Security Information and Event Management, universally known as SIEM, has been a cornerstone of enterprise security operations for over two decades. It is the technology category that promised to give security teams centralised visibility across their entire infrastructure, correlate events from disparate sources and surface the threats that matter.
But the landscape today looks very different from its origins, and the technology's limitations have become as well documented as its capabilities. This article explains what SIEM is, how it works, where it struggles and why many organisations are now looking beyond traditional log aggregation towards unified detection platforms.
A Brief History
The category emerged in the early 2000s from the convergence of two earlier technologies: Security Information Management (SIM), which focused on log collection and long-term storage for compliance reporting, and Security Event Management (SEM), which focused on real-time event correlation and alerting.
The combined term came into common use in the mid-2000s, and the category quickly became a staple of enterprise security architecture. Early products such as ArcSight, QRadar and LogRhythm were deployed primarily to meet regulatory compliance requirements. Regulations such as PCI DSS, SOX and HIPAA mandated log retention and audit capabilities, and these platforms provided a centralised answer. The problem they were solving was already well understood: NIST's Guide to Computer Security Log Management (SP 800-92), published in September 2006, set out how organisations should develop, implement and maintain log management practices across an enterprise, and it remains the reference document for the discipline.
Over time, the role expanded from compliance-focused log management to active threat detection and security monitoring. Vendors added correlation rules, threat intelligence integration, behavioural analytics and incident response workflows. The modern platform is expected to be the nerve centre of the security operations centre.
How SIEM Works
At its core, the technology operates through four fundamental processes:
1. Data Collection
The platform ingests log data and event telemetry from across the IT environment. Sources typically include firewalls, intrusion detection systems, endpoints, servers, applications, cloud platforms, identity providers and network devices. Data is collected through agents, syslog forwarding, API integrations and file-based imports.
The breadth of data sources is both the greatest strength and the most significant operational challenge. A full deployment might ingest data from dozens or hundreds of distinct sources, each with its own log format, volume characteristics and reliability profile.
2. Normalisation and Parsing
Raw log data arrives in countless formats. The platform normalises this data into a common schema, mapping fields like source IP, destination IP, username, action and timestamp into standardised formats that enable cross-source correlation. Microsoft's Sentinel documentation, for example, describes both query-time and ingestion-time normalisation "to translate various sources into a uniform, normalized view".
Parsing and normalisation is a labour-intensive process. Custom log sources require custom parsers, and parser maintenance is an ongoing burden. When a vendor changes their log format, which happens regularly with software updates, existing parsers break and must be updated.
3. Correlation and Detection
Correlation is where raw data becomes security intelligence. Correlation rules define patterns of activity that indicate potential threats. A simple example: a failed login attempt from an external IP followed by a successful login from the same IP within five minutes, followed by access to a sensitive file share.
Modern platforms supplement rule-based correlation with statistical analytics and machine learning models that detect anomalies without predefined rules. However, the effectiveness of these analytics depends heavily on the quality and completeness of the underlying data.
4. Alerting and Reporting
When correlation rules or analytics models identify potential threats, the platform generates alerts for analyst investigation. It also provides dashboarding, reporting and search capabilities that support both real-time monitoring and forensic investigation.
Alert management is a critical function. Effective deployments include alert prioritisation, suppression of known false positives and integration with ticketing systems for case management.
Core Capabilities
Mature deployments typically deliver the following capabilities:
Centralised log management: A single repository for security-relevant log data from across the environment, enabling cross-source search and investigation.
Real-time monitoring: Continuous analysis of incoming events against correlation rules and analytics models, with alerting for potential threats.
Compliance reporting: Pre-built and customisable reports that demonstrate compliance with regulatory requirements, including log retention, access monitoring and change tracking.
Forensic investigation: Historical search capabilities that enable analysts to reconstruct attack timelines and understand the full scope of security incidents. The UK's National Cyber Security Centre puts the retention requirement plainly in its logging and monitoring guidance: "Ensure logs are held for long enough to be able to answer the questions you'll be asked during an incident."
Threat intelligence integration: Enrichment of events and alerts with external threat intelligence, enabling detection of known indicators of compromise.
The Limitations of Traditional SIEM
Despite its central role in security operations, the technology has well-documented limitations that have driven significant frustration among security teams.
Data Volume Pricing
Most vendors price on data ingestion volume, measured in events per second (EPS) or gigabytes per day. Splunk's pricing models page states that "ingest volume-based pricing is based on gigabytes of data ingested into Splunk". Microsoft Sentinel's billing documentation describes pay-as-you-go pricing "based on the actual data volume stored", with commitment tiers starting at 100 GB per day. As organisations grow, add cloud workloads and expand their digital footprint, data volumes increase and costs escalate with them.
This pricing model creates a perverse incentive: to control costs, organisations limit what they ingest. Security teams are forced to make difficult decisions about which log sources to exclude, effectively creating blind spots in their monitoring coverage. The tool designed to provide complete visibility ends up encouraging selective blindness.
Licence costs for a mid-market deployment run to six figures a year, and they rise year on year as data volumes grow.
Alert Fatigue
These platforms are notorious for generating excessive alerts. The root cause is that correlation rules operate on individual events and simple patterns, lacking the contextual understanding needed to distinguish genuine threats from benign anomalies.
Microsoft's own automation documentation describes the result: SOC teams "are typically inundated with security alerts and incidents on a regular basis, at volumes so large that available personnel are overwhelmed", so that "many alerts are ignored and many incidents aren't investigated". Analysts spend the majority of their time investigating and closing false positives.
Rule Maintenance Burden
Correlation rules require continuous maintenance. New threats require new rules. Changes to the IT environment, such as new applications, infrastructure changes and cloud migrations, require rule updates. False positive tuning requires ongoing attention.
Most organisations struggle to maintain their rule sets effectively. Rules become stale, new threats go undetected because corresponding rules have not been written, and tuning falls behind. The CardinalOps 2025 State of SIEM Detection Risk report, based on analysis of production deployments, found that enterprise SIEMs on average have detections for only 22% of MITRE ATT&CK techniques, even though they ingest enough data to cover 90%, and that 10% of rules are broken by misconfigured data sources, missing fields or parsing errors. The result is a growing gap between the threats the platform theoretically detects and the threats it actually catches.
Integration Complexity
Building and maintaining integrations with diverse data sources is a significant operational burden. Each integration requires parser development, testing and ongoing maintenance. API-based integrations are subject to breaking changes when vendors update their platforms.
Organisations frequently report that their deployment project took far longer than planned, and that achieving full coverage of their intended data sources required months of integration engineering.
Skills Requirements
Effective operation requires specialised skills: rule writing, query languages (SPL, KQL, Lucene), parser development and platform administration. These skills are in high demand and short supply. Many organisations cannot recruit or retain the engineering talent needed to operate the platform effectively.
The Evolution: Next-Generation SIEM
Recognising these limitations, the market has evolved. Next-generation products, including cloud-native platforms like Microsoft Sentinel, Google Security Operations and various emerging vendors, address some traditional limitations:
Cloud-native architecture eliminates infrastructure management and provides elastic scalability. However, cloud pricing still typically scales with data volume, and the fundamental data cost challenge remains.
Built-in UEBA (User and Entity Behaviour Analytics) supplements rule-based detection with machine learning models that detect anomalous user and entity behaviour. This reduces reliance on predefined rules but introduces new challenges around model tuning and false positive management.
SOAR integration provides automated response capabilities that help manage alert volumes. However, SOAR adds another layer of complexity and requires its own engineering investment in playbook development and maintenance.
Whilst next-generation products represent genuine improvements, they still operate within the fundamental paradigm: collect logs from other tools, normalise them and attempt to correlate across disparate data sources.
Beyond SIEM: The Unified Detection Platform
The most significant shift in the market is the emergence of unified detection platforms that challenge the log-aggregation paradigm altogether. Rather than aggregating logs from separate security tools, unified platforms collect telemetry directly from endpoints, networks, cloud workloads and identity systems using native sensors.
This architectural difference has profound implications:
Data volume is not the whole bill: Unified platforms like SenseOn use a consumption-based Flex Intelligence Credit (FIC) model. Credits are consumed by outcomes such as detection, investigation and AI-accelerated resolution, and data volume is one meter, not the whole bill.
Native correlation: Because the sensor's endpoint, network and identity telemetry flows into a single platform, correlation happens automatically. There are no integration gaps, no parser maintenance and no normalisation challenges.
AI-powered detection: Unified platforms apply advanced AI, including deep learning and behavioural analysis, directly to raw telemetry rather than to normalised log data. This enables more accurate detection with fewer false positives.
Reduced complexity: A single platform consolidates the sprawl of SIEM, EDR, NDR and SOAR tools, reducing licensing costs, integration burden and operational complexity.
SenseOn exemplifies this approach. The platform deploys lightweight sensors across endpoints, network infrastructure and cloud environments to collect endpoint, network, identity and cloud telemetry. The cross-domain correlation engine, combining supervised learning, unsupervised learning and deep learning, processes this telemetry to deliver high-fidelity detections without the alert fatigue that plagues traditional tools. In the AV-Comparatives Business Security Test, March to June 2024, SenseOn recorded zero false alarms in the Real-World Protection Test.
SenseOn's Flex Intelligence Credit model prices outcomes: detection and response across your data, analytics included, and the investigations SenseOn's AI completes. Data volume is one meter, not the whole bill. One annual credit commitment covers all capabilities, and the more you commit, the lower the unit rate. Organisations get a predictable annual commitment in which data volume is one meter, not the whole bill.
Making the Transition
For organisations currently invested in a SIEM, the transition to a unified platform does not need to be abrupt. Many SenseOn customers begin by deploying the platform alongside their existing tool, validating detection coverage and gradually migrating workloads as confidence builds.
The key evaluation criteria for any replacement should include:
- Detection accuracy: Does the platform deliver fewer false positives whilst maintaining or improving true positive detection rates?
- Total cost of ownership: Does the pricing model align with your growth trajectory, or will costs escalate unpredictably?
- Operational complexity: Does the platform reduce the skills and effort required to maintain effective security monitoring?
- Coverage breadth: Does the platform provide visibility across endpoint, network, cloud and identity without requiring separate tools?
The SIEM served its generation well. But as threats grow more sophisticated and security teams face persistent resource constraints, the limitations of log-based correlation are increasingly difficult to overcome. The future of security monitoring lies in unified platforms that combine complete telemetry with AI-powered detection, delivering the visibility and accuracy that security teams need without the operational burden that holds them back.
Sources
- NIST, SP 800-92: Guide to Computer Security Log Management, September 2006, accessed 20 September 2026
- NCSC, 10 Steps to Cyber Security: Logging and monitoring, accessed 20 September 2026
- Microsoft Learn, What is Microsoft Sentinel SIEM?, updated 28 January 2026, accessed 20 September 2026
- Microsoft Learn, Plan costs and understand pricing and billing for Microsoft Sentinel, updated 1 April 2026, accessed 20 September 2026
- Microsoft Learn, Automation in Microsoft Sentinel, updated 16 July 2025, accessed 20 September 2026
- Splunk, Splunk pricing models, accessed 20 September 2026
- CardinalOps, State of SIEM Detection Risk, 2025 edition, June 2025, accessed 20 September 2026
- AV-Comparatives, Business Security Test 2024 (March - June), accessed 20 September 2026
Related reading:
- 5 Best SIEM Tools Compared for 2026
- SIEM Alternatives: 7 Platforms That Replace Traditional SIEM
- The Hidden Costs of Your SIEM
- How SenseOn's Cross-Domain Correlation Eliminates False Positives
How SenseOn writes, checks and corrects its content: editorial standards.
