AI-Enabled Cybersecurity Threat Prediction and Response Systems for Distributed Computing Environments

Abstract

Distributed computing environments spanning cloud, edge, and on-prem clusters amplify the attack surface, the velocity of threats, and the cost of delayed responses. This paper proposes an AI-enabled threat prediction and response architecture that fuses streaming telemetry (logs, traces, metrics, flows) with graph-based security analytics and probabilistic forecasting to anticipate compromise before service-level objectives are violated. A multimodal detection stack combines self-supervised embeddings for rare-event sensitivity, graph neural networks for lateral-movement path inference, and online anomaly detectors calibrated with conformal risk controls to bound false positives under drift. To translate predictions into action, we integrate a safety-aware policy layer: rule-constrained reinforcement learning selects minimally disruptive mitigations (isolation, policy tightening, token revocation) and is gated by a digital-twin simulator to prevent unsafe rollouts. Privacy and jurisdictional constraints are addressed through federated training with secure aggregation and differential privacy on labeled incident snippets. The system is production-minded: models are packaged with MLOps guardrails, continuous evaluation, and explainability artifacts (counterfactuals, feature attributions) for analyst trust and audit. Empirically, the approach is designed to improve early-warning horizon for high-severity events, reduce median time-to-mitigation, and maintain sub-percent enforcement overhead on latency-critical paths. We discuss deployment patterns for zero-trust, identity-centric networks and outline hardening against adversarial ML. The result is a cohesive, testable pathway from real-time prediction to safe, automated response in heterogeneous, distributed infrastructures