The OpenBait security team. We build anti-phishing detection, takedown automation, and customer-side protection tooling for mid-market brands.
How mid-market security teams should layer detection, takedown, and customer-side protection into one operational flow. Field notes from a year of running this stack against AI-generated phishing campaigns — what saves time, what wastes budget, and where vendors over-promise.
A concrete case from a customer takedown: the same phishing site reported on day zero to both Google Safe Browsing and Tencent's abuse channel. Tencent removed it within 24 hours; Google never responded. What this means for anyone running anti-phishing for a brand exposed to Chinese-hosted infrastructure.
What to do in the first 48 hours after a phishing site impersonating your brand goes live. Scoped to mid-market security teams without a 24/7 SOC. Each step has an owner, an output, and a hard deadline — based on the response runs we operate at OpenBait.
An honest 2026 comparison across the takedown landscape: enterprise platforms (Memcyco, Bolster, ZeroFox, Axur, PhishLabs), agency-style retainers, and self-serve SaaS. Where each model wins, how prices actually shake out, and what mid-market security teams should buy when budgets land under $50K/year.
フィッシング被害を受けた日本企業がテイクダウンを進める際の 3 つの実装パターン（自社対応・代行サービス・SaaS）を、年間コスト・初動時間・監督官庁対応・運用負荷で比較。中堅 300〜2,000 人企業に向けた現実的な意思決定の枠組み。
Google Safe Browsing、Microsoft SmartScreen、DNS 级 blocklist 各自的通报流程与生效时间。在注册商的 abuse 队列还在处理的时间里，直接在浏览器端保护终端用户的并行路径。
绝大多数 abuse 通报都石沉大海。通报"被读"和"被忽略"之间的差别是结构性的：标题分类、可复现证据、reporter 身份信誉。基于 OpenBait 生产环境在用的模板展开解说。
Anti-phishing budget proposals fail because technical value doesn't translate into business language. Framework for structuring the case — expected-loss model, regulatory cost, build-vs-buy ROI, and a one-page executive summary template.
Phishing-site registrations are not uniformly distributed across the 2,500+ ICANN-accredited registrars. A small handful consistently host the majority. Structural reasons — pricing, WHOIS privacy defaults, abuse-response temperature — and what brand owners can do about it.
82.6% of phishing emails in 2026 contain AI-generated content. The legacy detection signals — bad grammar, template matching, suspicious tone — are dead. Here's what works now.
AI-generated attacks, QR-code phishing (quishing), MFA bypass, supply-chain pivots, Deepfake social engineering, and Phishing-as-a-Service. A structured tour of the trends shaping the phishing landscape through 2026 and the countermeasures that actually move the needle.