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 SIEM landscape in 2024 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 SIEM towards unified detection platforms.
A Brief History of SIEM
SIEM emerged in the early 2000s from the convergence of two earlier technology categories: 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.
Gartner coined the term SIEM in 2005, and the category quickly became a staple of enterprise security architecture. Early SIEM 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 SIEM provided a centralised solution.
Over time, SIEM's 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 SIEM is expected to be the nerve centre of the security operations centre.
How SIEM Works
At its core, SIEM operates through four fundamental processes:
1. Data Collection
SIEM 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 SIEM's greatest strength and its most significant operational challenge. A full SIEM 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. SIEM platforms normalise 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.
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 SIEM transforms raw data into 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 SIEM 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, SIEM generates alerts for analyst investigation. SIEM also provides dashboarding, reporting, and search capabilities that support both real-time monitoring and forensic investigation.
Alert management is a critical function. Effective SIEM deployments include alert prioritisation, suppression of known false positives, and integration with ticketing systems for case management.
Core SIEM Capabilities
Mature SIEM 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.
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, SIEM has well-documented limitations that have driven significant frustration among security teams.
Data Volume Pricing
Most SIEM vendors price based on data ingestion volume, measured in events per second (EPS) or gigabytes per day. As organisations grow, add cloud workloads, and expand their digital footprint, data volumes increase and costs escalate.
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.
A typical mid-market SIEM deployment costs between £150,000 and £500,000 per year in licensing alone, with costs increasing 15-30% annually as data volumes grow.
Alert Fatigue
SIEM 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.
Industry research consistently shows that SOC teams are overwhelmed by alert volumes. Studies suggest that large enterprises may generate 10,000 or more SIEM alerts per day, of which fewer than 5% represent genuine security incidents. 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 SIEM rule sets effectively. Rules become stale, new threats go undetected because corresponding rules have not been written, and false positive tuning falls behind. The result is a growing gap between the threats the SIEM 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 SIEM deployment project took two to three times longer than planned, and that achieving full coverage of their intended data sources required months of integration engineering.
Skills Requirements
Effective SIEM 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 SIEM engineering talent needed to operate the platform effectively.
The Evolution: Next-Generation SIEM
Recognising these limitations, the SIEM market has evolved. Next-generation SIEM products, including cloud-native platforms like Microsoft Sentinel, Google Chronicle, and various emerging vendors, address some traditional limitations:
Cloud-native architecture eliminates infrastructure management and provides elastic scalability. However, cloud SIEM 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 SIEM products represent genuine improvements, they still operate within the fundamental SIEM 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 SIEM 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:
No data volume pricing: Unified platforms like SenseOn use a consumption-based Flexible Intelligence Credit (FIC) model. Credits are consumed by outcomes such as detection, investigation, and and AI-accelerated resolution, rather than charging by data volume. Organisations can collect complete telemetry without worrying about cost escalation.
Native correlation: Because all telemetry flows into a single platform from native sensors, 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 full 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 SIEM.
SenseOn's Flexible Intelligence Credit model eliminates the data volume tax entirely. Credits are consumed by security outcomes, including detections, investigations, compliance reports, and AI-accelerated resolutions, not by gigabytes ingested. One annual credit commitment covers all capabilities, and the more you commit, the lower the unit rate. Organisations ingest everything, see everything, and pay a predictable, manageable cost.
Making the Transition
For organisations currently invested in SIEM, the transition to a unified platform does not need to be abrupt. Many SenseOn customers begin by deploying the platform alongside their existing SIEM, validating detection coverage, and gradually migrating workloads as confidence builds.
The key evaluation criteria for any SIEM 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?
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.
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