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PhishEye

Automated takedowns

This is the automation engine story-not the brand strategy page, and not the managed services page.

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Concept view of monitoring and response workflows in PhishEye - replace with a product screenshot when available.

Coverage areas

Domains, social, app stores (scoped to your program)

Delivery

Platform workflows + optional managed services

Outputs

Prioritized queues, evidence, takedown tracking

Coverage

Threat patterns programs typically monitor

Programs are tuned to your marks and channels; the list below reflects common categories teams prioritize.

  • What automation handles well

    High-volume, pattern-stable abuse: cookie-cutter phishing kits, cloned login templates, and lookalike retail domains.

  • What stays in analyst hands

    Novel legal theories, slow jurisdictions, and providers that require custom narrative — surfaced, not hidden in a black box.

  • Evidence bundling at the point of detection

    Each case is born with its captures, timestamps, and identifiers — not assembled by hand at submission time.

  • Queue routing and stall reminders

    Cases route to the right channel automatically; reminders fire when a provider thread sits past its expected response window.

  • Recycle detection on closed cases

    A new host hitting the same kit fingerprint reopens the original case instead of creating a fresh untriaged ticket.

  • Throughput and time-to-suspend reporting

    Leadership metrics that follow takedown work end to end — median time, recycle rate, and analyst hours saved.

What to automate first

High-volume, pattern-stable abuse: lookalike retail domains, cookie-cutter phishing kits, and cloned login templates with predictable evidence.

Illustration: repeatable patterns and signals that machines handle well at high volume.

What stays manual

Novel legal theories, slow jurisdictions, and providers that demand custom letters. Automation should surface these-not hide them in a black box.

Illustration: edge cases and exceptions that still need analyst judgment.

Operational reporting

Throughput, recycling rate, and median time-to-suspend become leadership metrics alongside raw detection counts.

Illustration: pipeline view from queue through closure to leadership-ready reporting.

Protect revenue and customer trust

See how PhishEye centralizes detections, evidence, and takedowns so security, fraud, and brand teams share one operational picture.

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FAQs

Common questions

What should be automated first?
Repeatable abuse with predictable evidence packages-commodity phishing kits, cloned login pages, and serial lookalike shops-so analysts focus on novel cases.
What should not be fully automated?
Novel legal arguments, sympathetic jurisdictions, and incidents with executive visibility. The system should flag these for human judgment.
How do we measure automation success?
Track throughput, recycle rates, median time-to-suspend, and analyst hours saved. Pair quantitative metrics with spot audits of false positives.
Can analysts pause, override, or roll back automated steps?
Mature setups keep humans in the loop: exceptions queues, policy gates for sensitive marks, and audit trails for what was submitted and when. Automation should accelerate routine paths, not remove accountability.

Explore further

Related pages

Ready to scope a program for your marks and channels?

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