Preamble
Security investigations rarely stay confined to a single host. Today’s attackers increasingly use automation and AI to compress multi-stage attacks into minutes, turning what once unfolded over days into coordinated activity across endpoints, identities, workloads, and cloud services within minutes.
While many attacks begin on an endpoint, investigators must quickly determine how that activity spreads across the environment. In many environments, per-endpoint licensing limits how broadly protection and telemetry can be deployed, creating protection gaps during these investigations.
Elastic Security XDR is built around that reality. It includes best-in-class endpoint protection, without per-endpoint licensing constraints, in an agentic security operations platform where endpoint telemetry, infrastructure signals, and supporting artifacts can be analyzed together.
This post explores how Elastic Security XDR supports investigations across endpoints, workloads, and the broader environment, highlighting tools and workflows that help analysts collect evidence, pivot across telemetry, and respond efficiently.
Endpoint at the heart of XDR
The 2025 Elastic Global Threat Report reveals that with 90% of malware targeting Windows, and browsers acting as the 'primary battleground', host-level visibility is essential to stopping a breach before it scales to the cloud. Elastic Defend, Elastic Security’s native endpoint protection, powers XDR from the endpoint outward. It not only prevents threats across Windows, macOS, and Linux, but also generates rich, investigation-grade telemetry that gives analysts the context they need to understand what happened on a host.
As activity occurs, Elastic Defend captures system events including process execution, file changes, network connections, and related artifacts. This telemetry forms the foundation for broader investigations, allowing analysts to correlate endpoint behavior with activity across workloads, identities, and other systems.
Multiple detection layers protect against malware, ransomware, fileless techniques, and other malicious behaviors, using both static and behavioral analysis. Independent validation from the AV-Comparatives Business Security Test confirms Elastic’s effectiveness; in the 2025 test cycle, Elastic Security was the only vendor that blocked every tested threat, earning perfect scores in both Real-World Protection and Malware Protection.
Elastic also takes a principled approach to openness. Unlike many endpoint security tools that operate as a black box, Elastic publishes detection and prevention logic in an open repository. This transparency lets analysts understand how protections work, validate them in their own environments, and prioritize high-risk gaps. By empowering users with visibility and insight, Elastic ensures security teams can act with confidence and maximize the value of their investigations.
Beyond the endpoint: expanding the investigation
Attacks rarely stay confined to a single host. Credentials may be compromised, workloads modified, or activity spread across cloud services and infrastructure. To fully understand an incident, analysts need to correlate endpoint activity with signals from the broader environment.
Elastic Security XDR enables this by bringing multiple data sources into the same analysis environment through hundreds of integrations with popular security tools and data sources. Endpoint telemetry,whether collected by Elastic Defend or another EDR platform, can be analyzed alongside cloud activity, identity events, network telemetry, and third-party logs, without forcing organizations into a closed security stack. Elastic provides the common schema and unified detection engine required to normalize disparate signals, allowing analysts to bypass manual data mapping and immediately pivot between sources to follow how activity moves across users, systems, and infrastructure.
Centralized detection rules operate across the unified dataset in the security platform, complementing real-time protections that run directly on the endpoint. They enable alerts to reflect correlated activity across multiple domains. Suspicious process activity on a host can be matched with identity events, cloud API calls, or network behavior, helping analysts determine whether an event is isolated or part of a larger attack chain.
Container workloads highlight another way XDR extends investigations. Elastic Defend for Containers monitors runtime behavior inside containerized environments, detecting suspicious activity such as unexpected process execution, privilege escalation, or access to sensitive resources. By connecting endpoint behavior to the broader environment, Elastic Security XDR gives analysts the visibility needed to scope incidents accurately, prioritize critical threats, and respond with confidence.
Reconstructing the attack path
After relevant telemetry is collected, analysts need to piece together what happened and how the attack progressed. Investigations involve pivoting between events, validating hypotheses, and assembling a complete timeline of activity across the environment.
Elastic Security XDR provides investigation tools designed to support this process. Visual Event Analyzer, Session View, and Timeline allow analysts to explore relationships between events, trace execution chains, and correlate activity across datasets while maintaining investigative context.
Visual Event Analyzer offers a graphical view of process relationships, helping analysts spot suspicious parent-child behavior and understand execution flows. Session View reconstructs activity within a process session, showing commands, network connections, and other actions as they unfolded. Timeline acts as an investigative workspace where analysts collect and correlate events from multiple sources, refine queries, and build a coherent attack narrative.
Together, these tools help analysts validate hypotheses faster, deepen analysis, and enable more confident response decisions.
Agentic investigation: discovery, summarization, and natural language querying
Elastic Security’s AI-driven investigative workflows help analysts keep pace with modern attacks by accelerating investigation and surfacing connected activity across the environment. Attack Discovery identifies connected alerts across endpoints, workloads, cloud services, and integrated third-party data, helping analysts uncover hidden attack chains without manually correlating events.
Once an investigation is underway, Elastic AI Assistant and Agent Builder enable natural-language workflows that let analysts interact with data and automation more efficiently. Analysts can summarize observations, ask questions about entities and activity, and move seamlessly from supporting signals to containment or remediation actions. With the introduction of agent skills, teams can now extend these workflows with reusable, task-specific capabilities, such as alert triage, rule management, and case handling, allowing the assistant to execute complex, multi-step security tasks with the same consistency and repeatability as traditional automation, but through a conversational interface.
In practice, these capabilities reduce the time from an initial alert to full incident understanding, allowing SOC teams to respond faster, focus on high-priority threats, and act with confidence.
Built-in forensics and host artifact collection
During incident response, investigators often need to retrieve additional host artifacts to confirm attacker behavior, identify persistence, or validate user activity.
Elastic Security XDR includes built-in forensic capabilities that allow responders to collect investigative artifacts directly from affected hosts, reducing the need for separate forensic tooling during common investigative tasks. Elastic Defend supports capturing memory snapshots for deeper forensic analysis, while Osquery Manager enables analysts to run targeted queries to gather and examine host artifacts as part of an investigation.
Forensic visibility is further extended through ongoing collaboration with Osquery. By extending Osquery-based forensics with supplemental tables for common investigative artifacts, Elastic helps uncover evidence such as browser history, AMCache records, and jumplist artifacts. These sources make it easier for analysts to examine user activity and execution history on Windows systems during an investigation. Also available is library of prebuilt forensic queries and packs to extract common investigative artifacts across Windows, macOS, and Linux, including:
- process listings and execution context
- scheduled tasks, startup items, and persistence mechanisms
- shell history and command execution artifacts
- network configuration and connectivity context
- file hashes and other execution-related artifacts
These capabilities turn artifact collection into an embedded step of the investigation, rather than a separate workflow, so teams can confirm what happened all in one platform and act sooner.
Response actions that keep investigations moving
Once investigators confirm malicious behavior, the priority shifts to containment and remediation. Elastic Security XDR enables analysts to take immediate action directly from the investigation context, isolating a host, terminating suspicious processes, collecting a file from the endpoint, or running a response script to collect additional evidence needed to complete the analysis.
For organizations using third-party EDRs, Elastic Security XDR can orchestrate containment and response across mixed environments, allowing teams to keep investigation, enforcement, and incident record-keeping anchored in a single platform.
Controlling removable media with Device Control
Investigations often uncover risk paths beyond traditional malware, such as removable media usage or potential USB-based exfiltration. Elastic Security XDR’s Device Control capabilities let teams manage and enforce removable media policies across endpoints, reducing attack surface and preventing unauthorized data transfer.
Device Control also allows teams to automatically block USB devices and maintain a trusted set of approved devices, ensuring policies are enforced consistently across all endpoints.
Scaling response with Elastic Workflows
Incident response often follows repeatable steps. When an alert fires, teams enrich it, gather evidence, contain affected hosts, open cases, notify responders, and document decisions, ensuring investigations persist across handoffs and shift changes.
Elastic Workflows gives teams a way to encode those steps as a reusable playbook that runs inside the Elastic platform. Workflows are defined declaratively in YAML in Kibana, and can be triggered in multiple ways: when a Kibana alerting rule fires, on a schedule, or manually on demand.
From there, a workflow can execute a sequence of steps that look a lot like what an analyst would do manually:
- Query Elastic data (including ES|QL), transform results, and branch based on conditions
- Create or update a Case, attach supporting context, and keep an auditable record of what was collected and why.
- Notify downstream systems (Slack, Jira, PagerDuty, and other services) using connectors you’ve already configured, or call internal/external APIs via HTTP steps.
This becomes especially impactful when paired with endpoint response capabilities. When an alert fires, teams can automatically isolate the host and kick off a standardized evidence bundle - capture a memory dump, collect a suspicious file (get-file), and list running processes - so responders have what they need immediately.
The net effect is faster execution of the first steps in incident response, while investigations follow consistent playbooks across analysts and shifts. Instead of relying on memory and manual checklists, Workflows helps enforce a repeatable investigation standard and makes it easier to scale response when alert volume spikes.
Elastic Security Labs - Research that powers real-world defenses
Elastic Security is informed by the work of Elastic Security Labs, a team dedicated to studying real adversary behavior and translating those findings into practical detection and investigation guidance. Threat Command tracks emerging techniques, malware activity, and endpoint tradecraft, then turns that research into updates that matter in day-to-day security operations: new and refined detection rules, improvements to prevention logic, and clearer guidance on how to investigate what you’re seeing.
Elastic Security Labs also publishes technical write-ups and analyses to help the broader community understand how threats operate in the wild. For defenders, that research provides useful context behind detections - why a technique matters, what evidence to look for, and how to scope impact once an alert fires.
Tying it all together
As a core capability of our agentic security operations platform, Elastic Security XDR unifies traditionally siloed defenses to tackle the speed and complexity of modern threats. An initial host-based signal can quickly spread across endpoints, identities, and cloud services. Agentic workflows and agent skills help analysts investigate and respond at machine speed. Analysts no longer need to stitch together disconnected tools - they can follow attacker activity throughout the environment, combining endpoint prevention with autonomous investigative and response capabilities in a single platform.
Learn More
Visit elastic.co/security/xdr to learn more. Try a free Elastic Security trial, explore Elastic Defend with our Getting Started video, or practice with real malware at ohmymalware.com.
Facts Only
Elastic Security XDR is a security platform that integrates endpoint protection, telemetry analysis, and cross-environment investigation tools.
Elastic Defend is the native endpoint protection component, supporting Windows, macOS, and Linux.
The platform captures system events such as process execution, file changes, and network connections for investigative telemetry.
Elastic Security XDR supports over 100 integrations with third-party security tools and data sources.
The platform includes AI-driven features like Attack Discovery and the Elastic AI Assistant for investigative workflows.
Forensic capabilities include memory snapshots, Osquery-based artifact collection, and prebuilt queries for common investigative artifacts.
Device Control allows management of removable media policies across endpoints.
Elastic Workflows automates repetitive response tasks using YAML-defined playbooks.
Elastic Security Labs conducts research on adversary behavior to inform detection rules and investigative guidance.
In the 2025 AV-Comparatives Business Security Test, Elastic Security achieved perfect scores in Real-World Protection and Malware Protection.
The platform provides a unified detection engine and common schema to normalize disparate security signals.
Elastic publishes detection and prevention logic in an open repository for transparency.
The platform is designed to address multi-stage attacks that span endpoints, identities, workloads, and cloud services.
Executive Summary
Elastic Security XDR is a comprehensive security platform designed to address modern cyber threats by integrating endpoint protection, telemetry analysis, and cross-environment investigation capabilities. At its core, Elastic Defend provides endpoint protection for Windows, macOS, and Linux, generating detailed telemetry on system events such as process execution, file changes, and network connections. This data serves as the foundation for broader investigations, allowing analysts to correlate endpoint behavior with activity across workloads, identities, and cloud services. The platform supports over 100 integrations with third-party security tools, enabling unified analysis without requiring a closed security stack. Key features include AI-driven investigative workflows, such as Attack Discovery and the Elastic AI Assistant, which help analysts identify connected threats and respond more efficiently. Forensic capabilities, such as memory snapshots and Osquery-based artifact collection, are built into the platform, streamlining incident response. Elastic Security XDR also includes Device Control for managing removable media and Elastic Workflows for automating repetitive response tasks. The platform is informed by research from Elastic Security Labs, which translates real-world adversary behavior into practical detection rules and investigative guidance. The goal is to provide a unified, agentic security operations platform that reduces the time from alert to response while maintaining flexibility across diverse IT environments.
The platform's effectiveness is supported by independent validation, including a perfect score in the 2025 AV-Comparatives Business Security Test. Elastic emphasizes transparency by publishing detection and prevention logic in an open repository, allowing users to validate and customize protections. The platform is designed to handle the speed and complexity of modern attacks, which often involve automated, multi-stage techniques that span endpoints, identities, and cloud services within minutes. By integrating endpoint protection with broader telemetry and AI-driven analysis, Elastic Security XDR aims to help security teams respond faster and with greater confidence.
Full Take
**STEELMAN:** Elastic Security XDR presents a compelling case for a unified, transparent, and AI-augmented approach to modern cybersecurity. The platform’s integration of endpoint protection with broader telemetry analysis addresses a critical gap in traditional security tools, which often operate in silos. The emphasis on openness—such as publishing detection logic and supporting third-party integrations—aligns with the growing demand for vendor-agnostic solutions. The inclusion of AI-driven investigative tools and automated workflows reflects a realistic response to the speed and scale of contemporary threats, where attacks can unfold across multiple domains in minutes. Independent validation, such as the AV-Comparatives test results, lends credibility to its technical claims. By embedding forensic capabilities and response actions directly into the investigation workflow, Elastic reduces friction in incident response, a persistent challenge for security teams.
**PATTERN SCAN:** The narrative leans heavily on authority appeals, citing independent test results and the expertise of Elastic Security Labs to bolster its claims. While these are legitimate forms of validation, the framing occasionally borders on solutionism—the implication that a single platform can fully address the complexity of modern cyber threats. There’s also a subtle appeal to urgency, emphasizing the speed of attacks and the need for rapid response, which could pressure organizations into adopting the platform without fully evaluating alternatives. However, the transparency around detection logic and the absence of overt fear-mongering mitigate these concerns.
**ROOT CAUSE:** The underlying paradigm here is the recognition that cybersecurity is no longer just about perimeter defense but about visibility, correlation, and rapid response across hybrid environments. The narrative assumes that traditional, siloed security tools are inadequate for modern threats—a claim supported by industry trends but one that also serves Elastic’s commercial interests. The focus on AI and automation reflects a broader shift in cybersecurity toward machine-speed decision-making, which raises questions about the balance between human judgment and algorithmic efficiency.
**IMPLICATIONS:** For security teams, this platform could significantly reduce the cognitive load of investigations by automating correlation and response tasks. However, the reliance on AI-driven workflows may also introduce new risks, such as false positives or over-automation in critical decision points. The open detection logic is a double-edged sword: while it empowers users, it also places the burden of validation on them, which smaller teams may struggle with. The platform’s effectiveness will depend on how well it integrates with existing tools and whether organizations can adapt their workflows to its agentic model.
**BRIDGE QUESTIONS:**
How does Elastic Security XDR handle false positives in its AI-driven detection, and what safeguards exist to prevent over-reliance on automation?
What are the trade-offs between the platform’s openness (e.g., published detection logic) and the potential for adversaries to exploit that transparency?
For organizations with limited resources, does the platform’s complexity create new challenges in implementation and maintenance?
**COUNTERSTRIKE SCAN:** If this were part of a coordinated influence campaign, the playbook would likely emphasize the inevitability of AI-driven security, frame traditional tools as obsolete, and use third-party validations to create a sense of urgency. The actual content aligns with this pattern to some extent, particularly in its focus on speed and automation. However, the transparency around detection logic and the absence of exaggerated claims about "unhackable" systems suggest a more measured approach. The narrative doesn’t demonize competitors or resort to fear-based messaging, which distinguishes it from more manipulative campaigns.
**Patterns detected: ARC-0024 Ambiguity (subtle urgency framing), ARC-0043 Motte-and-Bailey (broad claims about "modern threats" without specific adversary examples)**
Sentinel — Human
The article exhibits strong human authorship signals, including product-specific details, idiosyncratic phrasing, and technical depth inconsistent with generic AI generation. Minimal stylometric or coherence red flags suggest a human writer, likely with deep familiarity with Elastic Security XDR.
