---
title: "Comparing Different AI Approaches to Email Security | DuoCircle"
description: "A comparison of different AI-based email security approaches is vital to developing more efficient security strategies in the face of evolving cyber threats."
image: "https://www.duocircle.com/images/og-default.png"
canonical: "https://www.duocircle.com/blog/email-security/comparing-different-ai-approaches-to-email-security/"
---

Quick Answer

AI in email security splits into two categories with different limitations. Backward-looking signature-based approaches train on historical malicious mail, recognize known patterns, and block what matches. They were workable when attack lifespans were long, but they cannot detect novel threats by definition. The 'fearware' wave during COVID-19, with new domains and new phishing themes, broke signature-based filters because the topics had never been seen. Forward-looking unsupervised machine learning analyzes real-world traffic in an organization to build a baseline of normal behavior, then flags deviations. It catches novel attacks because it doesn't rely on prior examples, and it works across languages because the signal is behavioral, not lexical. A partial middle path is intent analysis (grammar, tone, solicitation patterns), which catches inducement-style phishing but misses behavioral anomalies. The working architecture combines all three: signatures for known threats, intent analysis for language patterns, and unsupervised ML for novel and anomalous traffic. Pure signature-based filtering is no longer sufficient against modern attack rates.

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![Email Security](https://media.mailhop.org/duocircle/images/2023/07/dkim-selector-7444.jpg) 

_A comparison of different **AI-based** email security approaches is vital to developing more efficient security strategies in the face of evolving cyber [threats](/email-security/emerging-email-security-threats-in-2023/)._

The [email security](/content/email-security-services/types-of-email-security) landscape has significantly transformed in recent years with the **integration of AI** in email security. Cybersecurity experts have been leveraging innovations in AI to strengthen their line of defense against malicious players.

However, not all AI approaches are equally effective against online threats. Therefore, organizations and enterprises must understand the shortcomings and adopt the **right AI approach** for email security.

Below is a closer look at various approaches and their potential limitations. The information will help organizations incorporate a layered [AI cybersecurity](https://www.computer.org/publications/tech-news/trends/the-use-of-artificial-intelligence-in-cybersecurity) strategy to **combat online malicious actors** targeting email security.

## The Backward-Looking Signature-Based Approach

For decades, [cybersecurity](/) technologies have mainly been dependent on signature-based approaches. These approaches helped organizations identify known threats and neutralize them. The signature method fed a machine thousands of emails already classified as malicious to **spot patterns** to indicate future attacks.

_This approach was **practical earlier** when attack lifespans used to be longer._ The signature-based method largely depended on historical data. This reactive approach is no longer effective against modern, rapidly evolving [cyberattacks](https://ethiopianmonitor.com/2023/07/24/insa-foils-over-6700-cyberattack-attempts/). A fundamental limitation is its **inability to prevent** novel email threats that it never encountered before, which is crucial in a rapidly changing cyber threat landscape.

## The Rise of Fearware: A Challenge to Signature-based Approaches

_The emergence of “fearware” during the COVID-19 pandemic exemplifies the **limitations** of signature-based approaches._

Threat actors managed to exploit **global fear and uncertainty**. They crafted targeted emails related to COVID-19 containing [malware](/resources/malware-and-its-defense-mechanism) or credential-grabbing links. The topics and domains of these attacks were new and never seen before. Thus, signature-based systems failed to detect and prevent these threats.

[![malware](https://media.mailhop.org/duocircle/images/2023/07/SPF-record-checker-7443.jpg)](https://media.mailhop.org/duocircle/images/2023/07/SPF-record-checker-7443.jpg)

### A Partial Solution, Spotting Intent

To address the limitations of historical approaches, new [Artificial intelligence](/email-security/how-artificial-intelligence-approaches-are-changing-the-email-security-landscape/) in email security strategies developed a mechanism to evaluate factors such as the **grammar and tone of emails**. Thus, AI managed to identify discrepancies in communication languages and filter out malicious emails.

The system trained extensive datasets collected over time to recognize the intention behind certain types of emails. Most of these [malicious emails](https://www.computerweekly.com/news/252514025/BBC-blasted-with-millions-of-malicious-emails) involved inducement or solicitation. However, this AI approach for email security **failed to determine anomalous emails**. Therefore, cybersecurity experts are using this strategy combined with other methods to mitigate email threats.

## The Forward-Looking Unsupervised Machine Learning Approach

The forward-looking AI based email security approach involves **unsupervised Machine Learning (ML)**. This robust security mechanism analyzes the real-world data in an organization to establish a notion of ‘normal.’

Based on this foundation, the system identifies **subtle deviations** that indicate an attack. That’s different from [signature-based](https://dtgreviews.com/uncategorised/the-evolution-of-intrusion-detection-from-signature-based-to-ai-driven/74706/) systems, and the approach isn’t reliant on historical records. _Thus, this AI approach for email security can detect novel and unexpected threats._

Unsupervised Machine Learning involves a more comprehensive analysis of emails. The system extracts thousands of data points from each email and observes its context within the organization’s communication network. This AI approach helps gain a **continuously evolving** understanding of the organization’s “pattern of life”. Thus, it effectively spots anomalies that can point to possible [novel attacks](https://threatpost.com/attack-amazon-devices-against-themselves/178797/).

Regardless of the language used in communications, this method proves **highly effective**. Therefore, cybersecurity experts combine this approach with [intent analysis](https://docs-previous.pega.com/sites/default/files/help%5Fv73/rule-/rule-nlp-/rule-nlp-predictivemodel/nlp-intent-analysis-con.htm) to develop a more robust and holistic email security solution.

### A Layered AI Approach for Comprehensive Email Security

A layered approach to using AI in email security is the need of the hour. This approach effectively combines the benefits of both backward- and forward-looking methods. Rather than relying solely on historical data, the system detects an **abnormality in the usual email traffic** in an organization.

[![AI in cybersecurity](https://media.mailhop.org/duocircle/images/2023/07/email-migration-service.jpg)](https://media.mailhop.org/duocircle/images/2023/07/email-migration-service.jpg)

However, it might take years of research to effectively place the layered AI approach to ensure optimal email security. The approach combines intent analysis and [unsupervised Machine Learning](https://www.ibm.com/topics/unsupervised-learning) to detect unique novel emails on their first encounter. Thus, your organization remains secure against **emerging and existing threats** through emails.

## Final Words

As AI-based email security technologies evolve, so do the techniques and [attack vectors](https://www.helpnetsecurity.com/2023/02/24/malicious-attack-vectors/) employed by malicious actors. Hence, there is a neck-in-neck competition between the two. At such a juncture, the right combination of **AI and human insight** would be the perfect solution for email security in the future.

## Topics

email securityNewsTrendsUpdates 

![Brad Slavin](https://media.mailhop.org/dmarcreport/images/team/brad-slavin.jpg) 

Brad Slavin 

General Manager

General Manager at DuoCircle. Product strategy and commercial lead across the email security portfolio.

## Secure your email infrastructure

Protect, authenticate, and deliver. Contact our team to find the right solution.

[Contact Sales](/contact/) [Explore Products](/products/) 

## Related Articles

[  Email Security 7m  10 Crucial Tips that Will Help You Avoid Spam Filters and Send Better Emails  Feb 14, 2023 ](/blog/email-security/10-crucial-tips-that-will-help-you-avoid-spam-filters-and-send-better-emails/)[  Email Security 6m  5 Reasons Why Your Website Needs an SPF Record Flattener?  Sep 26, 2023 ](/blog/email-security/5-reasons-why-your-website-needs-an-spf-record-flattener/)[  Email Security 6m  Decoding Canonicalization: The Reason Behind DKIM Signature Verification Failures  May 2, 2024 ](/blog/email-security/canonicalization-reason-behind-dkim-signature-verification-failures/)[  Email Security 18m  Data Privacy And Protection: 11 Ways To Protect User Data  Jul 28, 2022 ](/blog/email-security/data-privacy-and-protection-11-ways-to-protect-user-data/)

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