Shutting Down the Party - Threat Detection and Response (TDR)

Read time: 10 mins

Last Updated on April 16, 2025

Published Oct. 10, 2023

Today’s TDR tools aren’t your grandma’s antivirus software. They’ve leveled up big time — packing AI, machine learning, and automation so they can spot trouble, analyze it, and kick it out faster than you can say “phishing email.”

The big shift? Instead of just guarding one door or one system, modern TDR is like installing security cameras, motion sensors, and robot bouncers across the entire digital neighborhood. It’s not just watching the front gate anymore — it’s keeping an eye on every window, back alley, and Wi‑Fi router, ready to shut down shady activity before it ruins the party.

1. Expanded Scope and Integration (XDR and SIEM)

Traditional security often relied on tools like Intrusion Detection Systems (IDS) and older Security Information and Event Management (SIEM) systems:

Intrusion Detection Systems (IDS) are typically listen-only, passive devices that monitor traffic and report results to an administrator, but they generally cannot automatically take action to prevent a detected exploit.
Traditional SIEM solutions collected data to generate security alerts but lacked the ability to respond directly to threats.
Endpoint Detection and Response (EDR) solutions were limited, focusing only on endpoints such as computers, servers, and mobile devices.

Current TDR solutions overcome these limitations through integration:

Extended Detection and Response (XDR): XDR products represent an evolution from EDR, simplifying the entire prevention, detection, and response lifecycle. XDR monitors a much broader scope, including endpoints, cloud apps, email, and identities. XDR solutions leverage advanced correlation and analytics to provide comprehensive TDR across various security domains. Many organizations are migrating from endpoint-only EDR to broader XDR solutions.
Modern SIEM: Modern SIEM solutions aggregate and correlate data across endpoints, clouds, emails, apps, and identities to gain visibility into the entire digital environment. Modern SIEMs also incorporate AI and external threat intelligence feeds to more effectively uncover new and emerging cyberthreats.
Network Detection and Response (NDR): NDR evolved from legacy IDS/IPS systems to provide comprehensive visibility into network traffic. They move beyond standard IDS features (like signature-based detection and deep packet inspection) by leveraging advanced analytics, machine learning, and behavioral analysis.

2. Advanced Detection Methods

Current solutions utilize complex analytics to find threats that evade older, signature-based tools:

Anomaly-Based Detection: Unlike signature-based detection (which looks for known malware patterns), anomaly-based detection uses AI and analytics to understand the typical behaviors of users, devices, and software, flagging anything unusual that may indicate a cyberthreat.
Behavioral Analysis: Solutions employ Behavior-based detection (looking for actions common in cyberattacks). This includes User Behavior Analytics (UBA), which establishes a baseline for "normal" activity and alerts when behavior deviates from that baseline.
Identity-Based Focus: Because most breaches involve compromised identities, modern TDR includes Identity Threat Detection and Response (ITDR), often using User and Entity Behavior Analytics (UEBA) to uncover anomalies related to user credentials and access.
Threat Intelligence: Modern advanced threat detection and response focuses on highly evasive cyber threats. TDR relies heavily on threat intelligence—analyzing data from various sources (endpoints, email, cloud apps, and external feeds) to help security teams prepare for, detect, and investigate active threats.

3. Automation and Proactive Response

Modern TDR emphasizes speed and efficiency in response, moving beyond simple alerts provided by passive tools:

Automated Prevention and Disruption: The boundary between detection and prevention is now blurred, as modern systems perform both capabilities. Solutions like Microsoft Defender XDR use AI to automatically disrupt in-progress cyberattacks.
Security Orchestration, Automation, and Response (SOAR): SOAR solutions simplify TDR by consolidating tools and data into a centralized place and automating cyberthreat responses based on predefined rules.
Proactive Threat Hunting: Current practices include cyberthreat hunting, where security analysts proactively search for sophisticated, hard-to-detect attackers and indications of compromise, rather than simply waiting for system-generated alerts.
Vulnerability Management: TDR includes continuous and often automated vulnerability management to proactively monitor systems and applications for security weaknesses, assess their severity, and prioritize remediation.
Deception Technology: Advanced techniques include setting attacker traps, such as honeypots, which trigger alerts when an attacker attempts to access seemingly appealing, high-privilege network services or credentials.

The evolution of TDR solutions can be likened to moving from relying solely on a neighborhood watch (a passive IDS that reports suspicious activity) to implementing a comprehensive, centralized smart home security system (XDR/SIEM). This modern system not only monitors every door, window, and motion sensor (endpoints, networks, identities) using predictive analytics (AI/behavioral detection) but can also automatically lock down compromised areas and call for backup instantly (automated response and SOAR), reducing the critical time an intruder can remain undetected.

The core distinctions and overlaps among major detection and response systems—such as Extended Detection and Response (XDR), Endpoint Detection and Response (EDR), Network Detection and Response (NDR), and Security Information and Event Management (SIEM)—primarily revolve around their scope of visibility, their integration capabilities, and their primary focus (detection versus correlation and automation).

Core Distinctions Among Major Systems

The major detection and response systems have distinct scopes of focus:

System	Primary Focus (Scope)	Key Evolution/Function	Traditional Precursor
EDR (Endpoint Detection and Response)	Endpoints only (computers, servers, mobile devices, IoT).	Monitors and analyzes endpoint activities (file/process behaviors, registry changes).	Older security tools/Antivirus.
NDR (Network Detection and Response)	Network traffic and network events.	Provides comprehensive visibility into network traffic; leverages advanced analytics, machine learning, and behavioral analysis to detect anomalies.	Intrusion Detection Systems (IDS) and Intrusion Prevention Systems (IPS).
XDR (Extended Detection and Response)	Broadest scope: Endpoints, cloud apps, email, and identities.	Simplifies the entire prevention, detection, and response lifecycle by integrating multiple security components and data sources.	EDR, SIEM, and SOAR systems.
SIEM (Security Information and Event Management)	Data Aggregation: Logs and alerts across endpoints, clouds, emails, apps, and identities.	Gathers and correlates data to gain visibility and surface potential cyberthreats using detection rules and playbooks.	Traditional log management.

1. Scope: EDR, NDR, and XDR

XDR is considered an evolution that integrates and expands upon EDR and NDR:

EDR is an earlier version of XDR focused exclusively on monitoring individual endpoints for potential cyberattacks.
NDR is focused on network traffic to enable real-time detection and response. It evolved from legacy intrusion detection/prevention systems and uses advanced analytics for detection.
XDR expands upon EDR's capabilities by monitoring a broader range of sources, including endpoints, cloud apps, email, and identities. Organizations are generally migrating from endpoint-only EDR to broader XDR solutions.

2. Function: SIEM vs. XDR

While both provide wide visibility and use correlation, their primary functions differ:

SIEM solutions primarily aggregate and correlate data (logs and alerts) across the entire digital environment (endpoints, clouds, emails, apps, and identities) to surface potential cyberthreats. Traditional SIEM focused on data collection and generating alerts, lacking the ability to respond directly. Modern SIEMs incorporate AI and external threat intelligence feeds.
XDR simplifies the entire prevention, detection, and response cyberthreat lifecycle by integrating multiple security components. XDR utilizes advanced correlation and analytics and, if a threat is detected, it alerts teams and can respond automatically to defined incidents. XDR is explicitly noted as expanding on the capabilities of SIEM and SOAR (Security Orchestration, Automation, and Response) systems.

3. Legacy Distinction: IDS vs. IPS

Intrusion Detection Systems (IDS) and Intrusion Prevention Systems (IPS) represent the foundation from which many modern TDR systems evolved, with a fundamental distinction in their capabilities:

An IDS is a passive, listen-only device that monitors traffic and reports results to an administrator. It is placed out of band (outside the direct line of communication) and cannot automatically take action to prevent an exploit.
An IPS is an active system placed inline (part of the direct line of communication) that can monitor and automatically defend.
With modern systems like NDR, EDR, and XDR, the line between detection and prevention is blurred, as they perform both capabilities in addition to providing greater visibility and advanced automation.

Overlaps and Integration Points

Major TDR systems are often deployed together to create a robust security posture.

1. Overlap in Detection Methods

Several systems rely on similar detection techniques, moving beyond simple signature matching:

Behavior-based detection is used by security solutions to look for actions and behaviors common in cyberattacks.
Anomaly-based detection uses AI and analytics to identify unusual activity that may indicate a cyberthreat by understanding the typical behavior of users, devices, and software.
All major modern systems (SIEM, XDR) use threat intelligence to gain a comprehensive view of the cyberthreat landscape, analyzing data from various sources (endpoints, email, cloud apps) to detect and investigate threats.

2. Integration for Comprehensive Coverage

A multi-layer defense often requires tools to overlap so that if one detection method is compromised, a second one can detect the issue:

XDR and SIEM are both utilized to give security operations teams greater visibility over their environment, helping them identify threats quickly and uncover potential vulnerabilities.
Organizations are encouraged to deploy NDR and EDR together to ensure comprehensive coverage with both network and endpoint visibility, minimizing gaps.
SOAR functionality overlaps with XDR, as XDR is noted as expanding on SOAR capabilities. SOAR helps simplify TDR by centralizing tools and data and automating responses based on predefined rules.

3. Identity and Behavior Focus

Modern detection frequently focuses on identity and behavior, often crossing system boundaries:

Identity Threat Detection and Response (ITDR), a specialized function, uses User and Entity Behavior Analytics (UEBA) to define baseline user behavior and uncover anomalies related to compromised identities, which are involved in most breaches. This analytics approach is common across SIEM, XDR, and ITDR.
Modern detection systems emphasize cyberthreat hunting, where analysts proactively search for sophisticated, hard-to-detect attacks that may evade system-generated alerts, using tools like XDR and SIEM.

The integration of these systems is vital; for instance, a robust threat detection program should employ security event threat detection, network threat detection, and endpoint threat detection technology to act as a net across the organization's entire attack surface.

The stages of threat response are typically detailed within a comprehensive cybersecurity process known as Threat Detection and Response (TDR) or structured through formal incident response frameworks like the one developed by the National Institute of Standards and Technology (NIST).
Let's briefly discuss them both.

Stages of Threat Detection and Response (TDR)

Threat Detection and Response (TDR) is a cybersecurity process for identifying cyberthreats and taking steps to mitigate them quickly. The typical TDR process includes seven stages, moving from initial discovery through mitigation and future preparation:

Detection: This initial phase involves using security tools to monitor clouds, networks, endpoints, identities, and applications to surface risks and potential breaches. Security professionals also engage in cyberthreat hunting techniques to proactively uncover sophisticated threats that may evade automated detection.
Investigation: Once a risk is identified, the Security Operations Center (SOC) uses AI and other tools to verify that the cyberthreat is real. They must determine how the threat occurred and assess which company assets have been affected.
Containment: To prevent the cyberattack from spreading further, cybersecurity teams or automated tools isolate the infected devices, networks, and identities from the rest of the organization’s assets. Defining roles and duties for containment is a critical part of the incident response plan.
Eradication: Teams focus on eliminating the root cause of the security incident to completely evict the malicious actor from the environment. They also mitigate any vulnerabilities that might put the organization at risk of a similar cyberattack in the future.
Recovery: After the teams are reasonably confident that the cyberthreat or vulnerability has been removed, they bring the isolated systems back online.
Report: Depending on the incident’s severity, security teams must document what happened and how it was resolved, briefing leaders, executives, or the board. An incident response plan provides guidance for communicating with cross-functional stakeholders like lawyers, PR, and senior leaders.
Risk mitigation: To prevent a recurrence and improve future response capabilities, teams study the incident to identify necessary changes to processes and the digital environment. This evaluation ensures continuous improvement.

The NIST Incident Response Cycle

Many organizations follow the incident response cycle established by the National Institute of Standards and Technology (NIST). This model separates the incident response process into four main stages:

Preparation: This stage involves setting up incident response policies and functions, implementing preventative measures (such as securing the network perimeter and user training), and deploying tools like threat detection and response systems.
Detection and Analysis: This stage encompasses identifying threat types, classifying signs as indicators or precursors, performing incident analysis, documenting the event, prioritizing incidents based on impact, and reporting to relevant authorities. Threat detection and response tools primarily support this stage.
Containment, Eradication, and Recovery: This is considered the most active incident response phase. It includes isolating the threat, developing containment strategies specific to the attack type, gathering legal evidence, removing compromised accounts and malware (eradication), and executing a phased recovery. TDR tools also support this stage.
Post-Incident Activity: NIST considers this the most crucial, yet often overlooked, phase. Activities include holding a "Lessons Learned" meeting to process the incident, preserving evidence and data, updating preparation for future threats, creating follow-up reports, and evaluating team performance.

TDR solutions leverage advanced detection methods and automation to rapidly execute the necessary steps in the detection, analysis, containment, and eradication phases of these models.

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