Incident Response Alert Fatigue Is Becoming a Security Threat of Its Own As alert volumes outpace human capacity, organizations are turning to AI, automation, and deeper context to separate real threats from the noise. By Kevin Townsend | June 11, 2026 (9:45 AM ET) Flipboard Reddit Whatsapp Whatsapp Email Alert fatigue and its related effects on SOC efficiency are self-evident problems. Less obvious and more complex are the cause, effect and possible solutions to these problems. SOC analysts are inundated with a huge and continuous volume of alerts generated by security tools. Each alert is often meaningless absent correlation with other alerts. But finding relationships is time-consuming, and even if found, might be irrelevant to business security. Much of the alert volume is simply noise, but attempting correlation to find true positive alerts (signals) from the huge number of false positives (noise) is difficult, boring, and often pointless. The reasons are numerous: Absence of automated prioritization . Security tools are great at detecting alert signals but poor at prioritizing them. Alerts sometimes arrive with a score. “A tool might say, ‘I found a threat. The score is 32 out of 100’,” comments Obbe Knoop, founder and CEO at Lanxit. “What does that mean? What does a score of 100 out of 100 actually mean? Why give it a score of 32? Without context it is meaningless.” Absence of alert context . Alerts suffer from a paucity if not complete lack of context. An alert might suggest the presence of a vulnerability and appear to be urgent; but full context might indicate that this device in that location has no outgoing connectivity and zero relevance to business continuity. It can be noted and queued behind more genuinely urgent alerts. It all depends on having accurate and full context to understand relevance. Jeff Reed, CTO at SentinelOne, summarizes: “Alert fatigue isn’t necessarily the volume of alerts, but rather the relevance of the alerts.” Advertisement. Scroll to continue reading. Criminal use of AI is increasing the pace, sophistication, and stealth of attacks. “Attackers are increasingly using AI to scale their operations – analyzing stolen data faster, generating more convincing phishing campaigns and automating parts of the intrusion process,” adds Reed. The result is continuous growth in the volume of alerts. Defensive use of AI simultaneously increases the attack surface that bad actors can target. “AI systems themselves are also becoming part of the attack surface, introducing new risks around model manipulation, data exposure and misuse – and yet more alerts,” explains Reed. “In short,” he adds, “human analysts simply cannot triage and investigate every signal at the pace modern environments produce them.” This has two effects. Firstly, the pressure is continuous, and the stress level is constant and high. Secondly, there is no escape other than moving to a different job, while the analyst’s personal situation (such as ‘family and high mortgage’) may rule this out. This is a seedbed for burnout. Put simply, the modern SOC analyst is in danger of both alert fatigue (affecting work) and burnout (affecting both work and health); and the business suffers from reduced security. Effects Burnout is not an illness. It is not something that can be cured; it can only be prevented or alleviated. One solution is indeed to change jobs – but then the company loses a highly specialized skill. It is easier to prevent burnout than to alleviate it. This would involve the simultaneous benefit of reducing or preventing alert fatigue. Alert fatigue isn’t caused by occasional long hours and stress – it is caused by continuous long hours and continuous stress with no escape. If it isn’t prevented, the effect on the analyst could begin with a few missed false negatives and grow into a full business compromise. For the analyst, it could start with subconscious, but overly aggressive filtering merely designed to keep up with the volume of fresh alerts. Within this filtering, too many alerts may be assumed to be false positives. Many will be but some may not, and true positive signals may be filtered out as noise. The solution must be a business solution rather than an analyst reaction. The alternative to not keeping up with the volume of new alerts is that the noise generated will continue to grow, and both the cause and effect of alert fatigue will worsen. Alert fatigue can transform an effective security defense into an unseen security threat. It can lead to slower containment, increased dwell time, and a consequent increase in blast radius. Solutions There are two obvious approaches to prevent alert fatigue: reduce the number of alerts by formal filtering to improve the signal to noise ratio, or improve the speed and efficiency of triaging through AI-assisted automation. The problem with the former is the potential to throw out true positives with the noise bathwater; while the problem with the latter is that AI is not yet foolproof. Ariel Parnes, former colonel at IDF 8200 Cyber Unit, and current co-founder and COO at Mitiga, believes the solution to alert fatigue is to increase rather than decrease the alerts, but to more clearly surface and correlate associated alerts for the analysts. The goal is to reconstruct every action, log, and signal into a unified attack sequence, so analysts aren’t triaging individual events but reading a complete, decoded story of attacker behavior. “AI-native automation,” he suggests, “can turn alert floods into clear priorities: automating triage and accelerating investigations so the SOC leads every response rather than chasing it.” Ismael Valenzuela, VP of threat intelligence at Arctic Wolf, agrees with the principle of using automation to give SOC analysts more time on threat investigation rather than continuous and repetitive alert triaging. “Organizations are moving toward more operationalized models that combine automation, correlation, and continuous monitoring to reduce noise, improve prioritization, and give analysts the space to work both sides of that equation.” Reed agrees. “Repetitive tasks such as log analysis, enrichment and early-stage investigation can be handled automatically, allowing analysts to focus on understanding attacker behavior and making strategic decisions. When machines handle the heavy data processing,” he adds, “security teams gain the clarity and time they need to respond effectively.” His solution is to use artificial intelligence to provide automation. “AI is becoming essential for analyzing large volumes of telemetry, correlating signals across multiple environments and identifying the small number of events that actually represent real risk. Rather than presenting analysts with thousands of disconnected alerts, AI can group related activity, add context and prioritize incidents based on likely impact.” Michael Brown, Field CISO at Presidio, adds, “Analysts should not be working on any raw alerts, only correlated incidents. This enables much faster investigations and remediations while reducing staff burnout and attrition.” The question is, ‘How should this be done?’ Not all AI systems are created equal. AI only knows what it knows. It doesn’t know what it hasn’t learned – but it may still fabricate a wrong response. Merlin Gillespie, CTO of Cybanetix, offers one approach. He suggests that using known IoCs as the primary indication of compromise is no longer sufficient. “Over the past few years, attacks have become more subtle. Threat actors now obtain access via stolen credentials and maintain persistence using ‘living off the land’ techniques, which makes detection far more difficult.” So, agreeing with Parnes, he suggests, “This means we need to collect more alerts, not less, to catch and connect those small signs. Capturing more alerts and adopting a paranoid posture means those attacks can be spotted earlier, but it does of course increase the likelihood of alert fatigue and analyst burnout. It’s for this reason we need to let technology do the heavy lifting.” The technology he recommends is a combination of machine learning (ML) and large language models (LLMs). “Together, they can be used to carry out 90% of alert triage and investigation. ML can analyze vast sets of data and identify patterns, anomalies and potential breaches. Over time, ML can even make inferences to anticipate attacks and improve detection,” he says. “LLMs, on the other hand, can explain alerts, investigation findings, and provide case summaries, speeding up investigations and producing intelligible outputs.” But he also warns there are still problems with AI. “The subjective nature means it is also prone to variance. During a recent experiment, we found an agent not only misinterpreted the threat but produced a fictitious killchain. This illustrates,” he says, “that AI doesn’t yet have the maturity needed.” The key seems to be context. Everybody accepts that alert context is necessary for accurate correlation and prioritization, but there is little definition over what constitutes and what provides the necessary context. Valenzuela links it to divergence from normal. “Effective noise reduction requires… understanding which assets are truly at risk and establishing what normal and abnormal look like in their specific environment,” he explains. “Simply adding more tools without that context tends to increase complexity and volume rather than improve outcomes, creating what many describe as an ‘all noise, no signal’ problem.” The priority, he adds, “Is to improve signal quality by enriching alerts with context and continuously adapting detection logic to reflect a changing environment, rather than relying on static rules.” Rob Demain, CEO of e2e-assure, suggests that context can be understood by the analyst after AI has removed the humdrum layer of analysis. “AI removes the repetitive layer of work that consumes so much of an analyst’s day. The result is faster, more cons