Secure Your Cloud Infrastructure

This paper presents a comprehensive zero trust security framework specifically designed for cloud infrastructure environments. We analyze the transition from traditional perimeter-based security models to zero trust principles, evaluate current implementation challenges faced by enterprise organizations, and propose a systematic deployment methodology. Our framework addresses identity verification, micro-segmentation, continuous monitoring, and policy enforcement across multi-cloud environments. Through evaluation across 12 enterprise deployments, we demonstrate a 60% reduction in security incidents and 45% improvement in mean time to detection (MTTD). The framework provides actionable guidance for organizations at any stage of zero trust maturity.

We investigate efficient fine-tuning methods for large language models (LLMs) when training data is scarce or expensive to acquire. Our research systematically compares parameter-efficient fine-tuning techniques — including LoRA, QLoRA, and Adapter Layers — across multiple model architectures (7B to 70B parameters) and domain-specific tasks. We introduce a novel data augmentation pipeline that generates high-quality synthetic training examples using a teacher-student framework, achieving a 3.2x effective data multiplier. Our results demonstrate that combining QLoRA with targeted synthetic data generation achieves 94% of full fine-tuning performance using only 15% of the training data, while reducing GPU memory requirements by 70%. We provide practical deployment guidelines for enterprise applications with budget and latency constraints.

This study evaluates the deployment of language models on edge devices with constrained compute and memory resources. We systematically benchmark 15 model variants across 4 edge hardware platforms (Apple M-series, Qualcomm Snapdragon, Intel NPU, NVIDIA Jetson), analyzing latency, accuracy, memory utilization, and power consumption. We propose an optimization pipeline combining quantization (GPTQ, AWQ, GGML), structured pruning, and speculative decoding that achieves 8.3x inference speedup with less than 3% accuracy degradation. Our hybrid edge-cloud architecture provides automatic fallback for queries exceeding local model capability, achieving 95th-percentile latency under 100ms for on-device inference while maintaining cloud-equivalent accuracy for complex tasks.