Five attack types account for almost all the financial damage done through email, and they defeat different controls. A sandbox that detonates every attachment is useless against a wire fraud request typed by hand from a supplier's real mailbox. A behavioural model catches that easily and does nothing about a QR code buried in an image. So platforms get chosen on aggregate catch rates, then fail on the one threat the organisation was actually worried about.
This comparison is organised by threat instead. We took the five categories the FBI's 2025 Internet Crime Report shows doing the most damage, worked out what detecting each requires, then assessed eight platforms against them. Behind that selection: business email compromise caused $3.05 billion in losses across 24,768 complaints, account takeover appeared as its own category for the first time at $359.7 million, and AI-related complaints entered the report for the first time with $893 million attached.
AI-generated phishing and deepfake fraud. Grammatically perfect messages at scale, sometimes reinforced by cloned audio in a follow-up call. Language quality analysis is dead, because the language is now correct. Detection rests on relationship and behaviour. Has this sender ever emailed this recipient? Does the request match their pattern?
Business email compromise. A payment change request from a supplier whose mailbox has genuinely been compromised, so it passes SPF, DKIM and DMARC legitimately. Detecting it needs a behavioural model of your external counterparties, not just your employees.
Account takeover. An attacker inside a real mailbox using valid credentials. Detection means monitoring the account rather than the message: login geography and timing, new forwarding rules, unusual send volume, recipients that identity has never contacted. Inbound filtering contributes nothing here.
Ransomware and malicious attachments. Still the most common route to encryption, with Check Point putting the share arriving via email above 75%. This needs static and dynamic analysis of files and links, plus detonation of whatever sits behind a QR code. It's the most mature capability here, and where serious platforms differ least.
Lateral phishing. Mail from a compromised colleague to their own colleagues. It never crosses the perimeter, so a gateway never sees it, and it converts far better than external phishing for the obvious reason: the sender really is who they say they are. Detecting it needs API visibility of internal mail.
| Platform | AI phishing | BEC | Takeover | Malware | Internal | Pre-delivery |
| Check Point | Strong | Strong | Strong | Strong | Strong | Yes |
| Abnormal AI | Strong | Strong | Strong | Moderate | Strong | No |
| Sublime Security | Strong | Strong | Moderate | Moderate | Strong | No |
| Proofpoint | Strong | Strong | Strong | Strong | Moderate | Gateway only |
| Darktrace | Strong | Moderate | Strong | Moderate | Strong | No |
| Microsoft Defender | Moderate | Moderate | Moderate | Strong | Moderate | Yes, native |
| IRONSCALES | Moderate | Moderate | Moderate | Moderate | Strong | No |
| Mimecast | Moderate | Moderate | Moderate | Strong | Moderate | Gateway only |
Best for: Organisations that treat any period of user exposure as unacceptable, and want internal and outbound mail covered by the same engine.
Across all five categories Check Point holds one advantage the others can't replicate. It inspects mail inline through the API and holds it before delivery, so a message that shouldn't exist in the mailbox never does. That matters most on AI-generated phishing and BEC, where the attack succeeds the moment a human reads it and believes it.
Threat by threat: detection draws on ThreatCloud AI, and the published figures cite a 99.2% reduction in phishing reaching the inbox, with historical email analysis identifying impersonation to stop BEC. For account takeover, an event engine monitors more than 100 indicators including login location and time online. For malware, multi-layer AI quarantines files before the inbox. Internal and outgoing mail is scanned too, across Teams, Slack, OneDrive and SharePoint.
The catch: No published per-seat price, so budget planning starts with a sales conversation. The architecture depends on cloud mailboxes, and reviewers confirm there is no support for on-premises Exchange. Some also report limited coverage of SaaS platforms beyond the major suites, and legitimate bulk senders being quarantined.
Founded 2018 · Venture backed, $546m raised at a $5.1bn valuation · Roughly $3 to $8 per user per month
Best for: Organisations whose largest exposure is a fraudulent payment.
The most credible pure play on the two threats that carry no payload. It baselines each identity from tens of thousands of behavioural signals and flags deviation, correlating login and mail behaviour for account takeover rather than treating them separately. When it blocks a supplier's banking change request, it names the signals that deviated, turning a security decision into a business conversation finance will accept.
The catch: Remediation is post-delivery, so there's a window between arrival and removal. Malware analysis isn't the focus, which is why most deployments keep Microsoft or a gateway underneath.
Founded 2019 · Venture backed, $240m+ raised · Free tier, then pricing on quote
Best for: Teams with detection engineering capability facing targeted campaigns generic models miss.
The only platform where you can answer a novel threat by writing the detection yourself, the same day. Rules use an open, readable language with a public community rule set, and inbound, outbound and internal mail are all covered. When a campaign hits your sector, you read why a message did or didn't match, adjust the logic and ship it, instead of waiting on a vendor model update.
The catch: Account takeover detection is less developed than at the behavioural specialists, malware analysis isn't a strength, and the model assumes somebody on your team wants this work.
Founded 2002 · Private equity owned (Thoma Bravo, 2021) · Pricing on quote
Best for: Large enterprises wanting one platform across threat, data loss and archiving.
Proofpoint's advantage is intelligence scale, which matters most where seeing an attack elsewhere first is decisive. Taken private by Thoma Bravo in 2021 for $12.3 billion, with roughly 25 acquisitions behind it including Hornetsecurity for $1.8 billion in December 2025. It deploys as a gateway or through API, so pre-delivery blocking is available.
The catch: Complexity and tuning burden are the highest here, and professional services are usually part of the deal. Two overlapping SMB lines now sit under one owner, integration still in progress.
Founded 2013 · Private equity owned (Thoma Bravo, 2024) · Pricing on quote
Best for: Enterprises already running Darktrace that want lateral movement correlated across the estate.
Strongest on threats that only surface when email is read alongside network and identity behaviour. Unsupervised machine learning builds its own model of normal per user, no rules or signatures. An anomalous login, an unusual internal email and unexpected outbound traffic from one identity in the same hour make a far stronger case than any of the three alone.
The catch: Unsupervised models are the least explainable here, and analysts typically need weeks before they trust them. Bought as email-only the value case thins considerably, and it doesn't block pre-delivery.
Founded 1975 · Publicly listed (NASDAQ: MSFT) · $2 (Plan 1) or $5 (Plan 2) per user per month
Best for: Microsoft 365 tenants wanting native malware and phishing protection as a foundation layer.
Genuinely capable against commodity threats. Plan 2 adds Threat Explorer, Automated Investigation and Response and XDR correlation with Defender for Endpoint. Attachment and URL detonation is strong, though account takeover detection leans on Entra ID signals rather than mail behaviour. Microsoft's positioning now emphasises agentic response and deepfake patterns.
The catch: Because deployment is universal, campaigns get tested against it before they're sent, which structurally caps efficacy against targeted attacks. Payload-free BEC is where Defender on its own most often fails.
Founded 2014 · Venture backed (Series C, led by PSG) · Pricing on quote
Best for: Mid-market teams wanting the first user report to trigger tenant-wide removal.
Built around the threat pattern where speed matters more than sophistication: a campaign hitting many mailboxes at once. Similar reports are clustered so a confirmed threat is remediated everywhere it landed, and simulation shares the same console.
The catch: Reviewers rate it more strongly on workflow and simulation than on novel AI-generated threats, and it doesn't block before delivery. Send a targeted attack to three executives and nobody reports it, so the model's core advantage doesn't apply.
Founded 2003 · Private equity owned (Permira, 2022) · Pricing on quote
Best for: Regulated organisations where an email outage is a compliance and trading problem.
Mimecast addresses a category the AI-native platforms ignore entirely: risk to mail availability. Gateway deployment gives pre-delivery blocking, API deployment arrived in March 2026, and attachment protection is mature. When Exchange Online goes down, Mimecast customers keep sending and receiving. Treating availability as a threat surface is unusual, and for some buyers it decides the deal.
The catch: Detection of payload-free social engineering trails the specialists, and tuning requirements run higher. Four acquisitions in two years have left a less uniform console.
We began with the fourteen vendors in Gartner's December 2025 Magic Quadrant and assessed eight. Rather than scoring overall efficacy, we defined five threat categories from the FBI IC3 2025 loss data, established what detecting each requires, and rated platforms against them. Ratings draw on vendor documentation, analyst positioning, verified reviews on Gartner Peer Insights and PeerSpot, and disclosed architecture, not independent lab testing. Architecture was weighted most heavily, because it decides which threats a platform can act on at all. A post-delivery platform can't prevent a click. A gateway can't see internal mail.
Prioritise platforms that assess relationships and behaviour rather than language quality, since AI-generated mail is grammatically clean. Check Point, Abnormal AI and Sublime all detect it well. The deciding question is whether you want the message held before delivery or removed after.
The stronger platforms do, by monitoring the account rather than the message: login location and timing, new forwarding rules, unusual send volume and internal recipients that identity has never contacted. Check Point monitors more than 100 such event indicators. Inbound filtering detects none of it.
Check Point puts it above 75%, which is why attachment and URL analysis remains a core requirement even as attention shifts to payload-free social engineering.
Which of the five would hurt you most? Answer that, then pick the architecture that can act on it. If you run two platforms, make sure they fail differently: stack two reputation-based filters and you've bought the same blind spot twice. For most organisations that means prevention before delivery plus behavioural coverage of internal and outbound mail, which makes Check Point Email Security the logical first platform to test, with Abnormal AI beside it if payment fraud is your dominant exposure. Run both on your own mail for four weeks and judge them on the attacks one caught and the other missed.
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