Nirmal Kumar Jingar | Searcharxiv

arXiv2026

Policy-Governed Post-Quantum Migration for Legacy Microservices Using Ephemeral Sidecar Architectures

The fast development of quantum computing represents a big risk to classical cryptography that is commonly used in cloud native and microservice based enterprise systems. Traditional cryptographic primitives are closely linked to legacy microservices and it is both intricate, hazardous, and disruptive to straight up migrate to post-quantum cryptography (PQC). In a bid to overcome these issues, this research presents a PolicyGoverned Post-Quantum Migration through Ephemeral Sidecar Architectures (PG-PQMES), a framework of dynamic and reversible migration that allows transparent adoption of PQC without any modifications in the legacy code of applications. The architecture is a combination of three coherent layers, including Ephemeral Crypto Sidecar Layer which injects the runtime cryptography, Policy Governance Layer which manages the migration centrally, and Migration Safety and Observability Layer which measures the performance and rolls back automatically. An innovative Policy-Governed Ephemeral PQ Migration (PG-EPM) algorithm is proposed to maximize the performance, compliance, and trust-based migration. In simulated environment of microservices, experimental assessment shows that the time of migration, service downtime, overheads of latency, and rollback recovery time are substantially reduced using the current migration strategies. The findings show that a sidecar-based migration strategy that is policy-based offers a viable, scalable, and enterprise-scale migration to a secure post-quantum transformation.

cs.CR↗

arXiv2026

Reliable LLM-Powered Decision Engines for Large-Scale Supply Chain Operations: Architecture, Safety, and Performance Guarantees

Current large-scale supply chains are highly uncertain, dynamic, and disruption prone that are challenging to serve up timely and resilient decisions through traditional rule-based and optimization-only systems. The increasing supply of heterogeneous data sources, such as transactional demand signals and unstructured disruption report, presents a chance of intelligent systems, which could reason, adapt and optimize at the same time. A hybrid architecture that combines large language models (LLMs) with mathematical optimization, probabilistic forecasting, and safety-constrained decision filtering is proposed in this paper as a performance of a Decision Engine, which is called LLM-Powered Decision Engine (LLM-DE). In comparison to purely data-driven or heuristic solutions, LLM-DE integrates semantic reasoning with LLM with a set of performance and safety guarantees that allow safe decision-making in large-scale supply chain processes. The suggested framework enables the end-to-end decision making such as demand forecasting, inventory optimization, and transportation routing and disruption mitigation. The findings affirm that language-based reasoning combined with optimization and formal constraints can be used to come up with not only smarter but also safer and more scalable supply chain decisions. This research provides a new architecture, a complete pipeline of algorithm, and a formulation based on mathematical constructs of the operational decision systems incorporating LLM. The proposed model offers a pragmatic and theoretical basis of the next-generation intelligent supply chain infrastructures that can be implemented to work dependably in the face of uncertainty and massive complexity.

cs.AI↗