SIEM costs are spiralling. According to industry analysts, the average enterprise SIEM deployment costs between £300,000 and £1.2 million annually when accounting for licensing, infrastructure, staffing, and professional services. And those costs are growing, typically by 15-30% per year as data volumes increase and vendors adjust pricing.
Faced with these escalating costs, security teams and their procurement colleagues have developed a repertoire of tactics aimed at reducing the SIEM bill. But do these tactics actually deliver meaningful savings? Or do they simply shift costs elsewhere, often at the expense of security coverage?
We examined the four most common SIEM cost-reduction strategies to assess whether they deliver genuine value or merely defer the inevitable.
Tactic 1: Data Filtering and Exclusion
The Approach
The most common cost-reduction tactic is simply ingesting less data. Security teams identify log sources that generate high volumes but low perceived value and either exclude them entirely or apply filters to reduce the volume that reaches the SIEM.
Common targets for exclusion include verbose application logs, successful authentication events (keeping only failures), routine system health telemetry, DNS query logs, NetFlow or IPFIX data, web proxy access logs for approved categories, and endpoint telemetry for routine operations.
Some organisations deploy log management solutions (such as a syslog server or a data lake) to receive the excluded data, providing a search-only capability without SIEM correlation. Others simply stop collecting the data altogether.
The Real Impact
Cost savings: Moderate to significant. Aggressive data filtering can reduce ingestion volumes by 40-70%, translating directly to lower SIEM licensing costs. For an organisation paying £500,000 annually, this could represent £200,000-£350,000 in savings.
Security impact: Significant and negative. Every log source excluded from the SIEM is a log source that cannot participate in correlation rules, analytics models, or forensic investigations. The excluded data often proves critical when investigating actual incidents:
- DNS query logs are essential for detecting command-and-control communication, data exfiltration via DNS tunnelling, and domain generation algorithm (DGA) activity
- Successful authentication events are necessary for detecting lateral movement, credential abuse, and impossible travel scenarios
- Web proxy logs reveal drive-by download attempts, phishing link clicks, and data exfiltration via cloud services
- NetFlow data provides the network context needed to detect reconnaissance, lateral movement, and unusual data flows
Organisations that aggressively filter SIEM data frequently discover the gaps during incident response, precisely when they can least afford reduced visibility. The cost of a single missed detection can vastly exceed years of SIEM licensing savings.
Verdict: False economy. Data filtering trades security coverage for cost reduction. The savings are real but the security debt accumulates silently until an incident exposes the blind spots.
Tactic 2: Tiered Storage
The Approach
Tiered storage separates SIEM data into "hot" storage (fast, searchable, expensive) and "cold" or "warm" storage (slower, archive-grade, cheaper). Recent data, typically 30 to 90 days, lives in hot storage for real-time correlation and investigation. Older data is migrated to cheaper storage tiers for compliance retention and occasional forensic use.
Some SIEM vendors offer native tiered storage capabilities. Others require organisations to architect their own tiering using external storage solutions (Amazon S3, Azure Blob Storage, or on-premises object storage).
The Real Impact
Cost savings: Moderate. Tiered storage primarily reduces infrastructure costs rather than licensing costs. The savings depend on the ratio of hot to cold data and the price differential between storage tiers. Typically, tiered storage reduces total storage costs by 30-50%, but storage is only one component of the total SIEM bill.
Security impact: Minor to moderate. The primary trade-off is investigation speed. When analysts need to search historical data, which happens regularly during incident response and threat hunting, queries against cold storage are significantly slower. What takes seconds against hot storage may take minutes or hours against cold archives.
There is also a risk of data loss during migration. Tiering workflows that fail silently can create gaps in historical records that are only discovered when the data is needed.
Verdict: Reasonable optimisation. Tiered storage is a legitimate architectural pattern that delivers moderate savings with manageable security trade-offs. However, it addresses infrastructure costs without tackling the more significant licensing and staffing costs.
Tactic 3: Log Volume Reduction Through Source Optimisation
The Approach
Rather than excluding log sources entirely, this tactic involves working with IT teams and application owners to reduce the verbosity of log output at the source. Examples include adjusting firewall logging levels to reduce permitted traffic logs, configuring Active Directory audit policies to log only security-relevant events, tuning web server logging to exclude static asset requests, reducing endpoint telemetry granularity, and deduplicating repetitive events before ingestion.
Some organisations deploy dedicated log pre-processing infrastructure, such as Cribl, Fluentd, or custom ETL pipelines, to transform, filter, and aggregate log data before it reaches the SIEM.
The Real Impact
Cost savings: Moderate. Log optimisation typically reduces volumes by 20-40% without eliminating any log source entirely. The savings are less dramatic than outright exclusion but are achieved with lower security risk.
Security impact: Variable. The impact depends entirely on what is being reduced. Deduplicating identical events and removing truly noise-only data (such as health checks between load balancers and servers) has minimal security impact. But reducing the granularity of endpoint telemetry or consolidating authentication events can eliminate the detail needed for effective detection and investigation.
Pre-processing infrastructure also introduces a new cost and complexity layer. Organisations must invest in log pipeline engineering, which requires specialised skills and creates another system to maintain, monitor, and troubleshoot. The engineering cost of building and maintaining log optimisation pipelines can offset a significant portion of the SIEM savings.
Verdict: Worthwhile but limited. Source-level optimisation is good hygiene that every organisation should practise. However, the savings rarely transform the economics of SIEM. You are improving around the margins of a structurally expensive architecture.
Tactic 4: SIEM Alternatives and Supplements
The Approach
The most radical cost-reduction tactic is moving beyond the SIEM itself, either partially or entirely, with alternative platforms that offer different economics. Options include:
Security data lakes: Platforms like Amazon Security Lake, Snowflake, or Databricks provide log storage and query capabilities at data lake economics (significantly cheaper per GB than SIEM). Security teams use the data lake for storage and search whilst maintaining a smaller SIEM for real-time correlation.
Open-source SIEM: Platforms like Wazuh, Elastic Security (self-managed), and the SIGMA rule ecosystem offer SIEM-like capabilities without licensing fees. The trade-off is the engineering investment required for deployment, tuning, and maintenance.
Unified detection platforms: Platforms like SenseOn move beyond the SIEM entirely with a different architecture entirely: native telemetry collection, AI-powered detection, and consumption-based credit pricing that eliminates the data volume cost model.
The Real Impact
Cost savings: Potentially significant. Security data lakes can reduce storage costs by 5-10x compared to SIEM. Open-source SIEM eliminates licensing costs entirely (though engineering costs may offset much of the saving). Unified detection platforms with consumption-based credit models decouple costs from data volume entirely.
Security impact: Varies dramatically by approach.
Security data lakes provide storage and search but lack real-time correlation, behavioural analytics, and integrated response capabilities. They work well as a SIEM supplement but require significant engineering to function as a replacement.
Open-source SIEM offers comparable detection capabilities but demands substantial engineering investment. Organisations without dedicated SIEM engineering resources often find that the operational burden of self-managed open-source SIEM exceeds the licensing cost of commercial alternatives.
Unified detection platforms represent the most complete alternative, offering native detection capabilities across endpoint, network, and cloud telemetry. The security impact can be positive (better detection accuracy, fewer false positives, broader coverage) if the platform delivers on its promises.
Verdict: The only tactic that changes the equation. Alternatives and supplements are the only approach that changes SIEM economics rather than improving within the existing cost structure. But the choice of alternative matters enormously: the wrong choice trades one set of problems for another.
The Fundamental Problem
All four tactics share a common limitation: they treat SIEM costs as an optimisation problem within the existing paradigm. But the fundamental challenge is the paradigm itself.
SIEM's architecture, collecting logs from disparate tools, normalising them into a common schema, and attempting to correlate across incomplete data, is inherently expensive. The costs are structural, not incidental:
- Data volume pricing penalises full visibility
- Integration engineering is labour-intensive and never complete
- Rule maintenance requires scarce specialist skills
- Alert fatigue wastes the analyst time that justifies the investment
Working within this paradigm yields incremental improvements. Escaping the paradigm yields step-change ones.
A Different Approach Entirely
SenseOn's platform eliminates the cost dynamics that drive SIEM cost-reduction efforts:
Consumption-based credits: SenseOn's Flexible Intelligence Credit (FIC) model means data volume is irrelevant to cost. You commit to an annual credit pool and consume credits by outcome (detection, investigation, compliance, and AI-accelerated resolution) not by gigabytes ingested. Ingest everything across endpoint telemetry, network traffic, cloud workloads, and identity events without per-GB charges. There is no incentive to create blind spots.
No integration engineering: SenseOn collects telemetry through native sensors deployed across the environment. There are no parsers to write, no integrations to maintain, no normalisation issues to troubleshoot.
AI-powered detection: The cross-domain correlation engine replaces manual rule writing with automated detection that adapts to each environment. No rule maintenance, no tuning backlogs, no skills gap.
Reduced staffing requirements: Higher detection accuracy with fewer false positives means fewer analysts are needed to achieve the same security outcomes. The platform delivers actionable cases, not raw alerts.
The result is a different cost structure entirely. Organisations that switch from SIEM to SenseOn typically reduce their total security monitoring costs by 40-60% whilst simultaneously improving detection coverage and accuracy.
Stop tweaking your SIEM costs. Eliminate them.
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