Model cascading is a routing strategy that sends each request to the smallest model likely to handle it, and escalates only harder cases to more capable or more expensive models. The decision can be based on prompt length, confidence, sensitivity, latency targets, or the need for deeper reasoning. This keeps AI systems faster and cheaper while preserving quality where it matters most.
In cyber security, model cascading helps scale detection and response without wasting compute. A lightweight model can triage spam, phishing, simple policy questions, or repetitive alerts, while a stronger model reviews ambiguous incidents, malware analysis, or high-impact investigations. It also reduces attack surface from cost abuse: adversaries can try to flood a service with requests that force expensive inference, so routing logic and guardrails matter. Done well, cascading is both a cost control and a resilience control.