What is the Difference Between EDR and SIEM?

Quick Definitions

What Is EDR?

Endpoint Detection and Response (EDR) is a security solution installed on devices like laptops, desktops, and servers. It continuously records detailed activity on each endpoint — including process behavior, file changes, registry edits, memory activity, and network connections. EDR provides real-time endpoint monitoring and threat detection, giving SOC teams actionable intelligence.

EDR uses behavioral analytics, machine learning models, and threat detection rules to identify suspicious actions immediately. It can also respond automatically, such as isolating a compromised device, terminating malicious processes, or quarantining harmful files.

By capturing forensic-level data, EDR helps security analysts perform detailed investigations and understand attack methods.

In short: EDR's main purpose is to detect, investigate, and contain threats directly on the host with deep forensic visibility. It is particularly effective against malware, ransomware, fileless attacks, and in-memory threats.

What Is SIEM?

Security Information and Event Management (SIEM) is a centralized platform that collects, normalizes, and analyzes logs from across the entire IT environment — including networks, servers, cloud platforms, identity systems, applications, and even EDR alerts. SIEM enables enterprise-wide visibility and centralized threat monitoring, helping teams spot complex attack patterns.

It correlates data from multiple sources, highlights anomalies, and supports threat hunting and incident response. SIEM also provides long-term log retention for auditing, compliance, and reporting requirements. By integrating with other security tools, SIEM allows SOC teams to detect advanced persistent threats and coordinate investigations across the organization.

In short: SIEM's primary mission is to connect signals from many systems, enable threat hunting, support incident response, and provide long-term, searchable security data. It helps security teams monitor trends, generate alerts, and maintain regulatory compliance.

Where They Operate and Who Uses Them

Core Technical Differences

1) Data Scope and Granularity

2) Timing and Processing Model

3) Analytics Engines and Suited Detections

Telemetry and Data Sources

What EDR Collects (Examples)

EDR (Endpoint Detection and Response) collects detailed, high-fidelity data directly from endpoints. This includes:

• Process start/exit events with parent/child relationships to track application behavior.
• File activity such as create, read, write operations, and file hashes for integrity checks.
• Registry access and configuration changes on Windows devices to detect suspicious modifications.
• Network connections originating from the host, including DNS queries and socket connections.
• Memory snapshots, module loads, loaded DLLs, and in-memory indicators to detect fileless attacks.
• Sensor artifacts for forensic triage, such as timeline data and snapshots, to support detailed investigations.

EDR telemetry is designed for endpoint-level threat detection, live response, and deep forensic analysis. Collecting this granular data allows SOC analysts and incident responders to quickly identify malicious behavior, investigate root causes, and remediate threats before they spread.

It also helps security teams understand attack patterns and detect advanced threats like ransomware or in-memory exploits. This data is crucial for continuous endpoint monitoring and improving overall threat intelligence. EDR also helps in creating actionable alerts and supporting automated or manual containment of attacks.

What SIEM Collects (Examples)

SIEM (Security Information and Event Management) gathers logs and telemetry from across the enterprise environment. This includes:

• Syslog feeds from network devices like routers, firewalls, and switches.
• Windows Event Logs and audit logs from servers and workstations.
• Cloud provider audit trails, such as AWS CloudTrail or Azure Activity Logs.
• Identity provider logs including Okta, Azure AD, and VPN authentication records.
• Application logs, Data Loss Prevention (DLP) alerts, and aggregated endpoint alerts.

SIEM centralizes and correlates this data to provide enterprise-wide visibility, detect multi-step attacks, and support threat hunting. It helps security teams spot patterns across systems that individual endpoints alone cannot reveal. SIEM also enables compliance reporting, historical analysis, and monitoring of unusual user or network activity.

By integrating telemetry from multiple sources, SIEM helps identify complex attack chains and unusual behavior trends. It also supports security reporting, risk assessment, and proactive threat management across the enterprise.

Data Retention, Normalization, and Searchable Histories

Detection and Analytics Approaches of EDR and SIEM

EDR Approaches

EDR (Endpoint Detection and Response) focuses on detecting threats directly on endpoints using detailed device telemetry. Key approaches include:

• Behavioral detection: Identifies anomalies and suspicious activity based on process behavior, sequence, and execution patterns. This allows SOC teams to detect subtle signs of attacks that might bypass signature-based tools. It also helps in spotting abnormal activity before it causes significant damage, supporting proactive endpoint threat management.
• Machine learning (ML) models: Trained for host activity and adversary tactics, enabling detection of unknown or evolving threats. ML models help in identifying advanced malware and fileless attacks that traditional detection methods may miss. These models continuously learn from endpoint telemetry, improving detection accuracy over time.
• Hunting queries and process tree analysis: Allows analysts to investigate threats iteratively and track malicious processes across endpoints. It also helps in building detailed endpoint threat intelligence and improving future detection rules. Hunting queries enable SOC teams to uncover hidden threats and validate alerts from automated detection systems.

SIEM Approaches

SIEM (Security Information and Event Management) focuses on analyzing and correlating data from across the enterprise. Key approaches include:

• Rule-based correlation: Connects alerts and logs from multiple sources, detecting patterns that span hosts, users, and networks. It helps SOC teams identify multi-step attacks and suspicious behavior across systems. This method allows teams to generate actionable alerts from large volumes of enterprise logs efficiently.
• Statistical anomaly detection and User Behavior Analytics (UBA): Identifies unusual patterns over time and flags deviations from normal activity. This approach helps in detecting compromised accounts, insider threats, and abnormal access patterns. UBA also enables teams to detect subtle behavioral changes that indicate early-stage attacks.
• Threat intelligence enrichment and graph analytics: Detects lateral movement, coordinated attacks, and multi-step intrusions across systems. It allows teams to visualize attack paths and understand the full scope of incidents. Graph analytics provide a clear picture of attack propagation and support more effective incident response.

Rules vs Behavioral/ML Trade-offs

• Deterministic rules: Easy to understand, validate, and explain. They are reliable for known attack patterns but can miss novel threats. Many rules may be needed to cover all scenarios, which can increase maintenance. Rules are most effective for predictable threats and consistent detection across large datasets. Rules also provide clear audit trails for compliance and help SOC teams justify security decisions. They are critical for repeatable and explainable detection across multiple environments.
• Behavioral and ML models: Can detect unknown or evolving attacks by analyzing patterns in endpoint or log telemetry. They require high-quality data, proper model training, and tuning to minimize false positives. ML-based analytics are essential for identifying sophisticated threats like ransomware, fileless malware, and advanced persistent threats (APTs). Behavioral models adapt over time and can catch subtle threats that traditional rules miss. They also enhance threat hunting by revealing patterns in complex attack sequences.

Choosing the right approach depends on your security goals: rules provide consistency and explainability, while behavioral and ML-driven analytics increase detection of advanced threats. In practice, combining both approaches ensures comprehensive threat detection and improves overall security operations.

Organizations that leverage both rule-based and behavioral analytics benefit from faster detection, reduced risk exposure, and stronger incident response capabilities. Using both approaches together allows security teams to balance accuracy, coverage, and efficiency. It also ensures that organizations are prepared for both known and emerging threats.

Response and Remediation Capabilities

EDR Response Features

EDR (Endpoint Detection and Response) provides fast, device-level response capabilities to detect, contain, and remediate threats directly on endpoints. Key features include:

• Fast device-level actions: Quickly isolate a compromised host from the network, terminate malicious processes, or quarantine/delete infected files. These actions prevent threats from spreading across the enterprise and reduce dwell time. They also allow SOC analysts to act immediately on critical alerts, limiting potential damage. By acting directly on endpoints, EDR ensures attacks are contained at the source and reduces the risk of lateral movement. It also enables rapid verification of compromised hosts before broader remediation is needed.
• Remediation scripts and rollback capabilities: EDR can revert malicious changes, run automated remediation scripts, and provide live response shells for forensic commands. This allows SOC analysts and incident responders to neutralize threats without waiting for manual intervention. Rollback and scripting also reduce recovery time and help maintain system integrity. These features also allow teams to automate repetitive tasks, improving response efficiency and consistency. They help maintain endpoint stability while ensuring threats are fully removed.
• Detailed audit trails: Tracks every action taken on endpoints, ensuring accountability and providing rich forensic data for post-incident investigation. Audit trails also help security teams improve response workflows and validate compliance requirements. These trails support regulatory reporting and help refine detection and response strategies over time. Audit data also helps SOC teams analyze attack patterns and enhance endpoint threat intelligence for future prevention. It ensures all actions are fully traceable for both security and compliance purposes.

Overall, EDR response features are designed for fast containment, precise remediation, and detailed forensic visibility, giving security teams direct control over endpoint threats. They ensure endpoint security operations are proactive, reliable, and fully auditable. EDR provides actionable insights that improve incident response and strengthen overall endpoint protection.

SIEM (and SOAR) Response Features

SIEM (Security Information and Event Management), often combined with SOAR (Security Orchestration, Automation, and Response), focuses on centralized and coordinated enterprise response. Key capabilities include:

• Centralized orchestration: Automated playbooks can block IPs, disable compromised accounts, enrich alerts, and coordinate actions across multiple security tools. This reduces manual effort and ensures consistent response across the enterprise. It also improves response speed and standardizes remediation procedures for all systems. Centralized orchestration enables security teams to respond to complex, multi-source threats efficiently. It ensures consistent policy enforcement across endpoints, networks, and cloud services.
• Case management and ticketing integration: Provides structured workflows, incident documentation, and audit trails for all response actions. It enables SOC teams to track progress, assign tasks, and maintain accountability during complex investigations. This ensures that no critical step is missed during multi-team incident handling. It also supports post-incident reporting and performance metrics, helping teams refine response processes. Integration with ticketing systems improves collaboration across IT, security, and compliance teams.
• Alerting and aggregation: While SIEM primarily alerts and correlates events, SOAR extends this by adding automated remediation and cross-system enforcement. Together, they ensure rapid and coordinated action for multi-source threats. This combination helps reduce alert fatigue and ensures timely resolution of high-priority incidents. Aggregated alerts also allow security teams to identify attack patterns and emerging threats faster. This improves situational awareness across the entire enterprise.

SIEM and SOAR help organizations manage enterprise-wide incidents, correlate alerts across identity, network, cloud, and endpoint systems, and maintain compliance and operational efficiency. They provide a complete view of threats across the environment and support data-driven decision-making for security teams. They also allow proactive threat response and better preparation for regulatory audits.

Division of Automation Responsibility

To optimize threat response, responsibilities should be clearly divided:

• EDR: Handles fast, device-level containment and captures detailed forensic data from endpoints. It focuses on immediate actions like isolating hosts and terminating malicious processes. EDR ensures endpoint incidents are contained quickly, minimizing risk to other systems. It also allows SOC teams to validate threats in real-time and prioritize remediation for the most critical endpoints. EDR ensures endpoint telemetry is captured for future investigations and threat hunting.
• SIEM/SOAR: Coordinates cross-system responses, applies suppression rules, and manages post-incident workflows involving identity, network, cloud, and endpoint systems. It ensures that enterprise-wide policies are enforced and incidents are fully documented for compliance and trend analysis. It also helps identify patterns in attacks across multiple systems for improved prevention. SIEM/SOAR provides enterprise-level visibility, enabling teams to detect multi-step attacks and respond consistently. It also supports long-term threat analytics and continuous improvement of security processes.

By combining EDR and SIEM/SOAR, organizations achieve a balanced approach: rapid endpoint containment, centralized orchestration, automated workflows, and comprehensive incident documentation. This integration strengthens security operations, reduces dwell time, and improves overall enterprise risk management.

Using both tools together ensures faster detection, coordinated response, and more efficient security operations across the organization. It also provides SOC teams with actionable insights and measurable outcomes for continuous security improvement. Together, they ensure comprehensive threat coverage, improved incident response metrics, and stronger protection against both known and unknown threats.