Around 63% of enterprise security operational alerts go uninvestigated. That statistic should give any security expert pause. The problem isn’t with the attention to alerts or the effort with team training and new tech — modern security tooling simply generates more alerts than any team can meaningfully process, with too little context to know which ones matter most.

Splunk’s 2025 research found that 59% of SOCs suffer from too many alerts, with 55% attributing them to false positives. SANS reframed the issue by showing that the typical organization sees 3,000+ security alerts daily (on average) and two-thirds of SOC teams cannot match that pace.

Having too many security alerts isn’t an issue only for the few on the edge of customer-facing operations. New tools improve detection, but still require human judgment to properly assess, prioritize, and respond. The scaling simply hasn’t caught up to the alerts. There isn’t a shortage of risk signals. There’s a shortage of verifiable, actionable issues.

This piece is part of a series produced ahead of Tech Race Summit 2026 in Warsaw, 10 September, where cybersecurity, AI-driven risk management, and high-load infrastructure will be among the topics on the agenda.

The Cost of Alert Fatigue on Security Performance

In a 2023 controlled experiment, false alarm rates were measured to assess their impact on analyst performance. It found that precision dropped by 47% when the false alarm rate rose to 86%, resulting in a 40% slower response time. More noise from alerts didn’t improve performance. It made analysts slower and less accurate.

Alert fatigue cannot be solved by improving morale or staffing. Any time a system repeatedly sends weak or poorly contextualized alerts, performance degrades. Analysts are spending too much time filtering through the noise. The queue becomes part of the threat model, delaying or missing crucial issues.

The question is what to do when security systems produce so much noise. Why do they produce so much noise in the first place? More attention needs to be paid to what is and what isn’t being flagged.

Why Severity Alone Is No Longer Enough

The traditional “logic” of security systems focused on a severity-first model. The system would assign a severity score, rank findings by technical severity, and then investigate from the top down. That approach is useful but not sufficient when the queue of ranked issues exceeds analysts’ capacity.

The limitation is clear from what the National Vulnerability Database notes: while CVSS measures severity, it does not measure risk. There’s too much room to determine whether the risk is exploitable in a specific environment for a specific asset under current attack conditions. This distinction was reinforced in a 2026 study covering over 280,000 CVEs, which showed CVSS had limited predictive value for real-world exploitation. It was found that a risk-based model incorporating contextual indicators performed significantly better.

With a larger scale of alerts, the security conversation has to evolve. Between 2020 and 2025, the number of published CVEs grew by approximately 263%. Severity by itself cannot match the volume. The environment has shifted, but the need for context is only growing more important.

Rethinking Prioritization with Context-Aware Security

Security operations, cloud security, and application security have all shifted to context-aware prioritization. Regulators, analyst firms, and even major security vendors now build prioritization models around similar factors: exploitability, asset exposure, attack paths, business impact, reachability, and ownership. Adding context, or risk-based prioritization, leads to better team focus on what can truly “move the needle” for risk.

Regulatory bodies are seeing this change. CISA’s Binding Operational Directive 26-04 identified building vulnerability mitigation priorities around asset exposure, KEV status, exploit automation, and post-exploitation impact, rather than focusing solely on CVSS. When the industry, including regulators, moves based on the same logic, it’s a structural response to evolving threats.

Academic research points in the same direction. In a 2025 systematic review of vulnerability prioritization literature, the authors described how the security field is moving from static severity scores toward exploitability built around context and predictive indicators. The conclusion across these groups is increasingly similar: the problem is not only alert volume, but risk prioritization.

What’s less common in the new security environment is the implementation. The logic shouldn’t be implemented as just another security layer, but internally, inside the software delivery process.

Where Context-Aware Prioritization Meets Practice

To meet the evolution of contextual implementation, SOFTSWISS’ Pipeguard was developed. The platform recently won the 2026 Global Tech Award in the Cyber Security Technology category, recognition for a custom-built security platform embedded directly into the software development process. SOFTSWISS developed Pipeguard to solve a common engineering problem: as software delivery accelerates, conventional security tools generate too many low-value alerts, leaving engineers to chase false positives instead of addressing the risks that matter most.

When embedded into software development, Pipeguard identifies and blocks unsafe code, vulnerable software components, exposed credentials, and infrastructure or configuration issues. This implementation puts a barrier to risk before it reaches production, integrating security into delivery rather than a separate stage.

Pipeguard works by evaluating all alerts against a Risk Context Score based on actual business risk rather than severity alone. That shifts predictive security and prioritization from asking how severe a vulnerability appears in theory to understanding how much risk it may pose in a specific environment.

In practice, Pipeguard has reduced irrelevant security alerts by 95% and performed more than 2,000 automated scans daily across 200+ production repositories. Over 90% of critical issues are now resolved within an agreed timeframe. New projects can typically be connected to the platform in just a few hours. Today, Pipeguard supports the SOFTSWISS technology ecosystem used by more than 1,400 brands, while also helping meet compliance and audit requirements under frameworks such as ISO 27001 and PCI DSS.

Evgeny Zaretskov, Group Chief Security Officer at SOFTSWISS, put it another way: “In our job, where release speed is high and trust is critical, security controls have to work inside delivery, not next to it.” That internal philosophy is the guiding idea put into practice.

Exploring Cybersecurity Through Smarter Prioritization and Context

The more software, AI, automation, and other modern tools accelerate delivery and increase alert volume, the less time teams have to remediate exploitable weaknesses. Google’s Threat Horizons H1 2026 report finds the window between vulnerability disclosure and active exploitation has compressed from weeks to only days. If there’s too much noise during that window, requiring manual triage, it opens up structural liabilities.

The goal needs to shift from scanning enough systems or detecting an abundance of vulnerabilities. Modern security teams need a way to filter and focus efforts on genuine risk. Being able to identify which issues deserve the most attention is crucial to acting on them before attackers do.

To further protect a modern organization, security should be embedded in the software delivery process, enriched with context, and able to distinguish genuine risk from excessive noise. It’s not a game of detecting volumes of alerts, but of defining how effectively those risks are given weight so teams can make the right practical decisions.