A Capability-Based Framework for Evaluating Next-Generation Security Operations Center Technologies

Apr 16

This article discusses the evolving landscape of Security Operations Center (SOC) technologies and the challenges of evaluating them due to the convergence of traditional categories like SIEM, SOAR, EDR, and NDR into platforms like XDR and XSIAM. It proposes a platform-agnostic SOC Capability Catalog based on the NIST Cybersecurity Framework (CSF) 2.0, focusing on granular security functions rather than vendor labels. The catalog aims to help organizations define requirements, compare technologies objectively, and identify gaps. It also explores the role of AI/ML in enhancing SOC capabilities and transforming operations, while acknowledging its limitations. The document emphasizes utilizing the catalog for technology evaluation, service definition, and driving SOC maturity and strategy.

I. Executive Summary

Chief Information Security Officers (CISOs) and security leaders face a significant challenge in navigating the rapidly evolving landscape of Security Operations Center (SOC) technologies. The traditional categories used to classify these tools – such as Security Information and Event Management (SIEM), Security Orchestration, Automation, and Response (SOAR), Endpoint Detection and Response (EDR), and Network Detection and Response (NDR) – are becoming increasingly blurred. This convergence is driven by vendors consolidating features into broader platforms, exemplified by the rise of Extended Detection and Response (XDR) and comprehensive, AI-driven platforms like Extended Security Intelligence and Automation Management (XSIAM). This technological shift, while promising enhanced capabilities, complicates the evaluation process, making it difficult for organizations to compare vendor offerings accurately and make strategic investment decisions that align with their specific operational needs and risk posture.

To address this complexity, this report introduces a platform-agnostic SOC Capability Catalog. This catalog moves beyond ambiguous marketing labels and focuses instead on the fundamental security functions that SOC technologies provide. It deconstructs the objectives and workflows of a modern SOC into granular, discrete capabilities – such as log collection, behavioral anomaly detection, threat intelligence correlation, automated response orchestration, and incident case management [User Query]. Each capability is clearly defined based on its purpose within the SOC ecosystem, irrespective of the specific product category that delivers it. This approach provides a standardized lexicon for describing and assessing SOC-enabling technologies.

The primary value of this capability-based framework lies in empowering security professionals to conduct more objective and effective evaluations of SOC technologies. By using the catalog, organizations can precisely define their requirements, compare diverse platforms based on the specific functions they offer, and identify potential gaps or redundancies in their existing security stack. This facilitates clearer communication with vendors, ensures technology investments directly support operational objectives, and ultimately enables the selection of solutions best suited to enhance the organization's security posture and risk management strategy. The catalog utilizes the widely recognized National Institute of Standards and Technology (NIST) Cybersecurity Framework (CSF) 2.0 as its organizing structure, further enhancing its practicality and alignment with industry best practices.

II. The Evolving Security Operations Center (SOC)

The modern Security Operations Center (SOC) serves as the central nervous system for an organization's cybersecurity defenses. Its role has evolved beyond simple monitoring to encompass a complex interplay of people, processes, and technology aimed at proactively managing cyber risk. Understanding the core objectives, functions, workflows, and guiding frameworks of a contemporary SOC is essential before deconstructing the capabilities provided by its enabling technologies.

A. Core Objectives and Functions of a Modern SOC

The fundamental objective of a modern SOC is to enhance the detection, investigation, and remediation of security incidents through continuous monitoring and analysis of the organization's IT environment. By consolidating security expertise and relevant data, the SOC aims to proactively identify vulnerabilities, respond rapidly to attacks to minimize harm, and maintain operational resilience. It strives to provide comprehensive visibility across networks, endpoints, applications, and cloud environments, ensuring that security teams can protect assets effectively. The establishment of a SOC helps organizations significantly improve their ability to detect and react to threats in a timely manner, reducing the dwell time of attackers and mitigating the potential impact of breaches.

To achieve these objectives, a modern SOC performs several key functions, often guided by established frameworks and best practices:

Security Monitoring: This involves the continuous observation of security logs and activities from diverse sources, including firewalls, intrusion detection/prevention systems (IDPS), servers, endpoints, applications, and cloud services. Security Information and Event Management (SIEM) systems are commonly used to aggregate, correlate, and analyze this data in real-time to identify suspicious activity.
Incident Response (IR): This encompasses the entire lifecycle of handling security incidents, starting from initial detection and analysis, moving through containment, eradication, and recovery, and concluding with post-incident analysis and lessons learned. A well-defined and regularly rehearsed incident response plan is critical for minimizing damage and restoring normal operations swiftly.
Threat Intelligence: This function involves the systematic collection, processing, and analysis of information about current and emerging threats, including adversary tactics, techniques, and procedures (TTPs), vulnerabilities, and malicious indicators. Actionable threat intelligence enables proactive defense, improves detection capabilities, and helps organizations anticipate attacks.
Vulnerability Management: This focuses on the methodical identification, assessment, prioritization, and remediation of security weaknesses in an organization's systems and applications. Activities include regular vulnerability scanning, penetration testing, and security audits to address flaws before they can be exploited.
Log Management: This foundational capability involves the collection, secure storage, retention, and analysis of log data from across the IT environment. Logs are essential for real-time monitoring, incident investigation, forensic analysis, and compliance reporting.
Compliance Management: The SOC often plays a role in ensuring the organization adheres to relevant industry regulations (e.g., HIPAA, PCI DSS) and internal security policies. This involves collecting evidence, generating reports, and facilitating audits.
Preventative Maintenance: This includes proactive measures to strengthen security posture and reduce the attack surface, such as regular system patching, updating firewall policies, implementing application whitelisting/blacklisting, and hardening configurations.
Root Cause Investigation: Following an incident, the SOC investigates to determine the underlying cause, understand the full scope of the compromise, and identify improvements to prevent recurrence.

Achieving these functions effectively requires a synergistic combination of skilled personnel (security analysts tiered by expertise ), well-defined processes (standardized workflows, incident response playbooks ), and appropriate technology. However, acquiring and retaining personnel with the necessary skills remains a significant barrier to SOC excellence for many organizations.

B. Key Operational Workflows

The daily operations within a SOC typically follow a series of interconnected workflows designed to efficiently process security data and respond to potential threats. These workflows demonstrate how the various SOC functions interact:

Data Collection & Processing: The process begins with ingesting vast amounts of data from disparate sources across the IT landscape – logs from servers and applications, network traffic flows, endpoint events, cloud service telemetry, identity system logs, and external threat intelligence feeds. This raw data must then be normalized into a common format, parsed to extract relevant fields, and potentially enriched with contextual information (e.g., user identity, asset criticality, threat reputation) to prepare it for analysis.
Detection & Alerting: Processed data is analyzed using various techniques to identify potential security incidents. This includes applying correlation rules to link related events, matching data against known threat signatures or indicators of compromise (IOCs), employing statistical analysis to detect anomalies deviating from established baselines, and utilizing behavioral analytics (User and Entity Behavior Analytics - UEBA, Network Behavior Analytics - NBA) to spot suspicious patterns of activity. When a potential threat is identified, an alert is generated.
Triage & Prioritization: SOC analysts, typically Tier 1 personnel, review the generated alerts. They investigate each alert to determine its validity, discarding false positives and assessing the potential impact and severity of genuine threats. Alerts are then prioritized based on criticality, ensuring that the most significant threats receive immediate attention.
Investigation & Analysis: Higher-priority incidents are escalated, often to Tier 2 analysts, for in-depth investigation. This involves gathering additional evidence, correlating information across different data sources, leveraging threat intelligence to understand the adversary's TTPs, determining the scope of the compromise (affected systems, users, data), and identifying the root cause of the incident.
Response & Remediation: Based on the investigation findings, the SOC team takes action to contain the threat and mitigate its impact. This may involve isolating compromised endpoints, blocking malicious IP addresses or domains at the firewall, disabling affected user accounts, removing malware, and restoring systems from backups. Response actions can be manual or increasingly automated through orchestration platforms.
Threat Hunting: Beyond reacting to alerts, mature SOCs engage in proactive threat hunting. Analysts hypothesize potential threats or intrusion scenarios based on intelligence or environmental knowledge and actively search through datasets (logs, network traffic, endpoint data) for evidence of malicious activity that may have evaded automated detection systems.
Reporting & Improvement: Throughout the incident lifecycle, activities are documented in case management systems. After an incident is resolved, reports are generated for stakeholders, management, and potentially, regulatory compliance. Post-incident reviews are conducted to identify lessons learned, which are then used to refine detection rules, update response playbooks, improve security controls, and enhance overall SOC processes and capabilities.

C. Common Frameworks Guiding SOC Operations

To provide structure, consistency, and strategic direction, SOCs often align their operations with established cybersecurity frameworks. These frameworks offer best practices, common taxonomies, and methodologies for managing risk and improving security posture. Key examples include:

NIST Cybersecurity Framework (CSF): Developed by the U.S. National Institute of Standards and Technology, the CSF provides a voluntary framework consisting of standards, guidelines, and best practices to manage cybersecurity risk. Its core functions – Identify, Protect, Detect, Respond, Recover – provide a high-level lifecycle for managing cybersecurity risk. The recent CSF 2.0 release adds a crucial Govern function, emphasizing integrating cybersecurity risk management into the broader enterprise risk management (ERM) strategy and establishing oversight. Organizations use the CSF to assess their current cybersecurity posture, define a target state, identify gaps, and prioritize actions for improvement, creating a roadmap to minimize risk. The CSF's outcome-oriented approach suits organizations of all sizes and sectors.
MITRE ATT&CK Framework: This framework provides a globally accessible knowledge base of adversary tactics and techniques based on real-world observations. It categorizes attacker actions into tactics (the "why" - e.g., Initial Access, Execution, Persistence) and techniques (the "how" - e.g., Phishing, PowerShell execution, Scheduled Task). SOCs use ATT&CK to inform threat intelligence gathering, develop more realistic detection rules and analytics, plan adversary emulation exercises (red teaming), and guide incident response investigations.
Cyber Kill Chain / Unified Kill Chain: Originally developed by Lockheed Martin, the Cyber Kill Chain models the typical stages of a cyberattack (e.g., Reconnaissance, Weaponization, Delivery, Exploitation, Installation, Command & Control, Actions on Objectives). The goal is to identify opportunities to disrupt the attack at each stage. The Unified Kill Chain framework seeks to enhance this model by merging it with the MITRE ATT&CK framework, breaking attacks into phases like initial foothold, network propagation, and action on objectives, providing a more granular, time-oriented view for analysis, countermeasure mapping, and response modeling.

The inherent structure of established frameworks like the NIST CSF provides a logical and widely understood foundation for organizing the granular capabilities required by a modern SOC. Utilizing such a framework moves the focus from ambiguous technology labels to the specific security outcomes an organization needs to achieve. This aligns directly with the goal of creating a platform-agnostic evaluation method, as the NIST CSF functions (Govern, Identify, Protect, Detect, Respond, Recover) map naturally to the core activities and responsibilities of a SOC, irrespective of the underlying technology used. This structure provides an intuitive and practical way for security professionals to organize their requirements and assess technology capabilities against established best practices.

III. Navigating the SOC Technology Landscape

The technology stack underpinning the SOC has undergone significant transformation. What began as distinct tool categories addressing specific needs has evolved into a complex ecosystem characterized by overlapping functionalities and platform convergence. Understanding both the traditional roles of these technologies and the drivers and implications of their convergence is crucial for making informed decisions.

A. Defining Traditional Categories and Core Capabilities

Before the current wave of convergence, SOC technologies were generally categorized based on their primary function. While these lines are now blurred, understanding their original intent provides context:

Security Information Management (SIM): Focused primarily on the long-term collection, storage, and analysis of log data, mainly to support compliance requirements and post-incident forensic investigations. SIM capabilities included secure log aggregation, retention according to policy (e.g., HIPAA, PCI-DSS), and reporting for audits. Its orientation was largely reactive.
Security Event Management (SEM): Concentrated on real-time monitoring and analysis of security events. SEM tools correlated events as they occurred, generated notifications for potentially critical issues, and provided console views for immediate situational awareness.
Security Information and Event Management (SIEM): Emerged as the combination of SIM and SEM capabilities, aiming to provide a unified platform for real-time monitoring and long-term log management. Core SIEM functions include broad data aggregation from diverse network and security devices, servers, and applications; data normalization; real-time event correlation; alerting based on predefined rules; dashboards for visualization; reporting for compliance and analysis; and sometimes, capabilities for forensic investigation. SIEM became the cornerstone technology for many SOCs, providing centralized visibility and a repository for security data. However, traditional SIEMs often faced challenges with alert volume (noise), false positives, the complexity of rule tuning, and the need for significant manual effort in investigation and response.
Security Orchestration, Automation, and Response (SOAR): Developed specifically to address the inefficiencies in incident response processes. SOAR platforms integrate with other security tools (including SIEM, EDR, firewalls, threat intelligence platforms) to automate and orchestrate workflows using predefined "playbooks". Key capabilities include case management, automated alert enrichment (adding context from threat intelligence or internal systems), automated execution of response actions (e.g., blocking an IP, isolating a host, disabling a user), and streamlining communication and reporting. The goal is to reduce Mean Time To Respond (MTTR), free up analyst time from repetitive tasks, and ensure consistent response procedures. SOAR was often implemented as an add-on layer to SIEM systems.
Network Detection and Response (NDR): Focused on monitoring and analyzing network traffic (both north-south/perimeter and east-west/internal) to identify threats that might bypass traditional perimeter defenses or originate from within the network. NDR tools typically use techniques like deep packet inspection, flow analysis, behavioral modeling, and machine learning to detect anomalies, lateral movement, command-and-control communication, and data exfiltration. They provide crucial visibility into network activities that endpoint or log-based tools might miss.
Endpoint Detection and Response (EDR): Concentrated security efforts on endpoints (laptops, desktops, servers). EDR solutions continuously monitor endpoint activities (processes, file changes, network connections, registry modifications), record telemetry for investigation, detect malicious behavior using signatures, behavioral analysis, and machine learning, and provide capabilities to respond directly on the endpoint, such as isolating the device, terminating malicious processes, or deleting files. EDR is widely considered the technological precursor to XDR.

B. The Convergence Challenge: Understanding XDR and Platform Consolidation

The distinctions between these traditional categories have become increasingly indistinct due to significant capability convergence, largely driven by the emergence of Extended Detection and Response (XDR).

XDR represents an evolution, frequently originating from EDR platforms, aiming to provide a more holistic and integrated approach to threat detection and response. Its core premise is to unify security telemetry and response actions beyond the endpoint, integrating data streams from network sensors (NDR functionality), cloud environments, email security gateways, identity and access management (IAM) systems, and other security controls into a single, cohesive security operations system.

The primary objective of XDR is to overcome the limitations of siloed security tools by correlating data and alerts across multiple security layers. This cross-domain correlation aims to improve the accuracy of threat detection (reducing false positives and negatives), provide analysts with a more complete picture of an attack chain, accelerate investigations by consolidating relevant data, and enable more effective and coordinated response actions. Many XDR solutions are delivered as cloud-native, Software-as-a-Service (SaaS) platforms, offering scalability and potentially easier deployment.

This convergence has led industry analysts, particularly Forrester, to describe XDR as being on a "collision course" with SIEM and SOAR. XDR platforms increasingly incorporate capabilities traditionally associated with SIEM (data aggregation, analysis, threat detection) and SOAR (automated response playbooks, orchestration). The goal is often to provide a more streamlined experience with curated detections and integrated response workflows, potentially reducing the need for separate SIEM and SOAR tools, especially for threat detection and response use cases.

It is important to note that definitions of XDR can vary. Gartner initially emphasized a SaaS-based, single-vendor approach where multiple security products from that vendor are natively integrated. Forrester, conversely, defined XDR as an evolution of EDR, unifying endpoint data with other telemetry sources, potentially through third-party integrations (Hybrid/Open XDR) or within a single vendor's portfolio (Native XDR). This definitional variance adds another layer of complexity for buyers.

The drive towards convergence and the adoption of XDR-like approaches are not merely technological trends; they are responses to pressing operational challenges faced by modern SOCs. Security teams grapple with "tool sprawl," investing in numerous technologies but struggling to implement and manage them effectively, often spending more time on tool maintenance than on threat defense. This complexity, combined with the persistent shortage of skilled cybersecurity professionals , creates a demand for solutions that simplify operations, reduce the need for analysts to switch between multiple consoles ("tab-hopping") , automate repetitive tasks, and improve the signal-to-noise ratio through better data correlation and analytics across previously isolated security domains. Converged platforms promise to address these issues by providing a more unified, efficient, and effective security operations experience.

Despite the convergence and competition, XDR and SIEM are not entirely interchangeable, and understanding their nuances remains critical for strategic planning. SIEM platforms often maintain strengths in their ability to ingest and retain massive volumes of log data from virtually any source across the enterprise, making them indispensable for long-term storage, compliance reporting, and broad forensic investigations. XDR solutions, while expanding their data sources, are typically more focused on specific telemetry types deemed most relevant for threat detection and response (often rooted in endpoint, network, cloud, and identity data). Their data retention periods may be shorter than those required for compliance-driven SIEM deployments. Furthermore, XDR often emphasizes curated, high-fidelity detections derived from integrated data sources, aiming for lower alert noise compared to the potentially broader, but sometimes less refined, analytics applied across the vast datasets within a traditional SIEM. Organizations must carefully consider their specific needs for compliance, data retention, threat detection focus, and operational workflows when evaluating the roles of SIEM and XDR in their environment.

C. The Rise of AI-Driven Platforms (e.g., XSIAM)

Representing a further evolution in platform convergence are solutions explicitly marketed as AI-driven SOC platforms, such as Palo Alto Networks' Cortex XSIAM (Extended Security Intelligence and Automation Management). These platforms aim to fundamentally transform SOC operations by unifying an even broader set of capabilities onto a single, AI-centric architecture.

XSIAM, for example, is positioned as integrating the core functions of EDR, XDR, SOAR, Attack Surface Management (ASM), User and Entity Behavior Analytics (UEBA), Threat Intelligence Platform (TIP), and SIEM into one platform. The architectural concept revolves around creating an intelligent data foundation by centralizing security data from diverse sources (endpoint, network, cloud, identity, third-party feeds). This data is automatically prepared, enriched, and "intelligently stitched" together to provide context.

The defining characteristic of these platforms is their "AI-driven" or "automation-first" approach. Machine learning (ML) models are applied extensively for advanced threat detection, correlating low-confidence events into high-confidence incidents, automatically scoring incidents based on risk (e.g., XSIAM's SmartScore ), triaging alerts, and driving automated response actions through integrated orchestration and playbooks. The goal is to significantly reduce the manual workload on analysts, allowing them to focus on the most complex and critical threats, thereby accelerating response times and improving overall security outcomes.

These platforms often extend beyond traditional detection and response, incorporating capabilities like integrated ASM to provide visibility into internal and external assets and exposures, and Identity Threat Detection and Response (ITDR) modules to address identity-based threats.

The emergence and marketing of platforms like XSIAM strongly reinforce the need for a capability-based evaluation framework. These vendors explicitly list the discrete capabilities (EDR functions, SOAR functions, SIEM functions, ASM, etc.) that their platform unifies, rather than positioning themselves solely within a single traditional category. This market positioning serves as a clear indicator that the industry itself is moving towards describing solutions based on the portfolio of functions they offer, rather than relying on increasingly inadequate legacy labels. Evaluating such platforms necessitates breaking them down into their constituent capabilities to understand their true scope and effectiveness, validating the approach taken by the capability catalog presented in this report.

D. Comparative Overview of SOC Platform Categories

The following table provides a high-level comparison of traditional and emerging SOC platform categories based on key functional characteristics. It aims to synthesize the distinctions discussed above, acknowledging that capabilities are converging and vendor implementations vary.

SIEM (Traditional)

Primary Data Sources: Logs (Network, Security Devices, Servers, Apps)
Core Analytics Focus: Log Correlation, Compliance Reporting, Basic Anomaly
Automation Level: Low (Primarily Alerting)
Primary Use Case: Centralized Logging, Compliance, Basic Monitoring
Typical Data Retention: Long-term (Months/Years for Compliance)

SOAR

Primary Data Sources: Alerts from SIEM, EDR, TIPs, etc.
Core Analytics Focus: Workflow Automation Logic, Case Enrichment
Automation Level: High (Orchestration, Playbook Execution)
Primary Use Case: Incident Response Efficiency, Process Standardization
Typical Data Retention: Short-term (Incident Data)

EDR

Primary Data Sources: Endpoint Telemetry (Processes, Files, Network Conns, Registry)
Core Analytics Focus: Endpoint Threat Behavior Analysis, Malware Detection
Automation Level: Medium (Endpoint Response Actions)
Primary Use Case: Endpoint Threat Protection & Response
Typical Data Retention: Medium-term (Days/Weeks for Investigation)

NDR

Primary Data Sources: Network Traffic (Packets, Flows)
Core Analytics Focus: Network Anomaly Detection, Lateral Movement, C2 Communication
Automation Level: Low-Medium (Alerting, some response integration)
Primary Use Case: Network Visibility & Threat Detection
Typical Data Retention: Short-Medium Term (Traffic Data)

XDR

Primary Data Sources: Endpoint, Network, Cloud, Identity, Email, Threat Intel (Curated Telemetry)
Core Analytics Focus: Cross-Domain Correlation, Threat Hunting, Integrated Incident Analysis
Automation Level: Medium-High (Integrated Response Playbooks, some automated actions)
Primary Use Case: Unified Threat Detection & Response across multiple domains, SOC Efficiency
Typical Data Retention: Medium-term (Potentially shorter than SIEM, focus on TDIR)

AI-Driven Platforms (e.g., XSIAM)

Primary Data Sources: All XDR sources + potentially broader logs, ASM data, Vulnerability data
Core Analytics Focus: AI/ML-driven Detection, Cross-Domain Stitching, Automated Triage, Risk Scoring
Automation Level: Very High (Automation-First, AI-driven response, Playbook optimization)
Primary Use Case: Holistic SOC Operations, AI-Augmented TDIR, Tool Consolidation
Typical Data Retention: Configurable, potentially long-term depending on licensing/architecture.

Note: This represents generalized characteristics. Specific vendor offerings may vary significantly.

IV. A Platform-Agnostic SOC Capability Catalog

The convergence of technologies and the limitations of traditional labels necessitate a shift towards a capability-based approach for evaluating SOC platforms. Instead of asking "Do I need a SIEM or an XDR?", security leaders should ask "Which capabilities do I need to effectively detect, respond to, and manage threats within my specific environment, and which platform or combination of platforms best delivers those capabilities?" This section introduces such a catalog, organized using a standard framework.

A. Introduction to the Capability-Based Approach

The core principle of this approach is to deconstruct the complex functions of a SOC into discrete, measurable capabilities. A capability, in this context, represents a specific security function or task that a technology platform can perform (e.g., "ingest threat intelligence feeds," "correlate events across endpoint and network data," "automatically isolate a compromised host"). This catalog lists these granular capabilities, defined in a platform-agnostic manner, focusing on the what (the function performed) rather than the how (the specific vendor implementation) or the where (the product category label) [User Query].

By using this catalog, organizations can:

Define Requirements: Clearly articulate the specific functional needs of their SOC based on risk profile, operational workflows, and maturity level.
Evaluate Technologies Objectively: Compare different vendor offerings (whether labeled SIEM, XDR, SOAR, XSIAM, or something else) based on the specific capabilities they provide, enabling a true apples-to-apples comparison.
Identify Gaps and Redundancies: Assess their current technology stack against the required capabilities to pinpoint areas needing improvement or opportunities for consolidation.
Facilitate Communication: Provide a common language for discussing requirements and capabilities with vendors, managed service providers, and internal stakeholders.

B. Proposed Framework for Organization (NIST CSF 2.0)

To provide a logical and widely understood structure, this catalog organizes capabilities according to the core functions defined in the NIST Cybersecurity Framework (CSF) version 2.0: Govern, Identify, Protect, Detect, Respond, and Recover.

The NIST CSF is chosen for several reasons:

Industry Acceptance: It is a globally recognized and widely adopted framework for managing cybersecurity risk.
Outcome-Oriented: It focuses on desired cybersecurity outcomes rather than specific technologies or controls, aligning well with the capability-based approach.
Comprehensive Coverage: Its functions span the full lifecycle of cybersecurity risk management, from governance and identification through protection, detection, response, and recovery, providing a holistic structure for SOC capabilities.
Relevance: CSF 2.0 explicitly includes the 'Govern' function, acknowledging the critical role of strategy, policy, and oversight in effective security operations.

Mapping capabilities to these functions helps organizations understand how different technological features contribute to achieving broader cybersecurity objectives.

C. Detailed Capability Definitions (Organized by NIST CSF 2.0 Function)

The following sections define granular SOC capabilities, categorized under the NIST CSF 2.0 functions. Each definition outlines the capability's purpose and function within a SOC context.

Govern (GV) Related Capabilities: Capabilities enabling oversight, strategy alignment, risk management, and compliance within the SOC platform.

Policy Compliance Monitoring: The ability to continuously monitor system configurations, user activities, and data handling practices against predefined internal security policies and external regulatory frameworks (e.g., HIPAA, PCI-DSS, GDPR) using data collected by the platform. This involves defining compliance rules and generating alerts or reports on deviations.
Compliance Reporting Automation: Functionality to automatically generate standardized reports required for compliance audits and internal reviews, leveraging collected log and event data, monitoring results, and incident records. This reduces manual effort in audit preparation.
Risk Scoring & Visualization: The capability to calculate and display risk scores for assets, users, identities, or specific security events based on factors like detected vulnerabilities, threat intelligence matches, observed anomalous behavior, and asset criticality. Visualization helps prioritize response and mitigation efforts.
Cybersecurity Strategy Alignment Interface: Features that allow the mapping or tagging of SOC activities, incidents, and performance metrics (e.g., MTTR, detection rates) to specific business objectives, risk tolerance levels, or elements of the organization's overall cybersecurity strategy. This supports demonstrating SOC value and aligning operations with business goals.
Supply Chain Risk Data Integration: The ability to ingest data feeds or integrate with platforms providing information about the security posture or risks associated with third-party vendors and software supply chain components, allowing correlation with internal events.
Role-Based Access Control (Platform): Mechanisms within the SOC platform itself to enforce granular permissions for different SOC roles (e.g., Tier 1 Analyst, Threat Hunter, SOC Manager), restricting access to specific data, functions, or administrative settings based on defined responsibilities.

Identify (ID) Related Capabilities: Capabilities focused on understanding the organizational environment, assets, and potential cybersecurity risks.

Asset Discovery & Inventory: The ability to automatically discover and maintain an inventory of assets across the IT environment, including hardware (servers, endpoints), software, cloud instances (IaaS, PaaS, SaaS), network devices, IoT devices, and potentially data repositories. This includes identifying both internal assets and externally facing (internet-exposed) assets.
Data Source Integration & Management: The capability to connect to, configure, and manage data ingestion from a wide variety of sources relevant to security monitoring. This includes endpoints (via agents), network devices (via logs or sensors), cloud platforms (via APIs), applications, identity providers, and threat intelligence feeds.
Vulnerability Data Integration: The ability to ingest data from vulnerability scanning tools or vulnerability management platforms, correlating identified vulnerabilities with specific assets in the inventory and potentially with observed security events.
User Identity & Context Integration: The capability to integrate with enterprise identity sources (e.g., Active Directory, Azure AD, Okta) to associate security events with specific users, roles, and departments, enriching the data for analysis and behavior monitoring.
Threat Actor Profiling Support: Features within the platform (or via integration) to store, organize, access, and utilize information about known threat actors, including their motivations, common TTPs (mapped to frameworks like ATT&CK), and associated IOCs.
Security Baseline Definition: The ability to learn or allow configuration of normal patterns of activity (baselines) for users, endpoints, network segments, and applications. These baselines serve as a reference point for anomaly detection systems.

Protect (PR) Related Capabilities: Capabilities enabling the implementation or enforcement of safeguards through the SOC platform.

Access Control Policy Enforcement Support: The ability for the SOC platform to trigger enforcement actions in integrated access control systems based on detected threats or elevated risk scores. Examples include initiating a step-up authentication challenge, blocking a user login attempt, or quarantining a user session.
Endpoint Configuration Management Interface: Features that provide visibility into endpoint security configurations (e.g., OS hardening status, security agent health) and potentially allow analysts to trigger configuration changes or hardening tasks via integration with Unified Endpoint Management (UEM) or EDR tools.
Data Security Policy Application Support: The capability to identify potentially sensitive data within monitored events or data flows and integrate with Data Loss Prevention (DLP) or encryption tools to enforce data protection policies.
Threat Prevention (Signature/Behavioral): Direct capabilities, often embedded within integrated EDR or NDR components, to automatically block known threats based on signatures (e.g., malware hashes, malicious URLs) or predefined malicious behaviors (e.g., exploit techniques) before they can cause harm.
Security Awareness Training Integration: Potential mechanisms to trigger targeted security awareness notifications or micro-trainings for users based on detecting risky behaviors (e.g., clicking a simulated phishing link, visiting a risky website).

Detect (DE) Related Capabilities: Capabilities focused on analyzing data from the environment to identify potential cybersecurity attacks and compromises.

Log Collection & Aggregation: The fundamental capability to gather log data from diverse sources (operating systems, applications, network devices, security appliances, cloud services) into a centralized repository for storage and analysis.
Telemetry Collection (Non-Log): The ability to gather security-relevant data beyond traditional logs, such as network packet captures or flow data (common in NDR), detailed endpoint process execution and system call information (common in EDR), cloud configuration changes, API access patterns, and identity authentication events.
Data Normalization & Parsing: The process of transforming raw data collected from various sources and formats into a standardized, structured schema (e.g., a common event format). This involves parsing fields, standardizing timestamps, and categorizing event types to enable consistent analysis and correlation.
Data Enrichment: The process of augmenting incoming security data with additional context to enhance its value for analysis and investigation. This can include adding user information from directories, asset details from inventory, vulnerability status, geographic location based on IP address, or threat intelligence reputation scores for IPs, domains, or hashes.
Real-time Event Correlation: Analyzing streams of events from multiple sources as they arrive to identify relationships, patterns, or sequences of events that collectively indicate a potential security incident, often based on predefined rules.
Historical Data Correlation: Analyzing stored security data over extended periods (hours, days, weeks) to identify trends, low-and-slow attack patterns, or relationships between events that might not be apparent in real-time analysis.
Signature-Based Detection: Identifying known threats by matching observed data (e.g., file hashes, network traffic patterns, log entries, URLs, IP addresses) against a database of predefined signatures or indicators of compromise (IOCs).
Statistical Anomaly Detection: Applying statistical methods to identify activities or events that deviate significantly from established historical baselines of normal behavior for a specific user, system, or network segment.
Behavioral Threat Detection (UEBA/NBA): Analyzing patterns and sequences of activities for users, endpoints, or network entities to detect behaviors that are indicative of malicious intent or compromise, even if individual actions appear benign. This often involves comparing behavior against known malicious TTPs (e.g., from MITRE ATT&CK) or identifying significant deviations from peer group norms.
Threat Detection Rule Engine: A component that allows security analysts to define, test, deploy, and manage custom rules for detecting specific patterns, events, or sequences of activity indicative of threats relevant to their organization.
Threat Intelligence Ingestion & Correlation: The ability to automatically consume external threat intelligence feeds (e.g., lists of malicious IPs/domains/hashes, threat actor TTPs, vulnerability exploits) and correlate this intelligence in real-time against observed internal security data (logs, network traffic, endpoint activity) to identify potential threats.
Sandboxing (Integrated/External): The capability to automatically or manually submit suspicious files or URLs to a controlled, isolated environment (sandbox) for execution and behavioral analysis to determine if they are malicious without risking harm to the production environment. This can be a built-in feature or a dedicated sandboxing solution integration.
Machine Learning Model Application (Detection): The utilization of various machine learning algorithms (e.g., clustering, classification, regression, deep learning) trained on security data to identify complex malicious patterns, subtle anomalies, zero-day threats, or specific attack types (like ransomware or phishing) that may be difficult to detect with traditional rules or signatures.

Respond (RS) Related Capabilities: Capabilities focused on taking action once a potential incident has been detected.

Alert Generation & Presentation: The process of creating informative and actionable alerts based on the outputs of detection mechanisms. Alerts should be presented clearly in a unified console or dashboard, providing essential context for initial assessment.
Alert Triage & Prioritization Support: Features designed to help analysts efficiently manage the influx of alerts. This includes capabilities like automatic grouping of related alerts into single incidents , assigning risk scores based on severity and confidence , providing immediate contextual data display, automatically suppressing known false positives, and prioritizing alerts based on potential impact.
Incident Case Management: A system for formally tracking security incidents from creation to closure. This includes logging all investigation activities, evidence collected, actions taken, analyst notes, status updates, and resolution details, providing an auditable record.
Investigation Workflow Guidance: Features that assist analysts during investigations, such as providing graphical representations of attack chains (e.g., mapping to MITRE ATT&CK ), suggesting relevant queries or data sources to explore, visualizing relationships between entities (users, hosts, IPs), and providing step-by-step guidance based on incident type.
Cross-Domain Data Querying: The ability for analysts to execute searches and pivot seamlessly across all integrated data types (logs, endpoint telemetry, network data, cloud events, identity logs, threat intelligence) using a unified query language and interface, facilitating comprehensive investigation without switching tools.
Forensic Data Collection: Capabilities enabling analysts to remotely acquire forensic artifacts from compromised systems for deeper analysis. This might include collecting full disk images, memory dumps, specific files or logs, browser history, or running predefined forensic scripts.
Automated Response Orchestration (Playbooks): A core SOAR capability allowing the definition, automated execution, and management of sequences of actions (playbooks) triggered by specific alerts or incident types. These playbooks integrate actions across multiple security tools (e.g., query threat intel, enrich alert, isolate host, block IP, create ticket) to automate routine response tasks.
Manual Response Action Initiation: An interface that allows authorized analysts to manually trigger specific response actions through the platform's integrations with other security tools (e.g., clicking a button to isolate an endpoint via EDR, block an IP via firewall, or disable a user account via IAM).
Threat Containment Actions (Endpoint): Specific response actions executable on endpoints via an integrated EDR agent, such as isolating the endpoint from the network, killing malicious processes, deleting files, or quarantining malware.
Threat Containment Actions (Network): Specific response actions executable on network security devices (e.g., firewalls, proxies) via integration, such as blocking traffic to/from specific IP addresses, domains, or URLs, or applying specific filtering rules.
Threat Containment Actions (User/Identity): Specific response actions executable on identity management systems via integration, such as disabling a user account, forcing a password reset, requiring multi-factor authentication, or terminating active user sessions.
Communication & Collaboration Tools: Features embedded within the SOC platform to facilitate communication among team members during incident response, support shift handovers with clear status updates, and enable collaboration on investigation tasks.

Recover (RC) Related Capabilities: Capabilities focused on restoring systems and services affected by a cybersecurity incident.

Restoration Support Workflow: Guidance or automated playbooks within the platform designed to assist SOC analysts or IT operations teams in executing the necessary steps to safely restore affected systems, applications, and data to normal operation after a threat has been eradicated.
Post-Incident Reporting Generation: Tools to automatically or semi-automatically compile data from the incident case management system (timeline, actions taken, findings, impact assessment) into comprehensive post-incident reports for stakeholders, compliance, and lessons learned documentation.
System State Verification Support: Capabilities that help verify the integrity and normal functioning of systems after recovery actions have been completed, potentially through baseline comparisons or integration with configuration management tools.
Continuous Improvement Feedback Loop: Mechanisms within the platform to facilitate the process of learning from incidents. This could involve features for easily creating new detection rules based on incident findings, updating prevention policies, modifying response playbooks, or flagging areas for process improvement.

The true utility of this catalog emerges from its granularity. Simply stating a platform offers "Threat Detection" is insufficient for meaningful evaluation in today's complex landscape. Effective assessment requires understanding the specific methods employed (e.g., signature-based, anomaly-based, behavioral, ML-driven) and the supporting capabilities that enable them (e.g., data collection scope, normalization quality, cross-domain correlation, threat intelligence integration, rule engine flexibility). This level of detail allows security leaders to move beyond marketing claims and precisely match technology offerings to their specific operational requirements and desired security outcomes. For instance, a CISO can assess whether a platform supports behavioral detection specifically leveraging correlated endpoint and network telemetry, rather than accepting a generic claim of "detection." This precision is fundamental to making informed decisions in a converging market.

V. The Role of AI and Machine Learning

Artificial Intelligence (AI) and Machine Learning (ML) are increasingly prominent forces reshaping SOC technologies and operations. Initially viewed primarily as tools for automation and augmentation, AI/ML are becoming deeply integrated into the core fabric of next-generation SOC platforms, enhancing existing capabilities and enabling entirely new approaches to cybersecurity.

A. Enhancing Core Capabilities

AI/ML technologies are being applied across the SOC workflow to improve efficiency, accuracy, and speed, moving beyond simple rule-based logic or basic automation:

Threat Detection: ML algorithms excel at analyzing vast datasets to identify subtle patterns and anomalies that may evade traditional detection methods. Unsupervised learning can detect deviations from learned baselines, potentially identifying novel or zero-day threats without prior signatures. Supervised learning models can be trained to recognize specific complex attack types. Behavioral analysis powered by ML can model normal user and entity behavior and flag significant deviations indicative of compromise. AI can process and correlate data from diverse sources at speeds unattainable by human analysts.
Alert Triage & Prioritization: A major challenge in traditional SOCs is alert fatigue caused by high volumes of alerts, many of which are false positives. ML models can be trained to automatically score the risk associated with alerts or group related alerts into single, higher-confidence incidents (as seen in XSIAM's approach ). This helps filter noise, prioritize the most critical threats, and significantly reduce the manual triage burden on analysts. AI can automate the initial investigation steps for common alert types.
Incident Investigation: AI, including generative AI (GenAI), is being applied to accelerate investigations. Capabilities include automatically correlating related alerts and events across different data sources ("stitching" ), summarizing complex incidents into narrative descriptions, suggesting relevant investigation paths or queries, and translating natural language questions from analysts into the platform's specific query language (e.g., KQL). AI can also automatically enrich incidents with relevant context from internal systems and external threat intelligence.
Response Automation: AI can enhance SOAR capabilities by enabling more dynamic and context-aware automation. Instead of static playbooks, AI could potentially adjust response workflows based on real-time threat intelligence or the specific characteristics of an unfolding incident. For high-confidence detections of known threat patterns, AI might recommend specific containment actions or, in some envisioned autonomous systems, execute them automatically to minimize damage.
Reporting: GenAI tools can assist analysts in drafting incident summaries, executive reports, or post-mortem analyses, potentially translating them into different languages or formats as needed.
Phishing Detection: AI models analyze various aspects of emails – content, headers, sender reputation, embedded links, attachments, and behavioral patterns – to identify sophisticated phishing attempts that might bypass simpler filters.

Early perspectives on AI/ML in the SOC, reflected in surveys from a few years ago, emphasized its role in augmenting human analysts rather than replacing them, often noting that automation could handle repetitive tasks but skilled staff remained essential. However, the positioning and capabilities of newer platforms indicate a significant shift. Solutions like Cortex XSIAM are explicitly marketed as "AI-driven," suggesting that AI and ML are not just augmentations but core components responsible for fundamental analysis, correlation, triage, and even response decisions. Concepts like the "AI SOC" envision AI handling the bulk of alert processing and routine responses. Platforms like IBM's QRadar SIEM leverage pre-trained AI models to improve alert quality and prioritization , while Google Security Operations uses Gemini AI for natural language search, summarization, and playbook generation. This trajectory suggests that AI is increasingly taking over core analytical functions, potentially freeing human analysts to focus on more strategic activities like complex threat hunting, managing novel incidents, and improving the AI systems themselves. The role of AI appears to be evolving from a supporting tool to a central engine within the modern SOC architecture.

B. Enabling New Capabilities

Beyond enhancing existing functions, AI/ML holds the potential to enable entirely new capabilities within the SOC:

Predictive Threat Analytics: By analyzing historical attack data, current threat intelligence, and organizational vulnerabilities, ML models may be able to forecast likely future attack vectors or identify emerging threat campaigns targeting the organization or its sector, enabling proactive defense adjustments.
Automated Threat Hunting: AI could potentially automate aspects of threat hunting by continuously searching for complex, anomalous patterns of behavior across vast datasets, guided by learned adversary models or TTPs, rather than relying solely on predefined rules or analyst-driven hypotheses.
Autonomous Response: In certain scenarios with high confidence detections and well-understood threats, AI systems could potentially make and execute containment decisions independently and in real-time, offering response speeds faster than human intervention allows. This remains a forward-looking capability with significant associated risks and requires careful implementation.

C. Considerations and Limitations

Despite the significant potential and increasing integration of AI/ML, organizations must approach these technologies with realistic expectations and awareness of their limitations:

Data Quality Dependency: The performance of AI/ML models is heavily reliant on the quality, quantity, and relevance of the data used for training and operation. Incomplete, biased, or inaccurate data can lead to flawed analysis, incorrect detections, or biased outcomes. Ensuring a robust and representative data foundation is critical.
Explainability and Trust: Many advanced ML models, particularly deep learning algorithms, can function as "black boxes," making it difficult to understand precisely why they reached a specific conclusion. This lack of transparency can hinder analyst trust, make it challenging to validate findings, complicate forensic investigations, and pose challenges for regulatory compliance.
Potential for New Errors: AI systems are not infallible. They can generate novel types of false positives if models are poorly tuned or encounter unexpected data. Furthermore, adversaries are actively researching ways to evade AI-based detection (adversarial AI), requiring continuous model updates and monitoring.
Skills Gap Evolution: While AI automates certain tasks, it creates demand for new skills related to data science, AI model management, prompt engineering, and interpreting AI outputs. SOC teams need to adapt their skillsets to effectively leverage and oversee these technologies.
Cost and Complexity: Implementing and maintaining sophisticated AI/ML capabilities can involve significant investments in technology, infrastructure, data pipelines, and specialized expertise. The total cost of ownership needs careful consideration.
Operational Reality vs. Hype: It is crucial to distinguish between the marketed potential of AI and its current practical effectiveness in real-world SOC environments. Historical data indicated relatively low satisfaction with AI/ML tools among SOC practitioners. More recent survey data from SANS Institute suggests continued caution, with fewer organizations reportedly planning AI/ML deployments and expressing lower satisfaction compared to previous years. This persistent skepticism, despite intense vendor marketing , highlights a potential gap between the promised benefits and the experienced reality for many organizations. This discrepancy may stem from implementation challenges, unmet expectations regarding automation levels, difficulties in demonstrating ROI, lingering trust issues, or simply a lag in adoption maturity across the broader market. Security leaders must critically evaluate AI claims and consider pilot projects or phased implementations to validate value within their specific context.

VI. Utilizing the Capability Catalog

The platform-agnostic SOC Capability Catalog presented in Section IV serves as a practical tool for security leaders to navigate the complexities of the modern SOC technology market and drive strategic improvements. Its value extends beyond simple technology selection to encompass service definition and maturity planning.

A. Evaluating Technology Stacks and Vendor Offerings

The primary application of the catalog is to bring clarity and objectivity to the technology evaluation process. CISOs and their teams can use the detailed capability list as a structured framework:

Requirements Definition: Before engaging vendors, organizations can use the catalog to identify and prioritize the specific capabilities essential for their SOC, based on their risk profile, industry, compliance obligations, and operational maturity.
Vendor Comparison: During RFI/RFP processes or technology bake-offs, the catalog provides a standardized checklist. Vendor features and functionalities can be mapped directly to the agnostic capabilities defined in the catalog. This allows for a direct comparison of how well different solutions (regardless of their marketing label – SIEM, XDR, SOAR, XSIAM, etc.) meet the organization's specific needs. Questions shift from "Does Vendor A offer XDR?" to "How effectively does Vendor A provide 'Behavioral Threat Detection using correlated endpoint and network data' or 'Automated Response Orchestration for phishing incidents'?"
Gap Analysis: By mapping the capabilities of their existing toolset against the catalog, organizations can identify critical functional gaps that need to be addressed or areas where multiple tools provide overlapping capabilities, presenting opportunities for consolidation and cost savings.
Integration Assessment: The catalog helps assess how well a platform integrates multiple capabilities versus requiring stitching together disparate point solutions. A platform natively providing strong capabilities across multiple NIST CSF functions might offer greater efficiency than integrating several best-of-breed tools.

B. Defining SOC Service Requirements

The catalog is equally valuable when evaluating or procuring managed security services, such as those offered by Managed Security Service Providers (MSSPs) or Managed Detection and Response (MDR) vendors:

Clear Scope Definition: Organizations can use the capability list to precisely define the scope of services required from a provider. Which specific capabilities (e.g., 24/7 Monitoring, Alert Triage, Level 1 Investigation, Threat Hunting, specific Response Actions) are expected to be delivered by the service provider, and which will remain in-house?.
Provider Evaluation: The catalog serves as a basis for evaluating the capabilities of different service providers, ensuring their offerings align with the organization's requirements. It facilitates detailed discussions about the provider's processes, technology stack (in terms of the capabilities it enables), and personnel expertise related to each required function.
Service Level Agreements (SLAs): Specific capabilities from the catalog can be referenced when defining SLAs, ensuring clarity on performance expectations for functions like detection time, response time for specific incident types, or reporting frequency.
Hybrid Model Design: For organizations adopting a hybrid SOC model (combining internal and external resources), the catalog helps delineate responsibilities clearly, defining which capabilities are insourced versus outsourced.

C. Driving SOC Maturity and Strategy

Beyond procurement, the capability catalog can be a strategic tool for continuous improvement and maturity assessment:

Benchmarking: Organizations can use the catalog to assess their current capability levels across the NIST CSF functions, comparing their state against desired future states, industry benchmarks, or peer organizations. Frameworks like the SOC-CMM (Capability Maturity Model) can complement this process by providing maturity levels for specific capability domains.
Roadmap Development: The results of the capability assessment can inform the SOC's strategic roadmap. Identifying weaknesses or gaps in specific capabilities allows organizations to prioritize investments – whether in new technology, process improvements, automation initiatives, or staff training and development – to achieve their target maturity level.
Justifying Investments: By linking specific technology capabilities to required operational functions and overall risk reduction objectives outlined in frameworks like NIST CSF, security leaders can build stronger business cases for SOC investments.
Aligning Technology with Business Risk: The capability-based approach encourages a focus on achieving necessary security outcomes rather than acquiring technology for its own sake. This helps ensure that the SOC's technology strategy remains aligned with the organization's overall business objectives and risk tolerance.

By leveraging the catalog in these ways, organizations can move towards a more deliberate, requirements-driven approach to building and evolving their SOC, ensuring that their people, processes, and technology work together effectively to manage cyber risk.

VII. Conclusion

The landscape of Security Operations Center technology is undergoing a profound transformation, marked by the rapid convergence of capabilities previously siloed within distinct product categories like SIEM, SOAR, EDR, and NDR. The emergence of XDR and integrated, AI-driven platforms such as XSIAM further accelerates this trend, offering the promise of enhanced detection, streamlined workflows, and improved efficiency. However, this evolution simultaneously creates significant challenges for security leaders tasked with evaluating, selecting, and integrating these complex solutions. Traditional category labels are losing their descriptive power, making objective comparisons difficult and increasing the risk of misaligned technology investments [User Query].

This report has argued for and presented a platform-agnostic SOC Capability Catalog as a necessary tool to navigate this complexity. By deconstructing SOC functions into granular, clearly defined capabilities organized around the robust NIST CSF 2.0 framework, this catalog provides a standardized lexicon and evaluation methodology. It shifts the focus from ambiguous marketing terms to the specific security functions a technology performs, enabling a more objective assessment of how well different solutions meet an organization's unique operational requirements and risk management objectives.

The true value of this capability-based approach lies in its empowerment of security leaders. It provides the clarity needed to define precise requirements, conduct meaningful vendor comparisons, identify critical gaps in existing defenses, and make strategic decisions about technology adoption, service procurement, and SOC maturity development. In an era where threats are constantly evolving and the pressure on security teams continues to mount, focusing on the fundamental capabilities required to achieve desired security outcomes is paramount. Adopting this perspective allows organizations to build more effective, efficient, and resilient security operations, ensuring their technology investments truly contribute to safeguarding the enterprise in the face of a dynamic and challenging cyber threat environment.

VIII. Appendix

Glossary of Terms

AI (Artificial Intelligence): The theory and development of computer systems able to perform tasks normally requiring human intelligence.
ASM (Attack Surface Management): The continuous discovery, inventory, classification, and monitoring of an organization's internal and external IT assets (attack surface).
ATT&CK (Adversarial Tactics, Techniques, and Common Knowledge): A globally accessible knowledge base of adversary tactics and techniques based on real-world observations, maintained by MITRE.
CSF (Cybersecurity Framework): A set of standards, guidelines, and best practices to manage cybersecurity risk, developed by NIST.
CUI (Controlled Unclassified Information): Information that requires safeguarding or dissemination controls pursuant to and consistent with applicable law, regulations, and government-wide policies but is not classified under Executive Order 13526 or the Atomic Energy Act, as amended.
EDR (Endpoint Detection and Response): Technology focused on monitoring endpoint activity, detecting threats, investigating incidents, and enabling response actions on endpoints.
ERM (Enterprise Risk Management): A plan-based business strategy that aims to identify, assess, and prepare for any dangers, hazards, and other potentials for disaster—both physical and figurative—that may interfere with an organization's operations and objectives.
IAM (Identity and Access Management): Framework of policies and technologies for ensuring the right individuals access the appropriate resources at the right times for the right reasons.
IOC (Indicator of Compromise): Forensic data, such as data found in system log entries or files, that identify potentially malicious activity on a system or network.
IR (Incident Response): An organized approach to addressing and managing the aftermath of a security breach or cyberattack.
ITDR (Identity Threat Detection and Response): Security solutions focused on detecting and responding to threats targeting identity systems and credentials.
KQL (Kusto Query Language): A query language used to perform data exploration and analysis in various Microsoft services, including Microsoft Sentinel.
MDR (Managed Detection and Response): Outsourced services providing organizations with threat hunting, monitoring, and response capabilities.
ML (Machine Learning): A subset of AI that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
MSSP (Managed Security Service Provider): An outsourced provider of security management services, often including monitoring, alerting, and device management.
MTTR (Mean Time To Respond/Remediate/Resolve): A metric measuring the average time it takes to control, fix, and eradicate a threat after detection.
NDR (Network Detection and Response): Technology focused on monitoring network traffic to detect threats and enable response actions at the network level.
NIST (National Institute of Standards and Technology): A non-regulatory agency of the U.S. Department of Commerce that promotes innovation and industrial competitiveness.
SEM (Security Event Management): Technology focused on real-time monitoring, correlation, and notification of security events.
SIEM (Security Information and Event Management): Technology combining SIM and SEM capabilities for log management, real-time monitoring, event correlation, and reporting.
SIM (Security Information Management): Technology focused on the collection, storage, analysis, and reporting of log data, primarily for compliance and forensics.
SOC (Security Operations Center): A centralized function within an organization employing people, processes, and technology to continuously monitor and improve an organization's security posture while preventing, detecting, analyzing, and responding to cybersecurity incidents.
SOAR (Security Orchestration, Automation, and Response): Technology used to automate and orchestrate incident response workflows and tasks.
TIP (Threat Intelligence Platform): Technology used to aggregate, correlate, manage, and disseminate threat intelligence data.
TTPs (Tactics, Techniques, and Procedures): Patterns of activities and methods associated with specific threat actors or groups.
UEBA (User and Entity Behavior Analytics): Technology focused on detecting insider threats, targeted attacks, and financial fraud by analyzing user and entity behavior patterns.
XDR (Extended Detection and Response): Technology that unifies security data and response actions across multiple security layers (endpoint, network, cloud, email, identity).
XSIAM (Extended Security Intelligence and Automation Management): An AI-driven SOC platform concept (term coined by Palo Alto Networks) aiming to unify SIEM, SOAR, XDR, ASM, and other capabilities.

Works cited

Future of Cybersecurity: Will XDR Absorb SIEM & SOAR? | Trend Micro (DK), https://www.trendmicro.com/en_dk/research/25/a/xdr-siem-soar.html

Adapt Or Die: XDR Is On A Collision Course With SIEM And SOAR - Forrester, https://www.forrester.com/report/adapt-or-die-xdr-is-on-a-collision-course-with-siem-and-soar/RES165775
Redefining SecOps in the Era of AI - Palo Alto Networks, https://www.paloaltonetworks.com/resources/whitepapers/redefining-secops-in-the-era-of-ai.viewer.html
Common and Best Practices for Security Operations Centers: Results of the 2019 SOC Survey | SANS Institute, https://www.sans.org/media/analyst-program/common-practices-security-operations-centers-results-2019-soc-survey-39060.pdf
Gartner® Report: Create an SOC Target Operating Model to Drive Success, https://www.bankinfosecurity.com/whitepapers/gartner-report-create-soc-target-operating-model-to-drive-success-w-9532
nvlpubs.nist.gov, https://nvlpubs.nist.gov/nistpubs/CSWP/NIST.CSWP.29.pdf
Security Operations Center (SOC) - Cynet, https://www.cynet.com/incident-response/what-is-a-soc-10-core-functions-and-6-key-challenges/
NIST Security Operations Center Best Practices, https://blog.rsisecurity.com/nist-security-operations-center-best-practices/
NIST, MITRE ATT&CK? Choosing a SOC Framework - Blink Ops, https://www.blinkops.com/blog/security-operations-center-framework
How to Build a Security Operations Center (SOC): Peoples, Processes, and Technologies, https://www.digitalguardian.com/blog/how-build-security-operations-center-soc-peoples-processes-and-technologies
4 Security Operations Center Frameworks You Should Know - BlueVoyant, https://www.bluevoyant.com/knowledge-center/4-security-operations-center-frameworks-you-should-know
Difference Between SIM, SIEM, Log Management, and Log Analysis - Trunc, https://trunc.org/learning/whats-the-difference-between-sim-siem-log-management
SIEM vs. IDS: What is the Difference? - UpGuard, https://www.upguard.com/blog/siem-vs-ids
Security Information & Event Management (SIEM) - CrowdStrike.com, https://www.crowdstrike.com/en-us/cybersecurity-101/next-gen-siem/security-information-and-event-management-siem/
Security information and event management - Wikipedia, https://en.wikipedia.org/wiki/Security_information_and_event_management
What is Security Information and Event Management (SIEM)? - SecurityScorecard, https://securityscorecard.com/blog/what-is-security-information-and-event-management-siem/
2024 Gartner® Magic Quadrant™ for SIEM - Splunk, https://www.splunk.com/en_us/form/gartner-siem-magic-quadrant.html
Security Operations Center Training - SANS Institute, https://www.sans.org/soc/
Security Operations Center (SOC) Roles and Responsibilities - Palo ..., https://www.paloaltonetworks.com/cyberpedia/soc-roles-and-responsibilities
SIEM: Security Information & Event Management Explained - Splunk, https://www.splunk.com/en_us/blog/learn/siem-security-information-event-management.html
Application of artificial intelligence and machine learning in a security operations center - International Association for Computer Information Systems, https://iacis.org/iis/2023/4_iis_2023_311-327.pdf
Cortex XSIAM Solution Brief - Palo Alto Networks, https://www.paloaltonetworks.com/resources/ebooks/cortex-xsiam
EDR vs. XDR vs. SIEM vs. MDR vs. SOAR | Sysdig, https://sysdig.com/learn-cloud-native/edr-vs-xdr-siem-vs-mdr-vs-soar/
SOC 2 vs. NIST: Choosing the Right Compliance Framework for You - Bright Defense, https://www.brightdefense.com/resources/soc-2-vs-nist/
Decoding NIST Compliance: Your Guide to the Cybersecurity Framework, NIST 800-53, and NIST 800-171 - Hyperproof, https://hyperproof.io/resource/a-complete-guide-to-nist-compliance/
SIEM vs SIM vs SEM-Huawei Enterprise Support Community, https://forum.huawei.com/enterprise/en/SIEM-vs-SIM-vs-SEM/thread/667247278500495360-667213859733254144
What is SIEM? - ISTARI Global, https://istari-global.com/insights/glossary/what-is-siem/
Understanding SOAR vs SIEM vs XDR | Secureworks, https://www.secureworks.com/blog/xdr-vs-soar-finding-the-right-tool-for-the-job
AI/ML in Security Orchestration, Automation and Response: Future Research Directions, https://www.techscience.com/iasc/v28n2/42057/html
What is the Gartner® Magic Quadrant for SIEM? - Rapid7, https://www.rapid7.com/fundamentals/gartner-magic-quadrant-for-siem/
IBM named a Leader in Gartner Magic Quadrant for SIEM, for the 14th consecutive time, https://www.ibm.com/blog/announcement/ibm-leader-gartner-magic-quadrant-siem/
Top 10 XDR Definitions | Blumira: Extended Detect & Response, https://www.blumira.com/blog/10-definitions-of-xdr-extended-detection-response
Machine Learning in Cyber Security: Enhancing SOC Operations with Predictive Analytics, https://www.researchgate.net/publication/389350863_Machine_Learning_in_Cyber_Security_Enhancing_SOC_Operations_with_Predictive_Analytics
5 ways AI is being used to improve security: Automated and augmented incident response, https://blog.barracuda.com/2024/07/01/5-ways-ai-is-being-used-to-improve-security--automated-and-augme
Gartner: Market Guide for SOAR Solutions - Palo Alto Networks, https://www.paloaltonetworks.com/blog/2020/10/secops-gartner-soar-solutions/
AI-Driven Security Operations Center: AI SOC Explained, https://securityboulevard.com/2025/03/ai-driven-security-operations-center-ai-soc-explained/
What is Extended Detection and Response (XDR)? - Sangfor Technologies, https://www.sangfor.com/blog/cybersecurity/what-is-extended-detection-and-response-xdr
Corelight recognized for SaaS and Cloud Identity Applications Security in the Gartner Competitive Landscape Report*, https://corelight.com/blog/gartner-ndr-competitive-corelight
EDR, XDR, NDR, SOAR, MDR: What are the Differences and Why Should you Care?, https://www.channele2e.com/native/edr-xdr-ndr-soar-mdr-what-are-the-differences-and-why-should-you-care
Top EDR/XDR Tools to Know in 2024 - CIO Influence, https://cioinfluence.com/it-and-devops/top-edr-xdr-tools-to-know-in-2024/
What is XDR? Extended Detection & Response Security Definition ..., https://www.rapid7.com/fundamentals/extended-detection

Franklin Donahoe