Spring clean your security data: The case for cybersecurity data hygiene

Spring cleaning isn’t just for your closets; security teams should take the same approach to their security operations data, where years of unchecked log growth have created a bloated, inefficient and costly mess.

The modern Security Operations Center (SOC) is drowning in security telemetry from endpoints, cloud, SaaS applications, identity platforms and a growing list of other sources. In practice, most of these are redundant, irrelevant, or just outright noise, and are affecting detection effectiveness, operational efficiency, and the ability to extract real insights.

Poor data hygiene isn’t just an annoyance; it actively degrades security operation capabilities and readiness. Over-retention of low-value telemetry inflates SIEM and XDR costs while slowing down detection and response. It increases alert fatigue, making real threats harder to spot. Worst of all, a cluttered SIEM means analysts spend more time sifting through junk than responding to incidents.

We need to move beyond the outdated mentality of indiscriminate data hoarding. If security teams don’t proactively manage this data sprawl, they risk falling into the same trap that has plagued SIEMs for decades: collecting everything, finding nothing, and overpaying for the privilege.

Instead, security teams should focus on curation, contextualization, and value efficiency and forward only what matters when it matters, enriching it effectively, and storing everything where it makes the most sense.

Five steps to spring clean your security data

1. Eliminate manual rule tuning

Traditional SIEMs require constant rule tuning, another tarpit that overwhelms small security teams and leads them astray with mission creep. Instead, lean-forward teams should be leveraging a variety of techniques – including machine learning, vector analysis, knowledge graphs and LLMs – to automate event transformation, refinement, and prioritization.

Instead of manually tuning rules for detections that become outdated the moment an attacker changes tactics, modern security operations need more dynamic and adaptable data processing workflows.

Security data isn’t static, so your rules can’t be either. AI-driven approaches can analyze patterns across datasets rather than relying on brittle, manually curated rules that act on alerts individually and atomically.

2. Reduce SIEM storage costs without sacrificing security

SIEM pricing models remain broken. Most charge based on ingestion volume, not on any inherent security value of the data. This incentivizes overcollection, forcing security teams to waste budget on redundant logs that never really contribute to real detection and response.

A smarter approach:

• Use a tiered storage strategy, keeping high-fidelity logs in real-time analytics while archiving bulk telemetry in cost-effective object storage.
• Offloads non-critical data to security data lakes, allowing for retroactive analysis without incurring real-time SIEM costs.
• Deduplicate and preprocess logs before ingestion, cutting storage waste while preserving analytical depth.

Security operations shouldn’t be paying premium SIEM pricing for raw logs that never generate alerts or value.

3. Prioritize high-fidelity data over raw volume

Security teams don’t suffer from a lack of data; they suffer from too much of the wrong data. So more data is rarely the answer; better data is. SIEM vendors have long pitched “collect everything” strategies, but this has led to diminishing returns without ways to sort the wheat from the chaff. And the more irrelevant logs you store, the harder it becomes to find meaningful signals in the noise.

But this also doesn’t mean simply discarding “low value” logs. Security data isn’t inherently good or bad. It’s about how effectively you extract insights from it. And just like any new intensive means of extraction increases efficiency and opens new use cases, new technological approaches have also created opportunities for better value extraction of security logs.

• Instead of treating logs as isolated events, security teams should use analytics designed for scale.
• Instead of relying on predefined correlation rules, organizations should mine security data dynamically, identifying trends across vast datasets.
• Instead of dumping everything into a SIEM, telemetry should be preprocessed, enriched, and prioritized before entering downstream tools and analytics.

Even traditionally “low value” data like DNS logs or seemingly harmless authentication attempts can surface critical threats if analyzed correctly. The problem isn’t too much data; it’s the lack of automated, large-scale analysis that can extract the right patterns in real time.

4. Enable context-rich investigations with explainability and ontology models

Alerts without context slow down security teams. Every detection needs to answer three key questions:

1. Is this real? (Is this a true positive?)
2. Does it matter? (How critical is this event?)
3. What’s next? (What should we do about it?)

Without automated enrichment, analysts are left digging through raw logs manually, trying to piece together fragmented details. By mapping security data to ontology-based models and frameworks like MITRE ATT&CK, or adding external threat and internal user and asset context, teams gain deeper investigative context without additional manual effort. More crucially, as security operations slowly become more autonomous, contextual enrichment also helps inform automations, whether logic-based or via AI agents.

Instead of just saying “Failed login attempt from unusual location,” a properly structured security refinement workflow should surface broader attack narratives:

• Was this login attempt similar to other observed reconnaissance behavior?
• Did it involve other compromised user accounts with high privileges?
• Was there anomalous MFA bypass activity around the same time?

Context matters. Security data is only valuable if it helps analysts and machines make better decisions faster.

5. Stop DIYing security data management

For years, security teams had little choice but to repurpose log management tools, custom scripts, and DIY approaches to make sense of security telemetry. But today, a growing market of tools designed specifically for security data engineering is emerging.

• Security telemetry pipelines help clean, enrich, and route logs before they ever hit a SIEM or XDR.
• Schema-on-read architectures (common in security data lakes) allow security teams to analyze data on-demand rather than pre-filtering everything before ingestion.
• SOCless models are enabling new ways of handling detection and response without relying on monolithic SIEM deployments.

Security teams no longer need to fight their tools to get the right data at the right time. The key is to invest in modern security data pipelines that prioritize efficiency, enrichment, and real-time analytics without the traditional repurposing tax.

Security data should work for you

Data hygiene is about ensuring that security teams can detect real threats without drowning in irrelevant telemetry and must be part of any viable and effective security data strategy.

Organizations that continue treating security data as a “collect everything” problem will spend more, detect less, and burn out their SOC teams in the process. Those that prioritize analytics at scale, automate enrichment, and focus on high-fidelity security signals will have an advantage, not just in cost savings but in faster, more accurate threat detection.