Every reported email, investigated for you: confirmed threats gone in minutes

According to research from Omdia and Microsoft, 42% of alerts go completely uninvestigated, especially outside of working hours when threat queues go untouched overnight. The gap between what gets flagged and what gets investigated is where security incidents occur. Mimecast Managed Threat Response closes that gap, offering a modernized 24x7 service that handles the user reported email queue for you.

The problem: a queue that grows faster than headcount

User-reported phishing sits squarely inside that problematic gap where risk lies. As security behavior management programs mature and employees get better at spotting suspicious email, the volume of reported emails climbs. That's the intended outcome ꟷ more eyes and more reporting ꟷ but it creates a second-order problem: an abuse mailbox that fills faster than any SOC can triage it manually, and more hiring alone won't close that gap.

Most teams respond the way they respond to any queue they can't clear: they deprioritize it. Reports get batched to business hours, handed to junior analysts, or reviewed only when someone escalates. Most SOCs still lack a coherent strategy for managing the data flowing into their tools, and industry research shows over half of security teams describe themselves as overwhelmed by alert volume, not under-resourced in some abstract sense, just structurally outpaced.

That imbalance is made worse by the fact that most user-reported queues involve no automated enrichment: no sender reputation check, no cross-tenant correlation, no similarity matching against a known campaign. Every report requires a human to start from zero. Volume goes up. Context stays flat. The result is a queue that's largely benign submissions mixed with a small number of real threats, and no reliable way to tell them apart at the speed the queue demands.

What good defense looks like

The fix isn't choosing between automation and analysts, it's sequencing them correctly. Detection, triage, and remediation are three distinct jobs, and treating them as one collapses under volume. AI is well suited to the first two: classifying a reported email, matching it against known campaign patterns, and surfacing the small percentage that warrants human judgment. What AI shouldn't be left to do alone is remediation, deciding to block a sender, or update detection logic based on the confirmed outcome of an algorithm. In Torq's survey on AI security and trust, 34% of SOC teams cited excessive human review as their top complaint with AI triage, while unreliable conclusions were the top complaint for AI-driven investigation. The teams closing the investigation gap are the ones building that division of labor deliberately: automation absorbs the volume, analysts own the judgment calls, and the loop between "we saw this threat" and "our detection logic now knows about it" stays tight.