Adaptive Federated Ensemble AI for Real-Time Data Exfiltration Detection in Financial Networks

Data exfiltration poses a critical threat to financial systems, often resulting in monetary losses, reputational damage, and regulatory penalties. The distributed and encrypted nature of modern financial networks, coupled with rising insider threats, has rendered traditional centralized detection systems ineffective due to low detection rates, high false positives, and difficulty enforcing data privacy compliance. To address these limitations, this paper proposes an Adaptive Federated Ensemble AI framework for real-time monitoring of data exfiltration in financial networks. The model employs federated learning to train locally across distributed network segments, ensuring privacy by transmitting only encrypted model updates to a global aggregator. It integrates supervised, unsupervised, and semi-supervised learning techniques to analyse user behaviour, network metadata, and encrypted traffic flow, while reinforcement learning dynamically refines detection profiles to reduce false alarms. Additionally, sidechannel analysis enables identification of covert exfiltration in encrypted traffic without decrypting sensitive data. Automated, multi-tiered response mechanisms ensure timely mitigation of threats. Experimental results demonstrate that the proposed framework outperforms centralized models with higher detection accuracy, lower false positives, and faster response times. Overall, the Adaptive Federated Ensemble AI framework provides a scalable, privacy-preserving, and intelligent defence mechanism against real-time data exfiltration in financial environments.

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