In cybersecurity, correlation isn't just causation—it's everything. We calculate r for your security posture. We find the correlations. We spot the patterns. We stop the breaches.
We don't just use AI. We use it to do the one thing that actually matters in cybersecurity: find the patterns before they become breaches.
This isn't artificial intelligence guessing at patterns—it's mathematical certainty measuring relationships.
Most security AI is looking for anomalies—outliers, weird stuff, red flags. That's important, but it's reactive. It's looking at individual data points.
We're looking at the space between the data points. We're measuring the relationship between variables. The strength of the connection. The predictability of the pattern.
Our correlation algorithms are based on proven statistical methods, selected depending on data linearity and distribution. This isn't guesswork—it's mathematical rigor.
We calculate r across 47 behavioral dimensions simultaneously, constructing correlation matrices that reveal coordinated attacks invisible to conventional security tools.
Sophisticated attackers operate in the correlation sweet spot: r values between 0.6 and 0.9. Too random, and they're ineffective. Too predictable, and they're obvious. That's where we hunt.
No human analyst can calculate r for 50,000 simultaneous events per hour. But our AI can. And it does. We analyze covariance matrices across 47 different behavioral indicators simultaneously.
Traditional SIEM tools analyze events in isolation. We analyze relationships. When five seemingly unrelated events show r = 0.94, that's not coincidence—that's a coordinated attack.
In 1880, a British statistician named Francis Galton was obsessed with a question nobody could answer: How do we measure the strength of a relationship between two things?
He was studying fathers and sons. Tall fathers. Short fathers. Tall sons. Short sons. There was clearly some relationship, but how strong? How predictable? How measurable?
The correlation coefficient became the universal language for finding hidden connections in chaos. r became the foundation of modern statistics, medicine, finance, and now—cybersecurity.
When Francis Galton invented r in 1880, he had no idea it would become one of the most important concepts in science, medicine, finance, and now—cybersecurity.
rSecurity.ai isn't just a catchy domain. It's a promise.
The .ai means we're using artificial intelligence to calculate correlations at scale—across billions of data points, in real-time, across your entire attack surface.
No human analyst can calculate r for 50,000 simultaneous events per hour. But our AI can. And it does.
Every single product in the rFamily ends in .ai because every single product is correlation analysis at scale:
You use AI to calculate r to make customers secure. Simple. Memorable. Defensible. Smart as fuck.
For investors and technical buyers who want to verify we're not bullshitting:
Our correlation algorithms are based on Pearson's r, Spearman's ρ, and Kendall's τ depending on data linearity and distribution. This isn't experimental—it's 144 years of proven statistical science.
Traditional SIEM tools analyze events in isolation. We analyze covariance matrices across 47 different behavioral indicators simultaneously. We're not looking at points—we're mapping relationships.
We achieve 94% pattern detection accuracy with a false positive rate under 2%. These aren't marketing claims—they're mathematical outcomes from rigorous correlation analysis.
Remember Moneyball? Billy Beane used statistics nobody else was tracking to build a championship team for 1/3 the budget. That's us, but for cybersecurity.
We're the only security company named after a math equation. That should tell you something about how we think.