Phishing Report Triage and Response
Employee-reported phishing emails and automated detection alerts both flow into the same SOC queue, and a manual review of every report, checking sender reputation, link destinations, header details and similarity to known campaigns, takes real analyst time per email even though most reports turn out to be low-risk or already-known campaigns. The instinct to speed this up by auto-blocking or auto-deleting anything that resembles a known phishing pattern creates its own serious risk: legitimate mail from a new vendor, a marketing email with an unusual link structure, or an internal email flagged by an overzealous similarity match can get blocked or deleted before anyone notices, which is a real business disruption, not just an SOC inconvenience.
STARTING PRICE
From €299
Standard tier · Multi-step workflow with AI extraction/decisioning and 2-3 integrations.
Get a quote →Saves roughly 8-12 hrs/week of manual phishing report investigation.
How the automation works
We automate the evidence-gathering and initial classification for every reported or detected phishing email, covering sender reputation, link and attachment analysis, header inspection, and similarity scoring against known campaigns, and present analysts with a fully investigated case instead of raw data they'd otherwise pull together themselves. Similarity to a known campaign, even a strong match, is a scoring input that informs priority, not an automatic trigger to block or delete: any action beyond flagging for review, such as blocking a sender domain, quarantining messages fleet-wide, or disabling a compromised account, requires an analyst's explicit approval, because the cost of wrongly blocking legitimate mail at scale is a real business disruption. Confirmed campaigns and their indicators feed back into detection so genuinely known, confirmed threats get handled faster on the next occurrence.
Process flow
- 01
Phishing report or alert received trigger
An employee-reported email or an automated detection alert enters the triage pipeline as it comes in, from any reporting channel.
- 02
Gather investigation evidence integration
Sender reputation, link destinations, attachment analysis and header details are gathered automatically for the reported message.
- 03
Score similarity to known campaigns ai
The message is scored for similarity to known phishing campaigns and prior confirmed incidents, as one input among several, not a standalone verdict.
- 04
Prioritize the analyst queue ai
Reports are ranked by combined risk signals so high-confidence, high-impact threats surface first, ahead of low-risk or already-handled campaign variants.
- 05
Analyst reviews and approves action output
An analyst reviews the gathered evidence and decides on the actual response — block, quarantine, account action or dismiss — with nothing beyond flagging happening automatically, regardless of similarity score.
- 06
Log and update detection output
The analyst's decision and any confirmed campaign indicators are logged and fed back into detection, so genuinely confirmed threats are recognized faster the next time they appear.
Inputs
- Employee-reported phishing emails
- Automated detection alerts
- Sender reputation and link/header data
- Known campaign indicator library
Outputs
- Investigated and evidenced phishing cases
- Prioritized analyst queue
- Analyst action decisions
- Updated campaign detection indicators
Works with
Prefer a fully custom build instead of an off-the-shelf integration? We scope both options during your free consultation — most jobs like this one work fine on standard connectors, but higher-volume or non-standard systems sometimes need bespoke API work, reflected in the complex tier.
Where this goes wrong if you get it wrong
- Auto-blocking or auto-deleting a message based on similarity to a known campaign alone creates real false-positive risk against legitimate mail — a new vendor's first email, an unusual but real marketing link, or an internal message with an atypical structure can all score similar to a known campaign without being one, and blocking those at scale is a genuine business disruption.
- Fleet-wide quarantine or sender-domain blocking based on one report needs to stay a human-approved action, not an automated response to a high similarity score, because the blast radius of a wrong block is every employee who was going to receive legitimate mail from that sender, not just the one reported message.
- A prioritization model that ranks purely by similarity to past campaigns can under-prioritize a genuinely novel phishing technique that doesn't resemble anything seen before — the queue ranking needs to weight in signals beyond historical pattern match, like anomalous sender behavior or unusual link structure, so novel threats don't sit at the bottom.
- Evidence gathering that only checks the reported message and not related messages from the same sender or campaign misses the actual scope of an incident — a single flagged email is often one of many, and the investigation needs to surface related messages so the analyst's action, once approved, addresses the real scope, not just the one report.
Frequently asked questions
Does this automatically block or delete phishing emails?
No — it gathers evidence and prioritizes reports for analyst review, but any action beyond flagging, including blocking a sender or quarantining messages, requires an analyst's explicit approval, precisely because auto-blocking on pattern similarity alone risks catching legitimate mail.
How does this avoid false positives against legitimate mail?
Similarity to a known campaign is one scoring input among several used for prioritization, not an automatic trigger — a new vendor's first email or an unusual but legitimate link can score similarly to a phishing pattern without being one, so it stays a human decision to act.
What happens to reports that turn out to be low risk?
They're still logged with their evidence and analyst disposition, they just rank lower in the queue so the analyst's time goes to higher-confidence, higher-impact reports first, not to the exclusion of ever being reviewed.
Does this get faster at catching repeat campaigns?
Yes — confirmed campaign indicators from analyst-approved decisions feed back into detection, so a genuinely confirmed, previously-seen campaign is recognized and prioritized faster on its next occurrence.