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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.

  • Web impersonation & lookalike hosts

    Domains and pages that mimic your login, checkout, or support experiences-prioritized by live content and customer impact.

  • Brand misuse in social & marketplaces

    Impostor profiles, scam storefronts, and misleading listings that borrow your name, marks, or creative assets.

  • Mobile abuse surfaces

    Suspicious or misleading app listings that could confuse users alongside your legitimate mobile footprint.

  • Scam campaigns & lures

    Promotions, giveaways, and messaging that funnel victims toward fraud-often crossing email, web, and social.

  • Executive & VIP impersonation

    Personas and channels pretending to be leadership or finance personas to pressure employees or partners.

  • Operational evidence & takedowns

    Repeatable documentation and status tracking for hosts, registrars, and platforms so actions are defensible and auditable.

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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