Email security · Understand
Four different problems come through the same door. Spam is measured by volume, phishing by its credibility, malware by its attachment or link, and a compromised account writes from a real mailbox. A reputation-based anti-spam filter mainly targets the first: that is why it is no longer enough, and why a modern filter combines several signals before deciding.
Updated October 20266 minOfficial sources cited
Spam is unsolicited mail sent in bulk. It is a nuisance and wastes time, but it rarely targets a specific victim.
What gives it away: the reputation of the sending server or domain, a domain created a few days ago, advertising content repeated identically to thousands of recipients. A reputation and content filter stops it well, and targeted greylisting of unknown senders weeds out part of the automated sending.
Not to be confused with graymail. A newsletter someone subscribed to or a promotion from a known supplier is not spam. Sorting them separately keeps the mailbox from being flooded without blocking an expected message.
France’s national victim assistance service, cybermalveillance.gouv.fr, describes phishing as a fake message posing as a trusted third party (bank, government agency, supplier, social network) in order to steal usernames, passwords or banking details. Common forms in companies:
Domain authentication is not enough here. A fraudster who buys a lookalike of your domain can configure SPF, DKIM and DMARC perfectly for that domain: the message is authentic… for a domain that is not yours. What gives it away is the resemblance to a known brand or domain, the age of the domain, the reputation of the links and the content.
When phishing targets a specific person to obtain a wire transfer, it becomes CEO fraud: see impersonation and CEO fraud. What SPF, DKIM and DMARC actually stop is detailed in SPF, DKIM and DMARC.
The malicious program arrives in an attachment or behind a link: an Office document asking you to enable macros, a booby-trapped PDF, an HTML file imitating a login page, an archive, an image containing a QR code. The message itself can look ordinary (“your invoice”, “shared document”).
The response focuses on the file and the link: antivirus, inspection of macros and PDFs, reading of QR codes, URL reputation, then checking at the time of click. The guide attachments, links and ransomware details what is examined and what remains a human action.
Here, the message comes from a real mailbox, belonging to a supplier, a customer or a colleague whose password has been stolen. SPF passes, the DKIM signature is valid, the sender is known. A filter that only asks “is the sender unknown or poorly authenticated?” sees nothing.
The signs lie in the behavior:
Two situations to keep apart. When a third party’s account is writing to you, the inbound filter can spot the anomaly. When it is your own account, the issue is first and foremost one for your email system: password, two-factor authentication, sign-in alerts, forwarding rules added by the attacker. It is not the same project as external phishing, even though it often starts with phishing.
| Threat | What gives it away | Main response | What is not enough |
|---|---|---|---|
| Spam | Volume, reputation, recent domain | Reputation and content, targeted greylisting | Deleting by hand |
| Phishing | Imitated brand or domain, suspicious link | Impersonation detection, analysis of links and QR codes | SPF, DKIM and DMARC alone |
| Malware | Attachment or link | File analysis, time-of-click protection | A reputation-based anti-spam filter |
| Compromised account | Change in habits of a known sender | Anomaly detection, then account security | A sender blocklist |
An example, for illustration only. On the same Monday, a small business receives a wave of printer ads, a fake delivery notice with a QR code, an “invoice” in HTML format, and a message from a regular customer’s accountant announcing new bank details. The first three are a matter for the filter. The fourth comes from a real mailbox: the filter can flag the anomaly, but a call to the customer’s already-known phone number is what settles it.
A recent filter does not base its decision on a single criterion. It calculates a score from several signals:
The score leads to a graduated decision: deliver, quarantine, reject, or defer an unknown sender. Quarantine matters as much as rejection: a legitimate message set aside by mistake must be easy to find and release, see Quarantine, false positives and reports.
User reports, “this is spam” or “this is legitimate”, then adjust sender reputation. A filter that learns from its mistakes produces fewer false positives.
Because a filter has to strike a balance between blocking too much and letting things through. A setting that lets nothing through also sets aside expected messages.
No. It proves that the displayed domain authorized the sending, not that the content is honest. See SPF, DKIM and DMARC.
Do not click, do not reply, and report it to the administrator rather than deleting it. Cybermalveillance.gouv.fr also recommends, when in doubt, contacting the organization through a channel you already know.
Alert the email administrator, who will change the password and check two-factor authentication and forwarding rules. This is not a filter setting.
Klytic MTA analyzes each message in layers, before the mailbox, in front of any email system.
User reports feed shared learning across instances, and messages with an ambiguous score are arbitrated by an AI model, in both plans. What this model receives is explained in AI in email filtering.
Limitation: Klytic MTA sees the mail that passes through it. The security of your own accounts (passwords, two-factor authentication, sign-ins) remains the responsibility of your email system.
Consulted in October 2026.
Free 30-day trial, in front of your current email system.
Welcome offer
No-commitment trial. An advisor calls you back to understand your needs and prepare your Klytic workspace.