Why is behavioral AI better at catching AI-generated attacks?

Generative AI changes the email but cannot change the underlying behavior — a payment request still comes from an unfamiliar sender, breaks the established relationship pattern, and deviates from baseline writing style. Behavioral AI reads those signals; signature engines read the words. Words are now the part attackers control best.

Can Abnormal tell if an email was written by an LLM?

Detection does not depend on flagging 'AI-written' text — that's a brittle signal that erodes as models improve. Abnormal scores the message against the sender's behavioral baseline: writing style fingerprint, relationship history, request type, timing, and dozens of other signals. AI-generated attacks deviate from the baseline whether or not the content reads as machine-generated.

Does this work against polymorphic email campaigns?

Yes. Per-identity behavioral baselines are not pattern-matching against the message; they are scoring it against the established relationship. 50,000 variants of the same attack still trip the same behavioral signals — unfamiliar sender, atypical request, broken pattern.

What about voice clones and deepfake video tied to the email?

Abnormal protects email, Slack, and Teams in one platform. Cross-channel signals correlate — a suspicious email followed by a synthetic voicemail or Teams message escalates the behavioral score together. Multi-modal lures get caught as multi-modal anomalies.

How quickly does Abnormal learn a new identity?

The behavioral baseline starts forming within hours of connecting via API and reaches operational confidence within 7 days. From day one, the platform uses federated intelligence across 4,500+ organizations to catch novel attacks even before per-tenant baselines mature.

Stop Generative AI Attacks

Perfect Grammar Doesn't Mean Safe

LLM-crafted spear phishing reads like a colleague, references real projects, and survives every signature check — only a behavioral baseline catches the deviation.

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Increase in malicious phishing emails since LLMs went mainstream

SlashNext State of Phishing 2024

Zero

Missed attacks or false positives in the last month

Pegasystems customer story

Fewer false positives than competing vendors

Pegasystems head-to-head evaluation

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The Challenge

Why Signature Engines Can't See AI-Crafted Phishing

Every AI Email Is Linguistically Unique

LLMs generate fresh prose for every target, so signature-based detection has nothing to match against.

Grammar and Tone-Check Filters Fire Green

AI-generated text is grammatically perfect — none of the traditional 'broken English' indicators apply.

Personalization References Real Org Context

Attackers feed LLMs your LinkedIn data, then generate emails that name your projects, partners, and managers.

Polymorphic Content Evades Content-Based Rules

Every AI email is a one-off, so YARA rules and keyword filters expire on contact.

Multi-Modal AI Generates Lures Across Channels

Generative AI now produces voice clones, deepfake video, and synthetic chat messages that reinforce the email lure.

Real Incident

AnAnAI-craftedAI-craftedemailemailimpersonatingimpersonatingaaVPVPreferencedreferencedthetherecipient'srecipient'srecentrecenttraveltravelandandaarealrealcustomercustomerdealdeal——wirewiretransfertransferinitiated,initiated,signaturesignatureenginesenginessawsawnothingnothingwrong,wrong,behaviorbehaviorcaughtcaughtthethewriting-stylewriting-styledeviationdeviationinin0.40.4seconds.seconds.

Based on a real customer incident

The Solution

An Abnormal Approach to Stopping AI-Generated Attacks

Models normal behavior across thousands of identity signals, so AI-polished impersonation is caught on relationship and intent — not on how convincingly it's written.
Detects polymorphic campaigns where no two messages match, flagging the shared behavioral pattern instead of a static signature.
Correlates email, Slack, and Teams signals to catch multi-channel AI lures as the coordinated attacks they are.

主な機能

Behavioral AI Built for Modern Threats

Behavioral Baselines, Not Signature Lists

Per-identity baselines across 45,000+ signals score every message on relationship history, writing style, and request type — content-agnostic to LLM polish.

Cross-Channel Threat Correlation

Slack, Teams, and email signals correlate so multi-modal AI lures get caught as the coordinated attacks they are.

Train Users on AI-Era Threats

Personalized simulations evolve with the threat landscape — your team builds reflexes for the spear phishing of 2026, not 2016.

プラットフォーム概要を見る

Fortune 500企業の25%以上が、Abnormal AIの自動化されたセキュリティ判断を信頼して採用しています

Customer Voice

Real Results from Security Teams

“Abnormal's automation gives our analysts time back to work on other projects, and the fact that it's API-based gives us flexibility to tie in other applications and their data.”

John Roeser

Senior Manager, Information Security, Domino's

“Our goals are to get away from being so reliant on human judgment and leverage AI to be proactive. Abnormal helps us with those goals.”

Corey Kaemming

Senior Director, Information Security, Valvoline

AbnormalはFortune 500の25%以上を含む4,500社以上のお客様にご利用いただいています。

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Related Resources

Webinars

The Detection Gap: Why AI-Powered Attacks Are Winning Against Legacy Email Security

Watch this on-demand webinar to learn how AI-powered BEC attacks bypass secure email gateways, and how behavioral AI can detect what legacy tools miss.

White Papers

The Essential Guide to Human Risk Management

Learn why traditional security awareness training fails to reduce real-world risk and how modern human risk management helps organizations measure and reduce risky behavior over time.

Data Sheets

Displace Your Secure Email Gateway

Stop sophisticated email attacks that target your employees using native cloud-based email security plus Abnormal.

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Stop the Phishing No Signature Has Seen Before

Deploy in 60 seconds via API. No MX changes. Catch AI-crafted attacks the moment they break behavior — not after the breach.