<p>The growth of high-velocity data flows in security-important fields, such as network cybersecurity or big-data scientific computing, has generated an acute demand among smart systems that can handle data within strict operational deadline limits. Although deep learning (DL) models provide unprecedented levels of analysis power, their computational intensity also makes them unsuitable in applications where any break in the decision deadline can result in disastrous outcomes. The current literature has either concentrated on soft real-time systems or fixed-point optimisation of models, and a large gap in the literature that provides dynamic, adaptive frameworks to ensure performance within strict latency constraints remains open. The paper presents the Dynamic Gated Temporal Convolutional Network (DG-TCN), a new integrative architecture that combines an efficient feature extractor, a lightweight Temporal Convolutional Network (TCN), with a Deep Reinforcement Learning (DRL) agent to dynamically modulate the architecture in real time in a manner that is both practical and scalable. The DRL agent dynamically adjusts the computational graph of the TCN—selectively activating or bypassing convolutional blocks—to actively manage the trade-off between inference accuracy and latency on a per-batch basis. We empirically validate the DG-TCN on two disparate, high-volume public datasets: the CSE-CIC-IDS2018 for cybersecurity threat detection and the HIGGS dataset for scientific particle classification. Our results demonstrate that the DG-TCN outperforms, across evaluated conditions, both high-accuracy static models and low-latency static models, reducing deadline miss rates by up to 78% while maintaining an F1-score within 3% of the much larger, non-real-time model. The theoretical contributions include a new paradigm for deadline-constrained, adaptive-depth deep learning, while the practical benefits manifest in a practically deployable solution for latency-sensitive, security-critical decision-making systems.</p>