What is the difference between rule-based and AI-based anomaly detection?

Rule-based and AI-based anomaly detection differ primarily in how they identify deviations from normal behavior. Rule-based systems rely on predefined logical conditions or thresholds set by developers, while AI-based systems use machine learning models to learn patterns from data and detect outliers. Rule-based approaches are deterministic and explicit, whereas AI-based methods are adaptive and statistical, handling more complex or evolving data patterns.

Rule-based anomaly detection works by checking data against specific rules, such as “alert if CPU usage exceeds 95% for 5 minutes” or “flag transactions over $10,000.” These rules are manually designed based on domain knowledge or compliance requirements. For example, a firewall might block traffic from IP addresses marked on a known threat list. While straightforward to implement, this approach struggles with novel or subtle anomalies not covered by the rules. It also requires constant updates as systems evolve—for instance, if a server’s normal CPU usage increases due to new workloads, old thresholds become obsolete and cause false alerts. Maintenance costs grow as environments scale, making rule-based systems brittle in dynamic scenarios.

AI-based anomaly detection trains models on historical data to automatically recognize normal behavior, then flags deviations. Techniques like clustering, autoencoders, or time-series forecasting identify patterns humans might miss. For example, an AI model monitoring user logins could detect subtle anomalies in login times, locations, or device types that indicate account compromise, even if no single parameter breaches a rule. These systems adapt to changing data distributions over time, such as seasonal traffic spikes in e-commerce. However, they require large, clean datasets for training and can be computationally intensive. Explainability is also a challenge: unlike rule-based alerts, AI-generated warnings (e.g., “unusual network packet sequence”) may lack clear reasons, complicating root cause analysis. Hybrid approaches often combine both methods—using rules for known threats and AI for unknown patterns—to balance transparency and adaptability.