Counterfactual mitigation optimization for multi-stage attacks via causal AI

Abstract

Multi-stage cyberattacks often bypass defenses not due to detection failures, but because response actions are delayed or poorly aligned with the true causal drivers of attack progression. This paper proposes Counterfactual Mitigation Optimization (CMO), a causal AI framework that transforms heterogeneous security telemetry into a structural causal model and evaluates counterfactual intervention scenarios to identify minimal, high-impact mitigation actions. Unlike alert-driven playbooks, CMO generates actionable, stage-aware response plans aligned with the MITRE ATT&CK lifecycle while accounting for uncertainty, operational constraints, and side effects. The paper outlines the framework architecture, causal graph construction, counterfactual evaluation under feasibility constraints, and a multi-objective optimization strategy, and presents an experimental blueprint for assessing effectiveness in multi-stage attack scenarios.