Fortune 500 Retail and Logistics: Investigating Every Alert at Scale 24/7 with Prophet AI

The Problem

This Fortune 500 logistics and retail company runs operations across thousands of sites, and most of its workforce does not sit at a desk. People sign in from the warehouse floor, from shared machines at a store or counter, and from a different location than the day before, so the identity surface is large and always moving. Operational capacity consistently struggled to keep pace with incoming alert volume. Because the workload required more hours than were available, teams were forced into a common compromise: triage by skimming, bulk-close low-fidelity alerts, and risk missing a real threat buried in the uninvestigated backlog.

That shape shows up in the queue. Much of the suspicious-authentication volume is Microsoft identity activity from a workforce that is supposed to be logging in from everywhere, so whether a sign-in is real takes a genuine investigation, not a quick dismissal. Phishing and commodity malware fill out the rest, arriving faster than a team can work them by hand.

In the first 30 days on Prophet AI, that environment produced 613 alerts across these categories, a pace that annualizes to roughly 7,500 investigations a year. The pressure was heaviest after hours, and not because the company goes quiet then. In an operation that runs around the clock the overnight and weekend window is peak, so about 70% of alerts arrived when the business was busiest and the SOC was thinnest, alerts the team could neither wave off nor leave sitting.

The Solution

A full investigation on every alert 24/7

The company routes alerts to Prophet AI through ServiceNow and lets it investigate autonomously. For each alert, Prophet AI pulls context from across the stack, including Abnormal, CrowdStrike, Proofpoint, Rapid7, Microsoft, and Wiz, and works the way a senior analyst would: forming a line of questioning, gathering evidence across email, endpoint, identity, and cloud, and reasoning to a verdict with a recommended action. A questionable sign-in, for instance, is weighed against the endpoint it came from, the email activity around it, and the user's normal pattern before it is called benign or escalated.

The Results

90% of after-hours alerts investigated autonomously

Because the operation runs overnight and on weekends, the after-hours queue was the team's largest exposure, not an edge case. About 70% of alerts land outside standard staffed hours, and Prophet AI now investigates 90% of them as they arrive, so the team starts each day with the overnight queue already worked rather than waiting to be triaged.

Analysts focused on the work that needs them

At current volume, Prophet AI is on track to absorb roughly 2,500 hours of repetitive investigation a year (projection from observed 30-day volume). That time moves back to higher-risk incidents, detection tuning, and proactive work instead of clearing a backlog.

Every alert investigated to the same depth

Across the first 30 days, Prophet AI fully investigated all 613 alerts spanning phishing, commodity malware, and suspicious authentication, applying the same evidence-backed process to each. Nothing is triaged by skimming, and the audit trail behind every verdict is there for the team to check.

Worked on arrival, not waiting in a queue

Each alert is investigated the moment it lands rather than aging in a backlog. Against a manual baseline of about 20 minutes per investigation, Prophet AI completes one in roughly five minutes, so the queue does not pile up between shifts.