Human Risk, Mathematical Solution: A Bayesian View on Insider Threat Detection

The insider threat is a persistent and vexing challenge in cybersecurity. It’s a risk that comes from within, from the very people we trust, making it a difficult puzzle to solve. Standard rule-based systems often miss these threats because they look for simple, clear-cut violations, not the subtle, complex web of behaviors that often signal an insider acting maliciously. To truly get ahead of this risk, we need a smarter, more adaptive tool. That’s where Bayes’ Theorem comes in.

Think of Bayes’ Theorem to continuously update your assumptions with new information. It provides a powerful framework for assessing insider risk by letting us adjust the probability of a threat as new evidence emerges. Let’s say we have a baseline: based on industry data, we assume a very low prior probability that any single employee is a threat each year. For this example, let’s say it’s 1 in 1,000, or P(threat) = 0.001.

Now, let’s add some evidence. An employee suddenly downloads a large volume of sensitive customer data — an unusual behavior. Historical records show that 80% of actual insider data breaches were preceded by this unusual behavior (abbreviated as ‘ub’), which equates to: P(ub ∣ threat) = 0.8. Conversely, only 5% of non-malicious employees have done this: P(-ub ∣ threat) = 0.05). Using Bayes’ Theorem, we can calculate the new, updated probability of this employee being a threat:

Suddenly, the probability that this employee is an actual threat jumps from 0.1% to 1.6%. While this number is still low, it’s a sixteen-fold increase, an indicator strong enough to justify further, more focused investigation.

But Bayes’ Theorem isn’t just for numbers. It’s also incredibly effective for incorporating qualitative evidence — the human element. An employee’s unusual network activity might become far more suspicious when you also consider that they’ve been expressing significant job dissatisfaction or are dealing with personal hardships like a difficult divorce or mounting financial stress. Separately, these data points might be meaningless. But when combined within a Bayesian model, they can collectively push the probability of an insider threat to a level that warrants action. This approach moves us beyond simple red flags and allows us to build a more holistic, and ultimately more accurate, picture of risk, creating a more resilient “human firewall” for our organizations.