The cybersecurity landscape has changed dramatically over the past few years. Attackers are more sophisticated, more automated, and increasingly using AI themselves. To keep up, defenders need to evolve too.
The Evolution of Threats
Modern attacks are designed to slip past traditional defenses. Attackers use techniques like:
- Freshly compiled payloads that haven't been seen before
- Custom obfuscation to hide malicious intent
- Legitimate system tools repurposed for attacks
- Distributed attacks from multiple sources
These techniques make it harder for traditional signature-based tools to keep up. That's where behavioral analysis comes in.
How Behavioral Analysis Works
Instead of looking for known signatures, machine learning models analyze patterns of behavior. This lets them detect threats they've never seen before by recognizing suspicious activity.
Our models look at:
- Attack timing - Rapid repeated attempts, even when distributed
- Network patterns - Unusual connection behavior, scanning activity
- File characteristics - Structure, entropy, embedded patterns
- Coordination - Multiple sources working together
Real-World Results: Catching What Others Missed
Just this week, our ML-based detection flagged a suspicious file that had been uploaded to one of our honeypots. We submitted it to VirusTotal for analysis.
The result? 0 out of 76 antivirus engines detected it as malicious.
Our behavioral analysis? 94% confidence malicious.
The file had high entropy in packed sections, suspicious API call patterns, and structural anomalies that our model recognized as indicators of a credential stealer. Traditional signature matching saw nothing wrong because the file was freshly crafted to evade detection.
This is exactly why behavioral analysis matters. It catches what signature-based detection can't.
The Power of Federated Intelligence
For MSPs managing multiple clients, there's another huge advantage: shared threat intelligence.
When our system detects an attack on one client, that intelligence is instantly shared across the network. An attacker that targets Client A at 2:00 PM is already blocked at Clients B, C, and D by 2:01 PM - before they even attempt the attack.
We call this our BOLO (Be On Look Out) system. It turns every connected agent into a sensor that strengthens the entire network.
Getting Started
We built Prometheus AI to give MSPs enterprise-grade threat detection without the enterprise price tag. If you're interested in being part of our early access program, we'd love to hear from you.