Frustrations Shared By The Cyber Security Community
The FIVE Major Concerns Are:
- Log Ingestion Is Costly and Complex
- Too Many Alerts, Poor Prioritization
- Cross-Environment Correlation Is Fragile
- Detection Often Create Excessive Noise
- Detection Maintenance Requires Scarce Expertise
1. Log Ingestion Is Costly and Complex
Security analytics promises visibility, but that visibility comes at a cost—often a very high one. Ingesting logs from endpoints, networks, cloud platforms, and SaaS applications quickly drives up storage and licensing expenses. On top of that, managing pipelines, parsers, schemas, and retention policies becomes a full-time operational challenge.
Teams constantly debate what data to keep, what to drop, and what they can actually afford to analyze. When budgets tighten, visibility is often the first thing compromised, leaving security teams forced to make uncomfortable trade-offs between cost and coverage.
2. Too Many Alerts, Poor Prioritization
SIEMs and analytics platforms are excellent at generating alerts—but far less effective at telling analysts which ones truly matter. Without strong prioritization, every signal competes for attention. Analysts waste hours investigating low-impact events while genuinely dangerous activity blends into the background.
This overload leads to fatigue, slower response times, and declining trust in the system. When teams feel buried by noise, analytics becomes something to survive rather than rely on.
3. Cross-Environment Correlation Is Fragile
Modern environments span data centers, multiple clouds, and dozens of SaaS platforms, each with its own logging format and behavior. Correlating activity across these domains is fragile at best. Small changes in log structure or timing can break detections entirely.
When correlations fail, investigations stall and blind spots emerge. Security teams know attacks rarely stay in one environment, yet stitching together a complete picture remains painfully difficult.
4. Detections Create Excessive Noise
Out-of-the-box detection rules are generic by design. They rarely reflect how a specific organization operates, what “normal” looks like, or which assets truly matter. As a result, alerts fire constantly on expected behavior. Analysts learn to ignore them, and real threats lose urgency.
Without tuning to business context, detection content creates more distraction than protection.
5. Detection Maintenance Requires Scarce Expertise
Effective detection engineering is a specialized skill—and talent is scarce. Maintaining rules, models, and analytics pipelines requires deep understanding of threats, data, and the business itself. When expertise is limited, detections age quickly. New attack techniques go uncovered, and existing content degrades silently. Teams fall behind not from neglect, but from lack of capacity.
A Question Back to the Community
Do you agree with our analysis of problems and frustrations within the industry?
In Summary
Security analytics frustrations stem from scale, complexity, and human limits. High ingestion costs, alert overload, fragile correlations, generic detections, and scarce expertise all undermine effectiveness. Without careful tuning and sustainable operations, analytics platforms risk becoming expensive noise generators. Success depends on prioritization, context, and investment in detection engineering that aligns analytics with real business risk.