Synechron Inc

Kubernetes-based cloud-native systems are scalable and agile, and present intricate security challenges because of the dynamism of workloads, shared boundaries of trust, and automated pipelines of deployment. Conventional perimeter security models cannot guard against contemporary threats in such environments. This paper combines workload identity, policy-as-code, network isolation, and runtime security into a complete Zero-Trust Kubernetes architecture to make cloud-native applications secure. The framework proposed leverages SPIFFE-based workload identities, the Open Policy Agent (OPA) to enforce declarative policy, default-deny network policies, mutual TLS (mTLS) for communication between services, and continuous system monitoring. An experimental analysis of the conditions of baseline, partial and complete Zero-Trust deployment on a three-tier micro-service application is made. Findings show significant enhancements in security performance, such as 99.8% of unauthorized access prevention, 100% traffic encryption, and 99.2% of coverage of the policy, and acceptable performance overhead is seen. The paper substantiates the fact that the Zero-Trust principles can be applied to Kubernetes environments and can be successfully implemented to ensure a high level of security and responsibility to the operations as well as their efficiency and scalability.

Long-running Large Language Model (LLM)-based agents often accumulate large intermediate traces containing audits, eliminations, and numeric calculations. In practice, this state is compressed before handing it to a downstream decision step, creating an information bottleneck in which small omissions can break strict numeric or categorical constraints. This paper evaluates hand-off compression in a closed-world travel-planning relay with two LLM agents. A Researcher audits a fixed inventory of hotels and flights for 50 goal instances, and a Booker selects a hotel–flight pair using only the goal and the hand-off payload, with the inventory withheld. We compare four hand-off conditions: no compression, narrative summarization, schema-constrained JSON extraction, and embedding-based pruning. Exhaustive enumeration over the fixed inventory provides exact feasible and optimal labels. Results show that hand-off representation strongly affects downstream feasibility under a small decision model. JSON extraction achieves the highest feasibility accuracy at 0.96, while narrative summarization, despite producing the smallest compressed hand-off payload, degrades feasibility to 0.48. Embedding-based pruning matches the uncompressed control on feasibility at 0.88 without an additional generative compression call. These findings indicate that constraint checking benefits from structured and auditable hand-off representations rather than relying on brevity alone.

Creating conversational agents powered by a large language model (LLM) is a complex series of steps where there is a scientific basis for use of natural language processing (NLP), machine learning (ML) and human-computer interaction (HCI). The first step in creating a conversational agent using an LLM is to gather and prepare as varied a set of high quality written texts as possible to be used as data for training this model. Next, after gathering data, one has to determine which type of LLM architecture (e.g., GPT, LLaMA) to employ, as well as to fine-tune this chosen architecture by applying supervised or reinforcement learning through human feedback (RLHF). After this step, one then needs to create a method for managing dialogues with users through dialogue management so that users will engage with the conversational agent in a context-aware manner. There are two methods of ensuring that a conversational agent interacts with users in a context-aware manner: retrieval-augmented generation (RAG) and/or rule-based systems that improve on the coherency of a conversational agent’s conversation with a user. Once the methods of managing a dialogue with a user have been established, measures (e.g., BLEU, ROUGE, etc.) must be developed to assess the performance of the conversational agent against expected usability and safety requirements (human evaluations). Finally, the conversational agent is ready for deployment and iterative refinement, both of which require the development of scalable cloud integrations, real-time monitoring, as well as continued learning from user feedback. Throughout the various procedures associated with creating conversational agents using an LLM are ethical considerations in regard to eliminating or reducing bias and also addressing user privacy. In the end, the methodical procedure outlined here will enhance the precision, flexibility, and reliability of LLM-based conversational agents, laying the groundwork for their application in customer service, healthcare, and education. Future developments may involve possible multimodal integration and self-improvement capabilities.

The most prevalent cause of blindness is diabetic retinopathy, which may be avoided with early detection. For standard diagnosis, ophthalmologists manually examine fundus photographs, which is a costly, subjective, and time-consuming process. With findings comparable to or superior to those of human specialists, recent developments in deep learning (DL) and artificial intelligence (AI) have made it possible to automatically and precisely identify DRs from fundus pictures using CNN- and/or hybrid-based DR stage categorization. To enhance picture quality, several methods are applied first, such contrast-limited adaptive histogram equalization (CLAHE) and denoising. The GLCM or deep learning are then used for feature extraction. High binary and multiclass accuracy of up to 99.4% (mild, moderate, severe NPDR and PDR) with 512 has been attained by models like EfficientNet-B0, Inception-V3, and customized models like RSG-Net. Weighting fusion methods: These two pre-trained networks also function well when combined, offering a more potent recognition force. These AI models are appropriate for real-time applications in low-resource environments because they solve imbalanced dataset issues and provide computationally efficient solutions. For more in-depth DR advancement tracking, we intend to investigate the combination of vision transformers and severity rating regression. Therefore, automated DR diagnosis holds the promise for scalable and affordable screening, which could lessen the global burden of blindness.

Large language models (LLMs) are advancing rapidly and leading to the creation of advanced AI systems, but the production of these systems and their deployment is still time and resource-consuming. This paper presents methods of easing the creation of the next generation of systems that use LLMs by providing a modular framework with a clear separation between model training and model deployment, allowing pre-trained LLMs to be easily integrated with task specific components via standard APIs and lightweight adapters. In addition, several techniques are used to enhance inference speed and reduce the overhead of computation including: quantization; dynamic batching; and knowledge distillation. In addition, a configuration-driven pipeline has been created for fine-tuning and prompt engineering to minimize human intervention while allowing for flexibility to meet the needs of the user. The proposed approach also supports the use of automated monitoring and feedback mechanisms to promote the continual improvement of the model once in use. Experiments have shown significant reductions in the time needed to deploy and the cost of infrastructure for a variety of applications, including conversational AI, document analysis and decision support systems. Finally, best practices are provided to ensure that robustness, fairness, and interpretability are maintained when deploying systems in simplified production environments. Our framework expedites the adoption of LLM technologies in business contexts by abstracting away low-level complexity, freeing developers to concentrate on innovation rather than implementation challenges. This work offers useful insights for improving the sustainability and accessibility of cutting-edge language AI for broad industrial usage.