Fun fact: Abnormal AI analyzes more emails each week than Visa processes transactions.

That’s a staggering volume, and it generates a uniquely deep understanding of normal human behavior across thousands of organizations.

But knowing what’s normal is only half the equation. The other half is understanding threat actor behavior—how attacks unfold and how they scale. When those perspectives come together at the speed of AI, threats are detected earlier and with far greater precision.

For years, threat detection started with the activities of a threat actor—what we call the “known bad”. The usual scenario involved an analyst investigating something suspicious and, once they determined it was malicious, lifting indicators like IP addresses or domains from that incident so the system could look for the same patterns elsewhere.

Everything depended on that human review; nothing changed until someone manually confirmed an attack.

This approach worked well when attacks moved slowly and analysts had time to push out new indicators before attackers changed infrastructure or tactics. It collapses in a world where artificial intelligence lets attackers spin up and adjust new campaigns in minutes.

Supercharged by AI, phishing no longer creeps along at human pace. AI‑powered tools can generate clean, convincing emails for attackers and churn out endless variations until one lands. Phishing-as-a-Service kits, easy and cheap if you know where to buy them, handle the setup so an attacker can press “launch” and walk away. In the past, threat actors needed to be brilliant and deeply technical to outsmart security defenses. Now they mostly need an idea and the willingness to act.​

In this world, even the fastest human investigations trail behind. By the time an analyst updates the rules, the attack has already shifted shape.

To get past the limits of traditional threat detection, Abnormal flips the model and starts with trusted behavior—the “known good” instead of the “known bad”.

Behavioral AI operates by ingesting massive volumes of activity and applying machine learning algorithms to identify patterns in how people and organizations actually behave day to day. Who does Alice normally email? What time does Bob typically work? Does Carol usually request wire transfers on Fridays?

When something lands outside the baseline, the system treats it as suspicious, even if no attack signature exists. The anomaly might be a phishing email, a compromised account, or a social engineering attempt that slides straight past secure email gateways.

The strength of behavioral modeling lies in its ability to surface both never‑before‑seen attacks and those that have been intelligently engineered to fool traditional security models. Attackers constantly evolve their methods precisely because defenders look for known patterns. Then you add AI, and it’s easy for them to spin endless variations on the same idea. Behavioral baselines catch what stands out from normal, whether or not that pattern has been seen before.

But behavioral modeling alone doesn't explain why something is off or which threat sits behind it. Knowing that Alice's behavior changed is useful. Knowing that her new behavior aligns with a live campaign from a known threat group turns that generic anomaly into a clearly identified threat, with a much sharper sense of urgency and response.

Abnormal takes threat intelligence that used to sit in a separate, manual workflow and feeds it into the same high‑velocity behavioral engine that already understands “good” activity across every user and organization. That shift lets the models make more nuanced decisions at machine speed.

Here’s how it works. When Abnormal’s system confirms a compromise within one customer environment, it pulls the full picture of that attack. It looks at the target, abused identities and workflows, message movement, how the attacker maintained access, and so on. That pattern becomes a behavioral footprint that the system can recognize in other environments.

Remember how Abnormal analyzes more emails per week than Visa processes transactions? That scale turns each behavioral footprint into something reusable, applying it across a massive, constantly refreshed stream of real‑world activity. This builds a live feedback loop. Each confirmed attack teaches the models more about how that threat behaves, and that learning improves threat detection across every environment Abnormal protects.

For security teams, the impact is concrete:

• More accurate detection. Models learn real campaign behavior from live threat intelligence and use those patterns to catch lookalike attacks.

• Machine‑speed defense. AI has raised the attacker’s capability ceiling. Abnormal’s approach lets organizations move at the same speed and scale.

• Better use of analyst time. The system handles the heavy pattern‑matching and correlation, freeing analysts from alert fatigue and allowing them to focus on incidents that require human judgment.

• Faster learning cycles. Instead of lagging behind manual rule changes and signature updates, defenses refresh as the models learn from each new event.

• Stronger preventive controls. Patterns in real attacks highlight where to change configurations and policies to make whole categories of threats harder to pull off.

To see how this works in practice, let’s look at two examples from recent attacker behavior.

Fake Meeting, Real Remote Access

In these campaigns, a Teams or Zoom meeting invitation lands in the inbox and looks routine enough to click. The link installs a remote access tool. The attacker now has control and can scout around and move into other systems.

Abnormal’s AI behavioral modeling catches that first email. The invite often comes from an external contact who has never used that pretext with the target or sent that link or tool before. The behavior sits outside the baseline for that relationship, and the platform blocks it.

That decision is just the starting point. The system takes everything it has learned about that intrusion—the actions, step sequence, tools, attacker movement, etc.—and creates a behavioral footprint to search for. It then looks across other users and tenants for sessions that follow the same pattern closely enough to raise concern.

When it finds a strong match, it reevaluates those sessions with the new context of a confirmed intrusion and can reopen them as likely compromises, even if originally marked as low risk. That feedback loop means protection flows both ways. New attacks are stopped in real time, and earlier activity is re‑scored through fresh threat intelligence so a single incident at one customer can protect others showing the same attack pattern.

The Remote IT Worker Scam

The main advantage of this combined approach is its ability to capture entirely novel attack routes, as seen in recent activity from North Korea. In these state-sponsored campaigns, operatives sidestep the usual defenses by applying for real remote IT roles using fake identities and inflated credentials. They interview well enough to get hired and receive corporate laptops and full network access.

The threat now sits inside the perimeter on a trusted account. It’s part insider, part outsider; a hybrid threat that traditional tools struggle to see. Some operatives simply collect their salary to fund the regime. But others quietly steal data or lay the groundwork for future intrusions.

Behavioral modeling catches this activity by analyzing the new account’s behavior. It compares sign‑in locations, devices, sessions, and how the account uses email and SaaS against the baseline for similar employees, including the typical hiring processes for that position. When the behavior doesn’t match the claimed identity or role, the system treats it as abnormal and raises it for investigation.

Threat intelligence adds a second layer of context. When a suspicious pattern aligns with behavior from known North Korean remote worker scams, Abnormal can treat the account as part of a nation‑state operation and feed that insight back into its models. That framing helps security teams assess risk more clearly and move faster on the most likely active threat actors.

For CISOs, the core job hasn’t changed. You still own the risk. What has changed is how you decide where to focus in a landscape where attackers are operationalizing AI and pushing more work on your team than humans can reasonably handle. To keep pace, you have to fight AI with AI, and this points to a handful of actions:

• Press vendors on their AI use. Many tools claim to be AI‑driven, but that label tells you little on its own. Ask open questions about the models behind the product and how they adapt to new threats in live environments. The answers show whether you’re buying real AI capability or just a refreshed legacy detection system.

• Treat AI as part of the risk surface. While AI can protect more of the business, the models and data behind it create new security issues within your remit. Fold each AI system into your existing risk process with the same scrutiny as other critical platforms.

• Move beyond the detect‑and‑respond cycle toward proactive, machine‑speed risk reduction. Choose tools that learn from behavior across customers and feed that insight back into the product. That turns patterns in real attacks into concrete changes in your setup and shuts down whole routes without your team fighting the same campaign by hand.

You don’t have to bolt this on yourself. Abnormal’s AI‑native behavioral models and live threat intelligence already put those moves into practice. CISOs get defense aligned with today’s innovative attacks, with more control and less grind for their teams.

Interested in learning more about Abnormal's AI-native detection? Schedule a demo today!