Technical articles, research papers, and thought leadership on cloud architecture, AI, and software engineering.
Published on: Cloud Native Now
Comprehensive guide on securing AI agents in cloud-native environments using Docker containers, Model Context Protocol (MCP), and cAgent framework. Covers best practices for container security, secure communication between AI components, and building trusted AI workflows at scale.
Published on: DZone
Practical guide to deploying Large Language Models using Docker, covering containerization strategies, resource optimization, model versioning, and production-ready deployment patterns. Includes best practices for GPU allocation, model serving, and scaling LLM workloads.
Published on: DZone
Deep dive into scaling WebRTC applications on Google Kubernetes Engine (GKE), featuring GPU-accelerated media processing, real-time monitoring with Prometheus, and custom latency-based autoscaling strategies to maintain quality of service at scale.
Published on: DZone
Explores deploying AI agents using Docker Compose for local development and implementing cloud offload strategies for production workloads. Covers hybrid deployment models, workload distribution, and optimizing costs by intelligently moving compute between local and cloud environments.
Published on: DZone
Comprehensive guide to implementing API threat analytics in cloud environments. Covers threat detection patterns, anomaly detection using machine learning, security monitoring, and building a robust API security posture with real-time analytics and automated response mechanisms.
Published on: DZone
Detailed migration guide for moving Apigee Edge ExtensionCallout policies to Google Cloud. Includes migration strategies, code transformation patterns, testing approaches, and best practices for ensuring zero-downtime migration of API management infrastructure.
Published on: HackerNoon
Step-by-step guide to implementing zero-downtime deployments using Akamai Global Traffic Management. Covers multi-region load balancing strategies, health checks, failover configurations, and achieving high availability across geographically distributed infrastructure.
Published on: HackerNoon
Explores confidential computing on Kubernetes using Google Cloud's Trusted Execution Environments (TEEs). Covers securing data in use, implementing confidential containers, hardware-based encryption, and building secure multi-tenant Kubernetes clusters with enhanced data protection.
Published on: HackerNoon
Security analysis of the GH0STEDIT attack vector targeting Docker container images. Reveals how attackers exploit Docker's layered filesystem to hide malicious code, detection techniques, and best practices for securing container image supply chains and preventing layer-based attacks.
IEEE Xplore
Presents an AI-driven framework for real-time API threat detection in cloud environments, combining Isolation Forest anomaly detection with SHAP for explainable, transparent security analytics. Achieves rapid detection, high precision and recall, and supports auditability and regulatory compliance. Demonstrates effectiveness in detecting credential stuffing, token abuse, and deprecated endpoint access with explainable decision-making.
TechRxiv Preprint
Introduces a policy-as-code framework for automated cloud misconfiguration remediation with human-centered explanations. The system detects violations, synthesizes minimal Terraform patches, and generates didactic diffs to show changes and rationale. It enforces safety invariants, supports rollback, and logs all actions for reproducibility. Evaluation shows an 8× speedup over manual remediation, eliminates over-remediation, and reduces violation recurrence by half through explanations—transforming remediation into a learning opportunity.
ACM Digital Library
Presents an AI-augmented cyber lab platform for hands-on, adaptive security training in cloud-native environments. The system integrates Large Language Models for semantic analysis of student artifacts and a Reinforcement Learning agent for dynamic instructional support, all deployed on Kubernetes with automated threat simulation. Evaluation demonstrates high accuracy in identifying misconfigurations (F1-score 0.92), effective adaptation to diverse learner personas, and strong expert ratings for pedagogical value, establishing a validated architecture for next-generation cybersecurity education.
IEEE Xplore
Proposes NetSage, a self-supervised learning framework for automated, real-time anomaly detection in encrypted network traffic using flow-level metadata and temporal features—without labeled data. NetSage employs novel pretext tasks, flow sequence prediction, and masked feature modeling to train deep models that capture complex behavioral patterns. Experiments on CICIDS2017 show NetSage outperforms traditional methods, achieving 96.3% accuracy, 2.1% false positive rate, and real-time inference, offering a scalable, adaptive solution for encrypted network monitoring in the era of widespread encryption and limited labeled data.
In: Trustworthy AI Systems: Engineering Secure, Scalable, and Responsible Intelligence for Real Applications (Springer)
Examines the foundations and practicalities of AI governance, focusing on accountability, transparency, fairness, and human oversight. Reviews frameworks like the NIST AI RMF and ISO standards, discusses ethical and societal implications, and provides real-world case studies and guidance for building trustworthy, responsible AI systems.
In: Revolutionizing the Cloud: Generative AI, Security, and Sustainability (Springer)
Explores the evolution of data privacy and governance in the era of AI and cloud computing. Reviews global privacy laws (GDPR, CCPA, DPDPA, PIPL), discusses privacy risks of AI in the cloud, and provides guidance on building secure, privacy-preserving, and responsible AI-cloud systems for the future.