Alert Fatigue Is a Human Problem: Rethinking Detection Design for Analyst Efficiency

This guest post was contributed by Seth Goldhammer, VP of Product Management, Graylog

The cybersecurity community wages a daily battle not only against adversaries but also against the very systems meant to defend against them. One of the most pressing challenges facing security operations centers (SOCs) today is alert fatigue. Analysts are inundated with endless streams of alerts, many of which are noisy, redundant, or devoid of meaningful context. The result is predictable: real threats are overlooked, human operators burn out, and organizations carry hidden risk despite heavy investments in tools and sensors.

This is not simply a technological problem. It is a human problem. Alert fatigue exposes the mismatch between how detection systems are designed and how analysts process, interpret, and act on information. The overwhelming volume of signals is not just inefficient but actively undermines security outcomes by eroding the trust and focus needed for effective response.

Why Traditional Detection Falls Short

Traditional detection design tends to maximize coverage by generating as many alerts as possible. On paper, this approach looks thorough; in practice, it overwhelms human operators. Rule sets grow endlessly complex, sensors multiply, and alerts trigger without reference to business priorities or asset criticality.

Analysts are left sifting through a flood of signals with little clarity about which ones matter most. Over time, trust in the system erodes, and attention shifts away from what’s important. A cycle emerges where SOCs devote more and more resources to tuning, filtering, and retraining systems, yet the underlying experience for analysts remains frustratingly unchanged.

The paradox is clear: the more alerts are generated; the less effective detection becomes. This excess of unprioritized signals doesn’t just waste analyst time but it also creates opportunity for adversaries. Important threats get buried in the noise, slipping through unnoticed while staff are consumed by lower-value investigations.

Rethinking Detection for Human Efficiency

Instead of endlessly tuning rules or adding more layers of monitoring, security teams can achieve better outcomes by reimagining detection workflows around human cognition and efficiency. This shift begins with recognizing that alerts are meant to invoke human action, which means they cannot be based on a singular event detection. Relying on a single indicator is why we encounter so many false positives. Before we invoke a human, we need:   

Contextualization. Alerts need to carry more than raw, individual threat indicators.

Enrichment requires business context asset sensitivity, and capturing corroborating activities from the asset, e.g. behavior changes, other triggered security events, etc. to give analysts immediate insight into why this alert matters. Without context, an alert is just a blinking light; with context, it becomes actionable intelligence.

Prioritization. Not every signal carries equal weight, and analysts cannot treat them as if they do. Incorporating temporal sequencing, correlations between alerts, and risk scoring helps shift focus from volume to value. Analysts need ranked, risk-informed insights rather than an unstructured list of triggers. By making it easier to distinguish between what is critical and what is routine, prioritization improves both speed and accuracy of response.

Correlation and storytelling. Analysts respond best when alerts tell a coherent story. Clustering related events into incidents reduces noise and frames detection in terms of attacker behavior rather than isolated technical events. Instead of sifting through dozens of alerts that may all be related to the same intrusion attempt, analysts should be able to see the narrative: how the attack unfolded, what systems are affected, and what action is required.

The Cognitive Cost of Noise

From a behavioral science perspective, information overload is not a minor inconvenience. It is a serious barrier to effective decision-making. When analysts are faced with an endless stream of low-value or low-confidence alerts, their ability to concentrate and act decisively diminishes. Each additional alert competes for attention, creating cognitive drag that slows down the entire response process.

More importantly, repeated exposure to low-confidence signals erodes trust in the detection system itself. If analysts consistently find that alerts do not lead to meaningful findings, they begin to doubt the value of any signal produced. This skepticism can delay action when it matters most. In the worst cases, alerts are ignored altogether, not because analysts are careless, but because the system has trained them to expect noise rather than insight.

To address this, organizations need to rethink the principles of alert design. This requires a paradigm shift from alert-based triage to case-based triage. The case-based method is a natural revolutionary step to reduce alert volume by starting with a fuller body of evidence that corroborates successful infiltration.

The case-based method challenges our current understanding of alerts. Instead of an alert triggered from an individual threat indicator, an alert is triggered when a case reaches a significant severity based on the body of evidence. This returns us back to the intended definition of an alert - meant to invoke human action.

This doesn’t mean alerts are not able to stop breaches. Corroborating evidence in a case still is able to detect activity “just right of boom”, early in the attack lifecycle prior to the attacker’s ability to reach their objectives (e.g. data exfiltration, data encryption for ransom, etc.)In short, excess noise carries a cognitive cost that weakens both analyst performance and organizational defense. Reducing that cost requires elevating confidence and trust by automatically instilling context and corroboration. 

From Volume-Centric to Intelligence-Centric

The path forward is not about adding more tools, rules, or dashboards. It is about designing for the human element. Effective detection systems prioritize analyst focus, reduce cognitive drag, and provide clarity in moments of urgency. They do not seek to generate every possible signal but instead deliver the right signals at the right time.

That means fewer alerts but also alerts that are enriched, prioritized, and contextualized. It means moving from a volume-centric philosophy to an intelligence-centric approach, where outcomes matter more than counts. In doing so, organizations can restore analyst efficiency, improve response times, and build more resilient security operations without adding headcount or complexity.

In an era of alert fatigue, less truly is more. By aligning detection design with human cognition, organizations can not only reduce burnout but also strengthen their overall defense posture. Detection should be judged not by how much it produces, but by how much clarity it delivers.

For security teams, the reminder is simple: treat detection not as an exercise in coverage, but as a partnership with human operators. Regularly review the quality of alerts, measure analyst trust in the system, and ensure enrichment and context are embedded in detection logic. Most importantly, resist the temptation to equate more alerts with better security. By designing with people in mind, security teams can move from fatigue to focus, and from noise to intelligence.

About the author Seth Goldhammer, Graylog’s Vice President of Product Management, holds more than 20 years of experience in cybersecurity with a proven track record of driving innovation in the industry. He founded network access control pioneer Roving Planet and held product management leadership roles at TippingPoint, 3Com, and HP. He was the inaugural product manager at LogRhythm and the first executive hire at Spyderbat, a cloud native security startup.