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

Vercel News
Vercel News
Recorded Future
Recorded Future
CTFtime.org: upcoming CTF events
CTFtime.org: upcoming CTF events
The GitHub Blog
The GitHub Blog
Application and Cybersecurity Blog
Application and Cybersecurity Blog
Google DeepMind News
Google DeepMind News
cs.AI updates on arXiv.org
cs.AI updates on arXiv.org
Microsoft Azure Blog
Microsoft Azure Blog
K
KPMG report finds enterprise disconnect between AI and its ROI | CIO
M
MIT News - Artificial intelligence
云风的 BLOG
云风的 BLOG
Y
Y Combinator Blog
N
News | PayPal Newsroom
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
Help Net Security
Help Net Security
博客园 - Franky
SecWiki News
SecWiki News
Recent Announcements
Recent Announcements
T
Troy Hunt's Blog
The Register - Security
The Register - Security
The Last Watchdog
The Last Watchdog
Webroot Blog
Webroot Blog
S
Security Affairs
博客园 - 司徒正美
S
Schneier on Security
I
InfoQ
博客园_首页
www.infosecurity-magazine.com
www.infosecurity-magazine.com
T
Threat Research - Cisco Blogs
Forbes - Security
Forbes - Security
腾讯CDC
N
Netflix TechBlog - Medium
N
News and Events Feed by Topic
Cloudbric
Cloudbric
T
The Exploit Database - CXSecurity.com
P
Proofpoint News Feed
A
About on SuperTechFans
Engineering at Meta
Engineering at Meta
Recent Commits to openclaw:main
Recent Commits to openclaw:main
B
Blog
V
Vulnerabilities – Threatpost
C
Check Point Blog
Google DeepMind News
Google DeepMind News
Google Online Security Blog
Google Online Security Blog
C
Cyber Attacks, Cyber Crime and Cyber Security
Hacker News - Newest:
Hacker News - Newest: "LLM"
C
Cisco Blogs
Schneier on Security
Schneier on Security
O
OpenAI News
K
Kaspersky official blog

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
Datadog Cloud SIEM: Driving innovation in security operations
2025-12-01 · via Datadog | The Monitor blog
Vera Chan

Vera Chan

Lance Malacara

Lance Malacara

Yash Kumar

Yash Kumar

Security can quickly become overwhelming for large organizations, with teams processing logs that are fragmented across cloud providers and SaaS platforms, staggering alert volumes, and the need to scale operations efficiently as environments grow. Datadog Cloud SIEM is designed to help teams manage this workload by centralizing insights, detecting threats faster, and prioritizing investigations with rich risk context.

With the addition of Bits AI Security Analyst, Datadog helps teams respond faster by automating triage and accelerating investigations. By working closely with customers, we’ve continued to expand our Cloud SIEM platform to enhance visibility, improve detection, and automate response.

As a result, users can easily:

Gain unified visibility and context across complex cloud environments

As organizations continue to expand across multi-cloud and SaaS ecosystems, security teams face growing complexity in how they collect, normalize, and analyze data. Logs often arrive in inconsistent formats and are scattered across sources, which makes it difficult to access a comprehensive, accurate view of organization-wide security activity. To detect threats and respond quickly, teams need a centralized and consistent view of all security data.

OCSF Common Data Model: Centralize and normalize logs with log pipelines

Datadog allows teams to ingest data from different sources and automatically normalize and map it to the Open Cybersecurity Schema Framework (OCSF) through prebuilt log pipelines. This OCSF Common Data Model creates a consistent structure for analysis, enabling security teams to correlate events across multiple cloud and SaaS platforms. This consistency allows for prebuilt detection rules that are source-agnostic and apply across multiple attack surfaces.

By automatically mapping disparate data to a consistent schema, analysts can correlate events, scale detection, and investigate threats faster without creating custom parsers or managing inconsistent formats. Teams can also access prebuilt detection rules through the out-of-the-box pipelines.

Screenshot of a Cloud SIEM dashboard showing where Okta and CloudTrail signals were triggered by the detection rule.

OCSF pipelines supported today include identity sources such as 1Password, Okta, and AWS CloudTrail; network sources such as Cisco Meraki and Palo Alto Networks; and endpoint sources such as CrowdStrike and Microsoft Windows.

OCSF Processor: Normalize custom data for consistent analysis

For teams with custom logs or specialized tools, the OCSF Processor provides a flexible, guided experience for users unfamiliar with the OCSF framework to normalize events to OCSF. This self-service capability reduces friction when integrating new data sources and helps teams maintain consistent visibility across all environments, even those with unique or legacy systems. Additionally, security teams may need to ingest and analyze new log sources faster than vendors can release new integrations. The OCSF Processor gives teams the flexibility to do so on their own by enabling them to transform and normalize custom logs into the OCSF framework. The processor also supports more complete detection coverage on their own timelines, providing a consistent path for incorporating new or custom data.

Screenshot showing how to create and configure an OCSF processor

Risk Insights: Prioritize effectively with identity and behavior insights

Risk Insights helps security teams correlate activity and user identity across multiple sources, giving analysts the context they need to investigate threats efficiently and prioritize the most important alerts. These insights draw on a number of variables, such as the severity level and frequency of any signals associated with an entity, to calculate an easy-to-interpret risk score. In addition to AWS, Google Cloud, and Azure entities, Risk Insights now also includes support for identity sources such as Okta and GitHub, with more integrations on the way.

Screenshot of the Risk Insights dashboard in Cloud SIEM that lists security threats to investigate.
Screenshot of a specific Risk Insight in Cloud SIEM that shows its risk assessment and other information.

Integrations and Content Packs: Broad coverage with fast onboarding

Integrations and Content Packs give security teams a fast way to build robust monitoring processes with minimal setup. Integrations enable teams to bring telemetry from endpoints, identity providers, email systems, cloud audit platforms, networks and infrastructure, and collaboration tools directly into Cloud SIEM. Content Packs include prebuilt detection rules, dashboards, and Workflow Automation blueprints, all of which help teams start monitoring and investigating critical systems without manual configuration.

Together, these capabilities give security teams a complete and consistent view of their environment, improve detection and investigation speed, and make it easier to scale monitoring across complex and growing infrastructure. Cloud SIEM includes integrations and Content Packs for more than 90 platforms, such as AWS CloudTrail, Google Workspace, Datadog Audit Trail, Microsoft 365, Nginx, 1Password, Auth0, Okta, Azure, Fastly, and Zendesk.

Screenshot of the Content Pack library showing the available Packs in Cloud SIEM.
Screenshot of the Content Pack library showing the available Packs in Cloud SIEM.

Detect and investigate threats faster

To detect threats quickly, security teams need to ingest and examine signals from their entire stack. However, this often leads to alert fatigue, and critical incidents risk being buried under noise.

Cloud SIEM connects signals across sources and automatically highlights related activity, helping analysts prioritize the most pressing incidents and investigate more efficiently. It also provides a foundation for faster, more targeted threat analysis.

Sequence Detections: Uncover coordinated attack patterns

Security incidents are rarely isolated events. Attackers often move through multiple stages, from initial access to privilege escalation and data exfiltration, leaving behind a series of seemingly unrelated signals.

With Sequence Detections, security teams can now identify and correlate these linked behaviors within Cloud SIEM. This capability allows analysts to define ordered sequences of events and specify combinations of users, actions, and time frames that should trigger a signal. By correlating related activity across sources and time windows, Datadog helps surface coordinated attacks that individual rules might otherwise miss.

As a result, teams gain a more complete picture of incidents and reduce noise from single-event detections, enabling faster, more confident investigation and response.

Screenshot showing how to set and define Sequence Detections for Okta.

Threat Intelligence: Enrich alerts with context you control

Effective investigations depend on context. With Threat Intelligence, analysts can enrich alerts with known indicators of compromise (IOCs), such as malicious IPs, domains, or file hashes, to better understand whether activity in their environment is part of a known campaign. By automatically correlating internal signals with relevant external threat data, analysts can prioritize alerts more effectively and respond faster with greater confidence.

Datadog’s built-in feeds provide global threat data, while Bring Your Own Threat Intelligence allows teams to integrate their own internal or third-party feeds into Cloud SIEM. This gives organizations full control over the threat data they rely on, whether it comes from commercial sources, industry sharing groups, or custom intelligence pipelines.

Automate and scale security operations with confidence

As enterprise environments grow, security teams manage more alerts, data sources, and workflows than ever before. Scaling operations efficiently while maintaining speed and accuracy is a constant challenge. Analysts need tools that help them automate repetitive tasks, simplify investigations, and adapt quickly to new threats and data sources—all without adding operational risk. Cloud SIEM helps teams automate and scale with confidence by combining AI-driven investigation, self-service data normalization, low-code automation, and migration tools that make modernization seamless.

Bits AI Security Analyst: Automate Cloud SIEM investigations

As alert volumes continue to increase, analysts spend significant time analyzing and triaging signals. Bits AI Security Analyst uses generative AI to autonomously investigate SIEM signals. Bits AI assesses whether a signal is benign or suspicious, summarizes key evidence, and shows detailed investigative steps with accompanying queries. This reduces the toil of manual triage and helps teams surface and resolve high-priority incidents faster. To learn more, you can sign up for the Security Analyst Preview.

Screenshot showing how Bits AI Security Analyst identified a potential security issue and rated an Amazon Bedrock discovery attempt by access key-denied attempt as benign.

SOAR blueprints: Automate response actions

Many response processes still depend on manual playbooks that vary across tools and teams. Security Orchestration, Automation, and Response (SOAR) Workflow Automation blueprints standardize and automate those common actions—enriching alerts, creating tickets, disabling accounts, or escalating incidents—directly within Datadog. Teams can respond consistently and confidently while maintaining human oversight for higher-risk decisions.

Screenshot showing a list of available Security Orchestration, Automation, and Response (SOAR) Workflow Automation blueprints

App Builder for security: Customize automation at scale

Every security team has unique processes that evolve as their environment changes. With App Builder, organizations can create custom apps that orchestrate actions across Datadog and third-party tools. This flexibility allows teams to adapt automation to their specific needs and scale their response playbooks as they grow. For example, teams can create apps that automatically block IP addresses and disable user accounts in response to suspicious activity, enabling faster remediation upon detection.

Screenshot of the Asset Investigation App homescreen showing different connected devices on the Device Overview tab.

Migration and automated detection rule conversion tools

Modernizing from legacy SIEMs can be time-consuming and risky if teams must rebuild detection logic from scratch. Datadog simplifies the transition by ingesting data from any source through pipelines and by supporting rapid onboarding and operationalization with Content Packs and an intuitive query language designed for fast adoption.

Teams can also work with our trusted partners to automate dashboard, alert, and detection rule conversion, which helps accelerate migration efforts. This support enables organizations to carry existing detections and workflows into Cloud SIEM with minimal disruption, reducing operational risk and accelerating the time to value. As Rishi Divate, Director of Business Development at Crest Data, a Datadog partner, notes, “Our partnership with Datadog accelerates observability and SIEM migrations by up to 60% and reduces cost, risk, and complexity.”

Migrating to a new SIEM is more than a technical shift. It’s an opportunity to reduce operational overhead and give security teams more time for higher-value work. In one recent migration, a security team consolidated platforms, eliminated 20 hours of weekly administrative work, and saw search performance increase by a factor of five compared to their previous system. They achieved 30% license cost savings, reduced AWS egress costs by $120,000, and gained 365 days of instantly searchable logs via Flex Logs—eliminating cold-storage delays and expensive rehydration cycles. As a result, the team now spends less time on maintenance and more time advancing their security program with greater speed and confidence.

Start safely scaling your security operations

Across industries, customers are using Datadog Cloud SIEM to modernize how they detect, investigate, and respond to threats in the cloud. By unifying visibility across complex environments, accelerating threat detection, and automating investigation and response, Cloud SIEM enables security teams to keep pace with the scale and speed of modern infrastructure.

See our documentation for more information and to get started. If you’re new to Datadog, you can sign up for a 14-day free trial to get started.