falco metrics in the out-of-the-box dashboard

It’s also paramount to track and alert on output queue drops, as this is the strongest indicator that your Falco instance is missing critical threats. The health and performance of your Falco instance is a key component of the broader observability you need into your containerized workloads.

You can easily customize dashboards to help correlate Falco telemetry with health and performance signals from the rest of your Kubernetes environment. For instance, let’s say Falco detects an unexpected shell process in your application and submits an alert. By forwarding this alert to Datadog, you can correlate it with Kubernetes and application telemetry to understand the broader impact. For example, elevated CPU or memory usage on the affected pod or node might indicate that the attacker is running resource-intensive processes, such as crypto mining. Or, increased outbound network connections from the compromised container could signal data exfiltration or a command-and-control attack.

Speed up investigations with Falco and Datadog Cloud SIEM

By seeing security alerts within a broader application observability context, teams can more efficiently analyze the root cause and scope of a threat. The Falco integration can send security alerts from Falco directly into Datadog via API forwarding or the Datadog Agent. This allows your team to view, analyze, and act on runtime alerts while armed with logs and metrics from across your environment.

In addition to tracking Falco alerts within Datadog Monitors, adding alerts to dashboards, and pinning alerts to incident responses within Incident Management, you can send your Falco alerts to Datadog Cloud SIEM to help accelerate investigations and automate remediation workflows.

For instance, suppose Falco detects that a container in your Kubernetes cluster attempted to escalate privileges by writing to a sensitive system file such as /etc/passwd. This kind of unusual behavior could indicate a privilege escalation attack.

The Falco alert logs for this attack can be integrated into Cloud SIEM, which automatically scans all your security logs to flag malicious activity via built-in detection rules. By using Cloud SIEM’s historical engine, you can surface correlated threats hidden deep within your environment, such as abnormal user behavior, account takeovers, lateral movement, privilege escalations from authenticated insiders, and more.

The Falco security signal that can help you detect attacks in Cloud SIEM

By correlating the Falco alert and other security logs using Cloud SIEM, you can expand your investigation into things like:

  • User and authentication logs associated with the same container or service account, to detect if the attacker has accessed other resources with elevated permissions
  • Historical activity, including past Falco and Kubernetes audit logs, to reveal if similar attempts occurred on other nodes or pods, suggesting lateral movement
  • Correlated security signals to spot related detections (e.g., suspicious outbound SSH attempts or anomalous API calls) that can help you build a full timeline of the attack

Incorporate runtime security insights into your container observability

Falco provides visibility into container and host activity, while Datadog unifies those insights with metrics, logs, and traces across your entire stack. Together, they enable teams to accelerate investigations, reduce MTTR, and strengthen their security posture as they scale Kubernetes and other containerized environments.

With Datadog dashboards, monitors, and Cloud SIEM, your Falco alerts become part of a broader security and observability strategy that gives you real-time protection and actionable insights. Get started setting up the Falco integration by checking out our documentation. For more information about Datadog Cloud SIEM, see the Cloud SIEM docs. Or, if you’re brand new to Datadog, sign up for a free trial to get started.