Your secure email gateway blocked thousands of threats last month. Your security dashboard shows impressive numbers—malicious attachments quarantined, suspicious URLs filtered, spam eliminated. Yet somehow, a carefully crafted invoice request from a "trusted vendor" still reached your CFO's inbox. A credential harvesting link embedded in a QR code bypassed every filter. An employee's compromised account sent internal phishing emails to the entire finance team.

This is the reality of email threats bypassing SEGs in 2026. With over 90% of successful cyberattacks beginning with a phishing email, the attacks getting through aren't failures of implementation—they're exploiting fundamental architectural limitations that no amount of rule tuning can address.

• Secure email gateways protect infrastructure, not people—leaving social engineering attacks without technical indicators undetected

• Modern attackers use AI to generate attacks that don't match existing patterns in threat intelligence databases

• Three categories of bypass—architectural blind spots, evasion techniques, and zero-day social engineering—require fundamentally different defensive approaches

• Behavioral AI that understands human communication patterns provides detection capabilities that signature-based systems cannot match

This article draws from insights shared at Abnormal Innovate. Watch the full recording to hear how leading security experts address the evolving threat landscape.

Email threats bypassing SEGs represent attacks that evade detection entirely—not messages that slip through occasionally due to misconfiguration, but sophisticated attacks engineered to exploit what gateways fundamentally cannot see.

Secure email gateways serve as perimeter defenses, scanning inbound messages for malicious URLs, dangerous attachments, and other technical indicators. They excel at catching known threats—malware signatures, blacklisted domains, and suspicious attachment types. For years, this approach provided reasonable protection.

The bypass problem emerges when attackers craft messages containing no technical indicators. Pure text emails requesting urgent wire transfers. Links to legitimate cloud services hosting malicious content. Messages sent from already-compromised accounts within your organization. These attacks don't fail SEG inspection—they pass it completely because there's nothing technically malicious to detect.

As Evan Reiser, CEO of Abnormal Security, explained during the webinar: "Secure email gateways rely on predefined rules and threat intelligence databases to identify malicious activity, focusing on known attack patterns." When attacks contain no patterns matching those databases, they're invisible to the gateway.

The fundamental limitation isn't technology—it's architecture. SEGs were designed to protect email infrastructure by scanning for technical threats. They weren't built to understand human behavior or detect psychological manipulation.

SEGs analyze messages for malicious payloads: executable attachments, dangerous links, spoofed headers. This approach assumes threats carry technical fingerprints. Business email compromise (BEC) attacks contain none, just persuasive text from seemingly legitimate senders requesting normal business actions.

Traditional gateways require known signatures to detect threats. Modern attackers leverage AI to generate unique attacks for each target. Reiser noted: "Modern attackers are now using AI to create attacks that secure email gateways can't recognize because they don't match existing patterns."

SEGs generate alerts requiring manual review. Security teams already face overwhelming workloads—alert fatigue compounds the problem. Meanwhile, AI-powered attacks operate at machine speed, overwhelming human-dependent response capabilities.

A gateway cannot determine whether an invoice request from a vendor matches historical communication patterns. It cannot recognize that an employee suddenly accessing sensitive data at unusual hours suggests account takeover. Without behavioral understanding, attacks targeting human psychology remain invisible.

Email threats bypassing SEGs fall into three distinct failure modes, each requiring different defensive approaches:

Category 1: Architectural Blind Spots — Threats SEGs were never designed to detect. These exploit the fundamental gap between infrastructure protection and human protection.

Category 2: Evasion Techniques — Attacks specifically engineered to circumvent signature-based scanning. Attackers understand how SEGs work and design around them.

Category 3: Zero-Day Social Engineering — AI-generated attacks with no technical indicators and no historical patterns. These represent the emerging threat landscape where traditional defenses become obsolete.

BEC attacks represent pure social engineering—no malware, no malicious links, no dangerous attachments. An attacker impersonating a CEO sends an urgent wire transfer request to the finance team. The email passes every technical inspection because technically, there's nothing wrong with it.

These attacks target human behavior with convincing messages containing zero technical indicators. The gateway sees a clean email; only behavioral analysis revealing the request doesn't match normal executive communication patterns can identify the threat.

When attackers compromise legitimate accounts, emails sent from those accounts bypass reputation checks entirely. The sending domain is trusted. The user exists. Authentication passes.

Internal-to-internal attacks become invisible to perimeter security. An attacker controlling an employee's email account can phish colleagues, exfiltrate data, and establish persistence—all while appearing as normal business activity.

Trusted third-party accounts exploited for invoice fraud present unique challenges. A compromised vendor sends a legitimate-looking invoice with updated payment details. The sender's reputation is established. The relationship is real. Only detecting subtle anomalies in communication patterns or financial details reveals the attack.

Compromised employee accounts targeting colleagues remain invisible to perimeter-focused defenses. SEGs inspect traffic crossing organizational boundaries—internal communications bypass them entirely. An attacker who gains access to one account can systematically target the entire organization from within.

Attackers link to legitimate services—SharePoint, Google Forms, OneDrive—hosting credential harvesting pages. The URL reputation appears clean because the domain is trusted. Only the specific page contains the threat, and URL scanning at delivery time sees nothing malicious.

Clean URLs at scan time become weaponized post-delivery. Attackers send messages with benign links, then modify the destination after the email passes inspection. Time-of-click protection helps but can be defeated through timing manipulation and legitimate redirect chains.

Malicious URLs embedded in QR codes evade text-based scanning entirely. The image contains no scannable text. Users scanning codes on mobile devices often bypass corporate security controls, landing directly on phishing pages. This attack vector grows as organizations increasingly use QR codes for legitimate purposes.

Malicious payloads assembled client-side after delivery present nothing detectable at the gateway. The email contains fragments that individually appear harmless but combine in the recipient's browser to deliver malware. No payload exists at the inspection point—only the components to create one.

As Evan Reiser noted at Abnormal Innovate, "AI enables attackers to analyze vast amounts of personal data from social media, from email, from online to identify vulnerabilities."

According to IBM, generative AI has reduced the time to write a convincing phishing email from as long as 16 hours to just 5 minutes. These messages reference real projects, actual colleagues, and current business contexts—making them nearly indistinguishable from legitimate communications.

AI-generated voice or video reinforcing email urgency creates multi-channel attacks combining email with synthetic media. An employee receives an urgent email request followed by a deepfake voicemail from their CEO confirming the request. The combination proves devastatingly effective.

AI-generated unique content for each target eliminates pattern matching entirely. No two emails in a campaign share signatures. Threat intelligence databases never develop patterns because each attack is one-of-a-kind.

Contextually aware messages reference real business events scraped from news, press releases, and social media. Attackers autonomously scan for organizational vulnerabilities and craft targeted pretexts exploiting current situations—mergers, leadership changes, vendor relationships.

Addressing these threats requires fundamentally different defensive architecture. As Reiser emphasized: "Protecting humans with AI powered behavior driven solutions will be the cornerstone of the next generation of cybersecurity products."

Behavioral AI Analysis

Technology that understands human behavior adapts autonomously to detect never-before-seen attacks. Rather than matching signatures, behavioral AI learns normal communication patterns and flags anomalies—regardless of whether technical indicators exist.

API-Based Integration

Moving beyond perimeter security to API-based integration enables analysis of all email traffic—including internal communications invisible to gateway inspection. Full visibility reveals account takeover attempts, insider threats, and compromised vendor communications.

Machine-Speed Response

Detection and remediation must operate at machine speed. Manual review processes cannot keep pace with AI-generated attack volumes. Automated response removes threats before users engage.

Layered Defense

The goal isn't replacing SEGs but augmenting them. Behavioral AI integrates seamlessly with existing infrastructure to enhance detection capabilities, filling the gaps that signature-based systems cannot address. Gateway protection remains valuable for known threats—behavioral AI addresses the sophisticated attacks that bypass technical inspection.

Organizations frequently encounter obstacles when enhancing email security:

• Integration complexity: Adding new solutions alongside existing infrastructure requires careful planning

• False positive concerns: Behavioral detection must balance sensitivity with operational disruption

• Visibility gaps: Internal communications and cloud application usage create blind spots

• Resource constraints: Security teams lack bandwidth for additional manual review

Implement behavioral analysis as a complementary layer — Behavioral AI should enhance gateway protection, not create conflicting detection logic.

Establish baseline communication patterns — Understanding normal behavior enables anomaly detection without signature dependency.

Enable automated response for high-confidence threats — Remove clear attacks immediately; escalate ambiguous cases for review.

Monitor internal and external communications equally — Account takeover protection requires visibility into all organizational communications.

The twelve threats outlined here represent architectural limitations, not implementation failures. SEGs remain necessary infrastructure for blocking known attacks—but alone, they're insufficient against sophisticated threats targeting human psychology rather than technical vulnerabilities.

Organizations that recognize this reality and layer behavioral AI protection on top of existing defenses position themselves to address both current threats and emerging AI-powered attacks. Abnormal integrates seamlessly with existing security tools, enhancing your current stack rather than requiring full replacement. The fundamental shift toward human-centric security has become essential.

Ready to see what's bypassing your current defenses? Request a demo to discover how behavioral AI detects the email threats your gateway cannot see.