AI has fundamentally changed the economics of cybercrime.

AI-generated phishing emails are more convincing, dramatically more scalable, and far more profitable for threat actors than human-crafted attacks. Research shows users are more than four times as likely to engage with AI-generated phishing messages, and attackers can increase campaign profitability by up to 50 times using AI.

As AI accelerates attack workflows, security teams face a critical question: what kind of AI is actually capable of stopping AI-powered attacks?

The answer isn’t simply “more AI” layered onto tools that weren’t built for it. What matters is whether AI is foundational to how the security system operates.

Today, nearly every security vendor claims to be “AI-powered.” But for security leaders, that label has become increasingly meaningless.

In many products, AI is applied to limited use cases: a chatbot that summarizes alerts, a model that explains why a message was flagged, or an LLM that answers analyst questions. These capabilities can improve usability, but they don’t fundamentally change how threats are detected or stopped.

Modern attacks increasingly exploit human behavior, identity, and context, often without malware, suspicious links, or known indicators of compromise. They also continue to evolve, introducing new techniques such as thread-spoofed vendor impersonation. When threats look legitimate by design, surface-level AI features aren’t enough to reliably distinguish attacks from normal activity.

That’s where the difference between AI-augmented and AI-native security becomes determinative.

AI-augmented security refers to legacy tools that add AI on top of existing architecture. AI may assist with analysis or workflows, but it isn’t central to how the system operates. Detection remains constrained by static rules, predefined policies, and the need for manual intervention.

In an AI-native system, AI is foundational to how the technology operates. It shapes how data is ingested, how signals are evaluated, and how decisions are made. Because AI sits at the core of the platform, it drives every capability in a unified way, creating the conditions for intelligence to compound over time.

This distinction is not merely academic. Defending against AI-driven attacks requires systems that continuously learn behavior, adapt in real time, and make high-confidence decisions at machine speed, not tools that rely on bolt-on intelligence.

Rather than retrofitting AI onto legacy tools, Abnormal AI built an AI-native behavioral security platform architected to model how people, vendors, and systems normally interact. The Abnormal Behavior Platform ingests tens of thousands of behavioral, identity, and content signals through a cloud-native API architecture connected to Microsoft 365, Google Workspace, and other enterprise applications.

Using these signals, the Abnormal Behavior Platform builds continuously evolving behavioral models for every user, vendor, application, and tenant, establishing a baseline understanding of normal communication patterns, access behavior, infrastructure usage, and intent. A layered set of AI techniques, including machine learning, deep learning, and large language models (LLMs), works to detect subtle behavioral deviations that indicate risk with high confidence.

Most importantly, this intelligence is centralized. Behavioral insights are not locked inside individual products; they are shared across the Abnormal Behavior Platform through common Knowledge Bases, allowing learning in one area to strengthen protection everywhere else.

This shared intelligence model is what allows AI-native platforms like Abnormal to deliver outcomes that legacy tools structurally can’t.

Centralized behavioral intelligence allows Abnormal to detect socially engineered attacks that bypass traditional secure email gateways. That architectural advantage helps explain why 76% of Abnormal customers have retired their third-party SEGs. Across Inbound Email Security, this includes business email compromise, vendor fraud, and other advanced threats identified through analysis of sender-recipient relationships, communication frequency, historical context, and semantic intent.

That same behavioral intelligence drives rapid, autonomous response to identity compromise. Account Takeover Protection remediates threats in under 6 seconds on average, automatically cutting off attacker access before damage spreads across the enterprise.

AI-native platforms also deliver meaningful productivity gains. By applying behavioral understanding to user-reported emails, AI Security Mailbox reduces investigation and response times by 95%.

These results aren’t isolated product wins. They’re the natural outcome of an AI-native foundation where intelligence compounds across the platform rather than fragmenting across tools.

As attackers increasingly rely on AI, defenders must respond in kind, using platforms designed to operate at the same speed and scale.

AI-native platforms reduce operational burden, automate response, and eliminate the need for constant tuning and rule maintenance, allowing security teams to spend less time investigating alerts and more time reducing real risk.

Stronger protection, faster response, and meaningful time savings distinguish AI-native security from AI added as a feature—a structural advantage rather than a feature checkbox.

To see the Abnormal Behavior Platform in action, schedule a personalized demo.