Integrating SIEM with EDR: CrowdStrike SentinelOne and Defender

Why Integrate SIEM and EDR?

Endpoint detection and response platforms are designed to monitor and protect devices at the endpoint layer, but their scope is inherently limited to that domain. An integrated SIEM and EDR architecture provides several specific operational advantages that justify the investment in integration engineering and ongoing data pipeline maintenance:

• End-to-end visibility: A SIEM correlates EDR telemetry with network, identity, cloud, and application logs to reconstruct the complete attack timeline rather than isolated endpoint snapshots.
• Reduced alert fatigue: EDR alerts alone can overwhelm SOC analysts, especially in large deployments. SIEM aggregation and deduplication, combined with risk-based prioritization, ensures that only the most critical events reach human analysts.
• Advanced behavioral analytics and UEBA: User and entity behavior analytics (UEBA) built into next-generation SIEM platforms like ThreatHawk SIEM can baseline normal behavior across endpoints and users, detecting anomalous patterns that rule-based detection might miss — such as a user suddenly accessing protected file shares across multiple endpoints after hours.
• Compliance readiness: Many compliance frameworks require centralized log management, retention, and access controls. A SIEM that ingests EDR logs alongside other sources provides a single pane of glass for audit evidence generation and reporting.
• Threat hunting enablement: Proactive threat hunting relies on querying across multiple data sources. Integrated SIEM-EDR environments allow hunters to pivot from an endpoint process to the associated network flow, user identity, and cloud resource in a single query interface.

The Integration Architecture: How SIEM and EDR Connect

Understanding the technical architecture behind SIEM-EDR integration helps security architects evaluate which approach works for their environment. There are three primary integration models:

• Direct API-based integration: The EDR platform exposes REST APIs that the SIEM uses to pull alerts, telemetry, and forensic data. This is the most common approach for modern SaaS-based EDR platforms like CrowdStrike, SentinelOne, and Defender for Endpoint. The SIEM typically uses OAuth 2.0 authentication, polls for new events at configurable intervals, and supports real-time streaming where the EDR platform supports webhook-based push delivery.
• Event forwarding through syslog: Some EDR platforms support forwarding specific alert types via syslog (TCP/UDP 514) or over TLS via CEF (Common Event Format) or JSON payloads. This approach is less flexible than API integration — it typically sends only alert data, not full telemetry streams — but it works well for environments with strict network segmentation constraints or legacy SIEM platforms that lack modern API connectors.
• Endpoint telemetry pipeline: A data pipeline tool (logstash, fluentd, or a cloud-native ingestion service) collects EDR telemetry from the endpoint agents directly or from the EDR platform's cloud broker, transforms it, and forwards it to the SIEM. This model offers the highest granularity because it can capture raw process creation events, file modification events, and network connection logs that the EDR platform's API might not expose through its higher-level interfaces.

Each model has trade-offs in data fidelity, latency, and operational overhead. Most enterprise SOCs use a combination: API-based ingestion for prioritized alerts and forensic data, with syslog or pipeline-based ingestion for full telemetry streams used in threat hunting and long-term analysis.

CrowdStrike Falcon and SentinelOne Singularity: Two Integration Case Studies

To provide actionable guidance, we examine how CrowdStrike Falcon and SentinelOne Singularity integrate with SIEM platforms, then contrast these with Microsoft Defender for Endpoint's integration model, which benefits from being in the same ecosystem as Azure Sentinel and Microsoft 365 Defender.

CrowdStrike Falcon SIEM Integration

CrowdStrike Falcon exposes its data through the CrowdStrike API (Falcon API) and supports both REST API polling and streaming event delivery via Event Streams. Key integration characteristics include:

• Real-time event streaming: CrowdStrike Event Streams push over 100 different detection types (process creation, network connections, file modifications, registry changes, DNS queries) via AWS Kinesis or direct HTTPS webhooks.
• Rich data schema: Each event includes comprehensive fields — process ID, parent process hash, command-line arguments, user context, network destination IP and port, file path, and reputation scores from CrowdStrike's intelligence.
• Detection and incident mapping: The SIEM can classify CrowdStrike detections by severity, confidence, and technique (MITRE ATT&CK mapping), which is critical for prioritization and alert triage.
• Automated response capabilities: Some SIEM platforms, including ThreatHawk SIEM, can issue API-based response actions back to CrowdStrike — such as isolating endpoints, killing processes, or blocking IOCs — orchestrating response workflows that span the SIEM and EDR layers.

To integrate CrowdStrike Falcon with a SIEM, organizations typically deploy a connector that authenticates to the Falcon API using a dedicated API client ID and secret, subscribes to the relevant event streams, and forwards events into the SIEM's ingestion pipeline. The most common deployment patterns use AWS Event Bridge or a dedicated microservice that normalizes CrowdStrike's JSON payloads into the SIEM's schema.

SentinelOne Singularity SIEM Integration

SentinelOne's integration model is similar in concept to CrowdStrike's but differs in some specific implementation details that architects should understand:

• Management API: SentinelOne exposes a RESTful Management API (v2.1) that provides access to threats, agents, activities, and policy configurations. The API supports OAuth 2.0 Client Credentials flow for server-to-server authentication.
• Streaming export: SentinelOne supports real-time streaming export of telemetry events to data pipelines including AWS S3, Azure Blob Storage, Google Cloud Storage, and syslog. This allows SIEMs to ingest data directly from cloud storage buckets using event-driven ingestion.
• MITRE ATT&CK mapping: SentinelOne automatically maps detected behaviors to MITRE ATT&CK framework tactics and techniques, providing structured metadata that SIEMs can use for correlation with other sources that share the same ontology.
• Deep visibility agent: The SentinelOne agent captures granular endpoint information including process trees, file system interactions, registry changes, and network connections, all of which can be forwarded to the SIEM at configurable verbosity levels.

The practical integration pattern for SentinelOne involves setting up a streaming export target (typically an AWS S3 bucket or syslog server) from the SentinelOne Management Console, then configuring the SIEM to ingest from that target. Organizations running large SentinelOne deployments (10,000+ endpoints) should consider using S3 event notifications with Lambda or similar compute triggers to process and forward data in near-real time rather than relying on periodic polling.

Integrating Microsoft Defender for Endpoint with SIEM

Microsoft Defender for Endpoint (MDE) occupies a unique position because it can be integrated with Azure Sentinel (now Microsoft Sentinel) natively and with third-party SIEMs through API and syslog pathways. Organizations using MDE typically fall into two camps: those running Microsoft Sentinel as their SIEM, and those running third-party SIEMs like ThreatHawk SIEM, Splunk, ELK, or QRadar.

• Microsoft Sentinel (native integration): MDE integrates natively with Microsoft Sentinel through the Microsoft 365 Defender connector, which ingests advanced hunting tables (DeviceEvents, DeviceProcessEvents, DeviceNetworkEvents, DeviceFileEvents, DeviceRegistryEvents, DeviceLogonEvents) directly into Sentinel's data lake. This provides the deepest integration, with full telemetry streams and automatic mapping to Sentinel's schema and analytics rules.
• Third-party SIEM via MDE API: MDE exposes the Microsoft 365 Defender APIs (including the Advanced Hunting API) that allow third-party SIEMs to query and ingest telemetry. The API uses OAuth 2.0 and supports both polling for changes and scheduled query-based exports.
• Syslog/CEF forwarding: For environments that need a lighter weight integration, MDE can forward alerts to a syslog server via the Microsoft 365 Defender security portal's forwarding rules. This sends alert-level data (detection time, severity, affected devices, recommended actions) but not full telemetry streams.
• Graph security API: Microsoft Graph Security API provides a unified interface for ingesting alerts from across MDE, Microsoft Entra ID Protection, Microsoft Purview, and other Microsoft security services into the SIEM.

Organizations running ThreatHawk SIEM can integrate MDE through the Advanced Hunting API to ingest full event telemetry, then correlate endpoint events with Windows Event Logs (security, sysmon, PowerShell), Active Directory sign-in logs, Azure audit logs, and network data — all within a single investigation workspace.

Implementation Playbook: A Phased Approach to SIEM-EDR Integration

Integrating SIEM with EDR requires careful planning to avoid overwhelming the SIEM with duplicate data or creating blind spots due to misconfigured ingestion policies. The following phased approach has been validated across enterprise deployments and aligns with best practices from the SIEM solution process framework.

How ThreatHawk SIEM Handles EDR Integration at Enterprise Scale

ThreatHawk SIEM was architected for organizations that need to process massive volumes of endpoint telemetry alongside their existing log infrastructure without sacrificing query performance or alert latency. Key architectural features that support EDR integration at enterprise scale include:

• High-throughput ingestion pipeline: Built on a distributed streaming architecture, ThreatHawk SIEM can ingest millions of EDR events per second with sub-second latency, using horizontal scaling across worker nodes. This eliminates the need to throttle EDR telemetry during peak incident activity.
• Pre-built EDR connectors with field normalization: The SIEM includes curated connectors for CrowdStrike, SentinelOne, and Microsoft Defender for Endpoint that automatically normalize endpoint telemetry into a consistent schema, mapping fields like process ID, parent process hash, MITRE technique ID, and detection severity into the same ontology regardless of which EDR platform generated the event.
• Multi-source correlation engine: ThreatHawk's correlation engine supports temporal, spatial, and behavioral correlations across EDR events and any other data source in the platform — including network flows (NetFlow/IPFIX), proxy logs, cloud audit logs (AWS CloudTrail, Azure Activity Log), identity logs (Active Directory, Azure AD), and database audit logs.
• UEBA for endpoint-user interactions: The built-in UEBA module baselines normal user behavior on endpoints — including application usage patterns, access times, and file interaction patterns — and flags deviations that could indicate compromised credentials or insider threats.
• Compliance-ready log retention: EDR telemetry ingested into ThreatHawk is stored in an immutable, tamper-proof log store with configurable retention periods that align with SOC 2, PCI DSS, HIPAA, and NIST 800-53 requirements. Audit trails can be generated with a single click for any compliance review.

What Does It Cost to Integrate SIEM with EDR?

Integration costs involve both licensing and operational overhead. Understanding these costs helps in budgeting and in selecting the right integration model for your environment. For a detailed breakdown of SIEM platform pricing, refer to our SIEM tool cost guide.

Organizations that choose a SIEM with pre-built connectors (like ThreatHawk SIEM) and support from the vendor's professional services team typically see 30–50% lower initial integration costs compared to custom-building a connector using open-source tooling. Additionally, platforms that offer data deduplication and intelligent filtering (ingesting only high-fidelity events rather than every raw telemetry stream) reduce ongoing data ingestion costs by 40–60%.

Threat Hunting in an Integrated SIEM-EDR Environment

One of the most powerful capabilities of an integrated SIEM-EDR environment is the ability to perform proactive threat hunting across data sources that are typically siloed. A threat hunter using ThreatHawk SIEM with CrowdStrike, SentinelOne, or Defender for Endpoint can execute queries that combine endpoint telemetry with network flows, identity logs, and cloud audit data to surface advanced attacks that no single source would reveal.

Example hunting scenarios enabled by integration include:

• Lateral movement detection: Query for sequences where an endpoint executes remote admin tools (wmic, psexec, winrm) and the same user account seconds later authenticates to another endpoint via RDP or administrative shares. The SIEM correlates the endpoint process event with the Windows Security Event Log (Event ID 4624/4648) from the target system to confirm lateral movement.
• Living-off-the-land binary abuse: Hunt for patterns where legitimate system binaries (powershell.exe, bitsadmin.exe, mshta.exe) are invoked with command-line patterns that match known abuse techniques, correlated with network connections to suspicious IP addresses or domains. The SIEM can join EDR process creation events with DNS query logs and proxy logs to verify the full kill chain.
• Data exfiltration preparation: Identify endpoints that are compressing or archiving files (using rar.exe, 7z.exe, or PowerShell Compression cmdlets) while simultaneously establishing outbound connections to external IPs that are not within your organization's approved third-party list. The SIEM correlates the compression event from EDR with network flow data to identify potential exfiltration staging.
• Kerberos golden ticket detection: Correlate Kerberos authentication anomalies from domain controller logs with process creation events on endpoints where a user account suddenly creates service tickets for privileged accounts — a behavioral pattern indicative of golden ticket attacks.

How This Integration Differs Between Legacy and Next-Gen SIEM Platforms

Organizations that have previously integrated EDR with a legacy SIEM platform (typically SQL-based, with batch processing and limited scalability) will find significant differences when moving to a next-generation SIEM like ThreatHawk. The distinction between legacy and next-gen SIEM platforms is covered in detail in our article on SIEM vs next-gen SIEM, but the key differences relevant to EDR integration include:

• Real-time vs. batch ingestion: Legacy SIEMs often poll EDR APIs at intervals of 5–15 minutes, introducing latency that can delay detection during fast-moving attacks. Next-gen SIEMs support sub-second streaming ingestion through webhooks, event brokers, and distributed data pipelines.
• Schema flexibility: Legacy platforms require rigid log parsing and field mapping that must be updated whenever the EDR vendor changes its data schema. Next-gen SIEMs use schema-on-read or dynamic schema mapping that adapts to evolving EDR telemetry formats without requiring manual connector updates.
• Scalability for telemetry enrichment: Next-gen SIEMs are built on distributed data architectures (similar to data lakes) that allow ingestion of full EDR telemetry streams, not just alert-level data. This enables richer threat hunting and forensic analysis that is simply not possible with legacy platforms limited to alert correlation.
• AI and machine learning integration: Next-gen SIEMs incorporate AI-driven correlation and UEBA that can process endpoint, network, and identity data in a unified machine learning model. Legacy SIEMs typically lack this capability, relying instead on static rule-based correlation that generates higher false positive rates.