September 2025
Every modern enterprise invests heavily in Security Information and Event Management (SIEM) tools. On paper, the value is clear: centralised log collection, powerful dashboards, and the ability to spot threats across the digital environment.
But in practice? Many SIEM deployments buckle under their own weight. Instead of clarity, leaders often find themselves funding a system that consumes more data than it can use, generates too much noise for analysts to manage, and misses the very threats it was designed to catch.
At the heart of the issue is data overload.
Logs Without Insight
SIEMs are designed to ingest as much data as possible - firewall logs, endpoint alerts, authentication events, application logs, and more. The result is a mountain of raw information, often lacking context. Analysts must dig through endless dashboards, trying to connect small anomalies into a bigger picture.
High Costs, Low Value
Cloud-based SIEM pricing models charge by data volume. Ingesting “everything” becomes prohibitively expensive, forcing many organisations to reduce data feeds. But ingesting less data risks missing the very patterns that indicate compromise.
Information Overload, Analyst Fatigue
Security teams often face thousands of alerts daily. Most are benign, but the signal-to-noise problem leaves analysts overwhelmed and increases the risk of alert fatigue, where genuine threats are overlooked simply because they’re lost in the noise.
Executives may see SIEM as a necessary compliance checkbox - a way to produce audit logs or prove incident reporting capability. But when SIEMs are drowning in data and failing to provide timely, actionable intelligence, the consequences go beyond IT:
Compliance exposure: Regulatory standards demand timely incident reporting. If SIEM can’t spot threats quickly, organisations risk penalties and reputational damage.
Escalating costs: Paying for massive volumes of log ingestion without proportional value is a hidden tax on security budgets.
Operational risk: Missed threats lead to breaches, downtime, and customer impact.
Talent drain: Burnt-out security analysts are harder to retain, and recruitment is costly.
For boards and leadership teams, SIEM inefficiency isn’t a technical inconvenience - it’s a strategic risk to resilience, compliance, and cost control.
The problem isn’t SIEM itself - it’s the quality of the data feeding into it. Logs alone often lack context, but when enriched with network-derived intelligence, SIEM tools become exponentially more effective.
By integrating Gigamon’s deep observability with SIEM, organisations can:
Optimise data before it enters SIEM: Filter out irrelevant traffic and send only the data that matters, lowering ingestion costs dramatically.
Enrich logs with context: Add network-level attributes (such as application metadata and traffic patterns) to transform raw logs into meaningful insights.
Reduce analyst fatigue: Streamline dashboards so analysts focus on a smaller set of higher-value alerts, speeding up detection and response.
Support AIOps and automation: Provide context-rich data that fuels AI-driven workflows, making detection smarter and faster.
This approach turns SIEM from a reactive “log warehouse” into a strategic nerve centre for security operations.
Investing in SIEM is no longer enough. Without the right visibility and context, even the most advanced SIEM platforms risk becoming expensive, noisy, and blind to real threats.
For executives, the lesson is clear: ask not just what your SIEM collects, but how it’s being fuelled. With deep observability enriching and optimising SIEM data, organisations can lower costs, reduce noise, and dramatically improve detection accuracy.
In an age where speed, compliance, and resilience matter more than ever, this shift transforms SIEM from a struggling log manager into a powerful business enabler.