1. Historical Evolution of Logging and the Genesis of Audit Trails
Data logging, originally primitive records of system status, has undergone a dramatic evolution intrinsically linked to the complexity and interconnectedness of modern IT systems. Understanding the history of logs is essential for assessing the validity of current categorizations.
1.1. Beginnings of Centralized Logging and the Rise of Syslog
The need for robust log management became urgent in the 1970s and 1980s when engineers began connecting computers into networks. The emergence of the network environment led to an exponential increase in reliability and security issues, requiring a completely new dimension for building and maintaining systems. Log management thus became an essential tool for understanding and managing the complexity of networked computers.
A key milestone in this early era was Syslog. This protocol, which became the de-facto standard for centralized logging, was developed by Eric Allman in the 1980s at Berkeley, originally as part of the sendmail mail server (a conversion of Delivermail for ARPANET). Syslog initially served a dual purpose: audit logging (records important for security and monitoring) and debugging (records for resolving operational issues).
This historical fact points to a formative logging principle: Early technological solutions often did not see a sharp distinction between records for operational purposes and records for security purposes. The data needed to debug network errors were often identical to the data required for a forensic audit. Although original logging was unstructured and primarily focused on operating system events, it already demonstrated a fundamental convergence of the needs of operational and security teams.
1.2. Definitional Foundation: Audit Trail, Security Log, and the Start of Differentiation
Throughout the 1990s and the beginning of the 21st century, the understanding of the Audit Trail (or Audit Log) was formalized. An Audit Trail is defined as a chronological record that provides documentary evidence of the sequence of activities that have affected a specific operation, procedure, or device at any time. These records are critical for reconstructing security breaches and distinguishing between operator error and system failure. Special emphasis was placed on ensuring the integrity of the Audit Trail—the process of its creation must run in a privileged mode, and the trail must be protected against modification by a normal user to be trustworthy, for instance, in legal proceedings.
This need for differentiation led to the traditional, architecturally driven demarcation of log types:
- System Logs: Records related to the status of the operating system, hardware, errors, or process startup. They served primarily for operational monitoring and performance troubleshooting.
- Application Logs: Detailed records of specific software functionality, such as database queries, file uploads, or user interactions with the application. Important for DevOps and SRE teams.
- Security Logs: The guardians of the system, tracking events explicitly associated with security—login attempts, permission changes, and the detection of potential threats or anomalies.
The traditional differentiation arose mainly from the need for organizational and architectural division of responsibility between IT Operations (Ops) and Security Operations (SecOps). Security logs were intended to be immediately actionable for defense, while system and application logs supported IT operations.
1.3. Standardization for Security: The Rise of CEF and LEEF
With the development of SIEM (Security Information and Event Management) platforms in the early 21st century, the need arose to correlate events in a heterogeneous environment. The original Syslog, which used unstructured text, was inefficient for automated correlation. This led to a shift in priority from merely the delivery protocol to the content and structure of the data.
SIEM became the catalyst that forced the industry to standardize log formats. Open standards such as CEF (Common Event Format) and LEEF (Log Event Extended Format) emerged; these are extensions of Syslog, designed specifically for interoperability and the management of security information. These formats enforce the use of a structured schema that standardizes critical security metadata, such as device vendor, event severity, source and destination IP addresses, username, and action taken.
This development confirmed that for effective threat analysis in a modern environment, data must be normalized, regardless of whether it originates from a firewall (traditional security log) or a web server (traditional application log). SIEM platforms need a unified schema to quickly provide contextual analysis for investigating security events, thereby blurring the semantic differences between logs in the analytical phase.
2. Disruption of the Traditional Paradigm: Viability of Differentiation
The question of whether the differentiation between service logs and security logs is still viable in modern cybersecurity leads to a clear conclusion: From the perspective of threat detection and forensic analysis, the functional distinction of logs is practically unsustainable. This change results from the shift towards behavioral detection and the sophistication of attacks that exploit legitimate operational mechanisms.
2.1. Nation-State Actor Attacks and the Collapse of Log Silos
The most significant evidence of the collapse of the traditional paradigm is the response to targeted attacks carried out by nation-state actors. The 2023 compromise of Microsoft Exchange Online, where a hacker group supported by the PRC infiltrated the email accounts of senior government officials, showed that expanded logging capabilities that were not standardly available were essential for detection and response.
Útočníci v tomto případě kompromitovali cloudovou službu a zneužili legitimní uživatelské účty k prohledávání e-mailů a exfiltraci dat. Detection of this anomalous activity was not possible based solely on traditional security logs (such as failed login attempts) but required access to detailed records of activity in M365, including events such as mail items accessed and user searches. These records, often categorized as Cloud Audit Logs or Application Logs, became critical security data. CISA (Cybersecurity and Infrastructure Security Agency) subsequently issued a playbook for federal agencies emphasizing the necessity of implementing these expanded logs, confirming that all data from the Control Plane and applications in the cloud environment are security-relevant.
The shift in defensive strategy is evident here: attacks have moved from detecting binary intrusions to detecting behavioral anomalies and exploiting legitimate accounts. To understand what is anomalous, defenders must first analyze all data describing “what is normal.” This expansion effectively blurs the boundary; any log that contributes to reconstructing the attacker’s TTPs (Techniques, Tactics, and Procedures) immediately becomes a Security Log.
2.2. Using “Non-Security” Logs for Lateral Movement Detection
The Lateral Movement phase within the cyber kill chain is often performed using legitimate operating system tools that primarily generate system or application logs. Without correlation with this data, detection is difficult, if not impossible.
For example, when tracking lateral movement in a Windows environment, security teams rely not only on standard Windows Security Logs (e.g., Event Code 4624 – Successful Logon), but also on System Logs and application records traditionally falling into the operational category. An example is WinRM connection logs, found in the Microsoft‐Windows‐WinRM%4Operational log (ID 91/168). These records are crucial for identifying the use of PowerShell remoting for an attacker’s horizontal shift. Similarly, detecting access via network shares (Server Message Block – SMB) is necessary, where records like Event ID 5140, even if not enabled by default, provide critical forensic evidence of the source IP address and account that accessed shared resources.
This need for deep forensic detail is fully reflected in MITRE ATT&CK, which explicitly lists application and SaaS logs (e.g., Web Application Logs, Email Application Logs, Cloud Application Logs) as critical data sources (Data Source ID: DS0015) for detection. This demonstrates that data previously collected by IT teams for troubleshooting or performance monitoring purposes is now indispensable for cyber defense. Log differentiation thus becomes irrelevant at the level of detection semantics.
2.3. Evidence of Convergence in the Cloud Environment
Cloud computing has definitively accelerated the convergence of logs. In a cloud environment, where much of the infrastructure is abstracted, the center of gravity for detection shifts to Control Plane activity, API calls, and interactions within services (SaaS).
Cloud providers (e.g., Google Cloud) and security tools categorize logs not by their original type (OS, Application) but by their relevance to tactical and technical objectives (MITRE ATT&CK) or compliance standards (CIS). Tools for log scoping explicitly include Cloud Audit Logs, Access Transparency logs, and various platform logs as key security-relevant data sets. In this centralized model, all metadata and activity records in the cloud are implicitly considered security logs because they can be exploited or indicate compromise.
3. In-Depth Analysis of Log Differentiation Viability: Practical Implications
Although the functional necessity of detection and forensic analysis leads to unification, in practice, organizations continue to attempt architectural separation of logs. The main reason for maintaining this segmentation effort is not security, but economic and operational optimization.
3.1. Operational Viability and Economic Costs
One of the biggest problems in modern cybersecurity is Data Overload. Systems generate vast volumes of logs, and while collecting everything is ideal from a security perspective, indexing and long-term retention of every data point in high-performance SIEM or XDR platforms is extremely expensive. Log Management costs, especially Ingestion Cost and Storage Cost, become a critical factor in the TCO (Total Cost of Ownership).
IT infrastructure architects therefore often choose a compromise: segregating data according to their analytical priority and required access speed. Critical security logs and logs needed for fast correlation go into expensive “Hot Storage” (quickly indexable SIEM/XDR databases), while less critical operational logs, which may be needed for in-depth forensic investigation after a longer period, are moved to cheaper “Cold Storage” or long-term archives (e.g., S3, Tape).
The effort to maintain an architectural distinction (e.g., System/Application logs vs. Security logs) is thus primarily motivated by economic optimization and reduction of indexing costs, not by a difference in the security relevance of the data. CISOs must constantly balance the security imperative (rapid availability of all data) against financial constraints.
3.2. Architectural Dictate: From SIEM to XDR
The evolution of security platforms underscores the trend of data unification. Traditional SIEM platforms focused on providing contextual analysis by correlating logs with data about users, vulnerabilities, and assets. However, the modern approach leans towards XDR (Extended Detection and Response).
XDR is designed to bridge the gaps and weaknesses that plague traditional SIEM (e.g., Data Overload and Alert Fatigue). XDR aggregates and correlates data across a wide spectrum of sources—endpoints, network, email, cloud—into a single unified Data Lake. Although XDR may not completely replace SIEM for all non-threat use cases (such as comprehensive compliance reporting or non-security data management), its primary function for threat detection and incident response is to unify all relevant data sources.
In this model, the separation of logs is replaced by the concept of Observability, where Logs, Metrics, and Traces are considered three interconnected pillars. Logs provide full context and explain why an event occurred, which Metrics only indicate. From an architectural perspective, convergence thus leads to all data being collected together; differentiation then occurs dynamically using AI/ML and prioritization for indexing.
3.3. The Role of Behavioral Analysis (UEBA)
Behavioral analysis (part of UEBA – User and Entity Behavior Analytics) is a key method for detecting anomalies that traditional signature-based systems cannot catch. UEBA is based on understanding the “normal” behavior of entities (users, systems) and flagging deviations (e.g., logging in from two places simultaneously, accessing unusual systems).
To establish a valid baseline of normal behavior, UEBA needs access to a massive amount of low-level operational logs that record routine interactions. This definitively blurs the line between operational and security logs. A log about a daily database call, which would previously have been a purely application record, becomes a critical security log if its anomalous frequency or parameters indicate an attack.
Shift in Logging Paradigm: From Traditional to Modern (SIEM/XDR)
| Criterion | Traditional Model (Pre-2010) | Modern Model (Cloud and XDR Era) |
| Definition of Security Log | Narrow: Log-in/out, Firewall, ACL. | Broad: Any Log with Forensic Relevance. |
| Primary Source | OS Event Logs, Syslog. | OS, Cloud Audit Logs (Control Plane), SaaS API Logs, Application/Web Logs. |
| Main Purpose | Audit, Reactive Incident Response. | Proactive Threat Detection, Data Correlation, Behavioral Analysis (UEBA). |
| Format Standardization | Syslog (Unstructured Text). | CEF, LEEF, JSON, OpenTelemetry (Structured and Normalized Schema). |
| Regulatory Dictate | SOX/HIPAA (Accounting/Health Data). | NIS2 (Integrity, Retention), GDPR (PII, Pseudonymization). |
4. Regulatory Pressure and Log Integrity Requirements
Regulations and compliance standards, especially the European directives NIS2 (Network and Information Security 2) and GDPR, further enforce log management convergence by extending high demands for integrity and PII protection to a wider set of operational data.
4.1. NIS2 and the Dictate of Immutability
The NIS2 directive places significant emphasis on Log Management. It requires organizations to ensure comprehensive collection, normalization, and secure storage of logs from all critical assets (servers, endpoints, network devices, cloud services). A key aspect is the requirement for integrity and retention. Logs must be protected against unauthorized tampering, which often requires implementing mechanisms such as hashing, immutability, or WORM storage (Write Once Read Many).
If a critical system generates both explicitly security logs (e.g., firewall records) and operational logs (e.g., system status events) that have forensic value in incident reconstruction, then both sets of data must be managed with the requirement for immutability according to NIS2. This regulatory pressure acts as an indirect technical standard. It transfers the strictest requirements for the integrity of historical Audit Trails, which previously applied only to a narrow set of security data, to a much broader base of system and application logs generated by critical infrastructure. From a management and process perspective, the boundary between service and security logs essentially blurs.
NIS2 further sets minimum retention periods, recommending 6 to 12 months for forensic needs. Organizations must ensure that this storage is scalable and accounts for growing data volumes to prevent the overwriting of older but still forensically valuable logs.
4.2. GDPR and PII Protection in Logging Systems
The legal protection of personal data (PII) under GDPR brings another argument for unified Log Management. Logs, whether system, application, or security logs, often contain PII, such as IP addresses, usernames, or email addresses.
GDPR defines pseudonymization as an appropriate protective measure that reduces the risk of data misuse. Pseudonymized data that can be linked to a natural person with additional information (e.g., decryption keys) is still considered personal data. For this reason, GDPR requires that all additional information (e.g., decryption keys to reverse pseudonymization) be stored separately from the pseudonymized data.
If application logs were managed separately from security logs, the organization would risk inconsistent implementation of pseudonymization and protective measures across data silos. Comprehensive management of PII in logging systems is effective only with centralized control that ensures consistent adherence to retention (e.g., for employee records or communication) and data protection.
5. The Future of Log Management and the Role of Artificial Intelligence
The future of log management lies in the ability to handle Data Overload and in advanced automated analysis that dynamically determines the security relevance of logs.
5.1. AI/ML in Log Analysis and Anomaly Identification
Modern systems generate logs in such volume and velocity that human teams are unable to analyze them effectively. This is where the role of Artificial Intelligence and Machine Learning (AI/ML) comes in.
AI/ML algorithms are essential for transforming Big Data into actionable information. They enable rapid categorization of log streams, automatic identification of anomalies, and proactive detection of root causes before problems escalate. The ability of machine learning to find complex patterns in data and detect deviations faster than any human leads to a higher detection rate for malicious activities.
In the context of log differentiation, the role of AI is crucial: AI dynamically performs data sorting. A log that would statically fall into the “operational” category can be immediately reclassified by AI as “security-critical” if its behavior patterns correspond to a threat model. This replaces the static definition of Log Type with a dynamic, contextual, and behavioral definition.
5.2. Architectural Directions: XDR and Cloud Observability
Architectural trends point toward systems capable of unifying Log Management with advanced detection and response. XDR platforms with built-in automation and orchestration (SOAR) simplify operations for SOC teams by correlating data without the need for complex manual integrations. The goal is to reduce complexity and ensure that defenders can quickly close the OODA (Observe-Orient-Decide-Act) loop, thereby minimizing the operational impact of an attack.
A critical factor for future systems remains data consistency. Without accurate Timestamps and consistent formats (such as JSON or OpenTelemetry schema), reliable correlation cannot be performed between events originating from different systems. Therefore, normalization and format standardization remain key prerequisites for effective security analysis at massive scale.
6. Viability of Differentiation and the Proposed Universal Definition
Analysis of history, the current state of detection methods (Lateral Movement, UEBA), and regulatory requirements (NIS2, GDPR) leads to the conclusion that the traditional categorization of logs is functionally unsustainable in modern cybersecurity, but architecturally maintained for economic reasons.
6.1. Conclusion on the Viability of Differentiation
Modern, sophisticated attacks (such as those carried out by nation-state actors) are characterized by low noise and the exploitation of legitimate processes. Detecting such TTPs requires correlating events across system, application, network, and cloud logs. From the perspective of a security analyst, every log that is necessary to verify, refute, or describe an incident becomes a Security Log.
Maintaining the distinction (Security vs. Service Log) is artificially sustained by IT architects and financial departments primarily due to optimization of storage and indexing costs. Differentiation has moved from the functional level to the economic and retention level (fast vs. slow data availability). Furthermore, NIS2 extends the obligation to ensure integrity and longer retention to all logs from critical systems, effectively enforcing unified, security-oriented management for all data.
6.2. Proposed Universal Definition of a Security Log
Given the proven convergence of data sources, it is necessary to abandon static, origin-based definitions and adopt a definition focused on the purpose and forensic value of the log.
Proposed Universal Definition of a Security Log
A Security Log is a chronological, structured, and forensically reliable record (Audit Trail) of any event generated by a Host, Network, Application, Cloud Control Plane, or SaaS service, which must be ingested, protected against tampering (Immutability), and retained for a specified period for the purpose of:
- Detection, analysis, and validation of cybersecurity incidents or behavioral anomalies (Threat Detection and Incident Response).
- Forensic reconstruction of the sequence of events (Evidence) leading to an incident.
- Demonstrating compliance with regulatory requirements for audit, integrity, and retention (Compliance, e.g., NIS2).
This definition includes both explicit security events and operational and application events whose anomalous or specific combination indicates an actor’s TTPs (e.g., an unusual API call). Key attributes are not the type of log, but its format consistency (Structured Format), Timestamp Consistency, and proven integrity (WORM/Hashing).
7. Overview of Critical Log Convergence
The following table demonstrates how critical phases of modern attacks leverage logs that were previously categorized as purely operational or system logs.
Forensic Use of Operational (Service) Logs
| Traditional Log Type | Forensically Relevant Event | Cybersecurity Context (MITRE ATT&CK) | Reference |
| System/Endpoint Logs | Windows Event ID 4624 (Logon Type 3 – Network) | Lateral Movement (Remote Services) | |
| Application/System Logs | WinRM Operational Logs (ID 91/168) | PowerShell Remoting, Lateral Movement | |
| Cloud/Application Logs | API calls, User Searches (M365 Purview Audit) | Espionage, Data Exfiltration, Compromise Analysis | |
| Network Logs | SMB Share Access (ID 5140) | Lateral Movement, Exfiltration via Network Share |
Conclusions and Strategic Recommendations
The analysis of logging evolution and the pressure from modern threats and regulations confirms that the functional distinction between service logs and security logs is obsolete in the context of modern SOC and XDR platforms. Data is unified at the analysis level, with all logs from critical assets being considered security-relevant.
Recommendations for Strategic Investment and Log Management
- Priority of Collection and Normalization (Log Everything, Analyze Smart): Organizations must adopt a strategy of central Ingestion for all logs from critical assets into a unified Data Lake (SIEM/XDR). Log differentiation should not occur at the collection stage, but at the stage of analytical prioritization and storage. Critical factors are format standardization (JSON, CEF) and a unified time source for correlation.
- Integrity Driven by Compliance (NIS2/GDPR): Immediately implement mechanisms ensuring integrity (Immutability, Hashing, WORM) for all logs that have forensic or regulatory value, regardless of their traditional categorization (System/Application/Security). NIS2 compliance requirements for log management are becoming the de-facto security standard.
- Investment in XDR and Behavioral Analysis: Prioritize investments in XDR platforms and UEBA that can correlate data sources across endpoints, cloud, and network. Only such systems can utilize the full context of operational data to detect hidden anomalies and lateral movement.
Economic Storage Tiering: Given the unsustainable TCO, organizations should implement a tiered storage strategy: “Hot Storage” for quickly analyzable security events (short-term retention, high performance) and “Cold Storage” for long-term retention of deep operational logs needed for forensic analysis after months (low cost, slower access). This minimizes the operational risk arising from Data Overload while maintaining economic efficiency.