#Artificial Intelligence (AI) & Machine Learning

• Prompt-Injection-Resistant Enterprise Copilots: A Three-Layer Defense Framework
• Tiny Transformers on the Edge: How TinyML and Efficient AI Architectures Are Democratizing On-Device Intelligence for IoT
• Tiny On-Device LLMs: Privacy-First AI at the Edge for IoT and Smart Devices
• TinyML on the Edge: How micro-LLMs on consumer IoT devices enable private, real-time AI without cloud access
• Edge AI for Threat Detection: Privacy-Preserving On-Device Anomaly Detection Accelerates Zero-Trust Security in IoT
• Self-Sovereign AI for Wearables: A Privacy-First Data Economy with On-Device AI, Federated Learning, and Blockchain
• Tiny Transformers on the Edge: Practical pathways to private, low-latency AI inference for IoT and mobile devices
• Edge AI-powered digital twins for resilient smart cities: real-time optimization of energy, traffic, and public services
• TinyML at the Edge: A practical blueprint for on-device anomaly detection to secure IoT devices with privacy-preserving AI in 2025
• On-Device AI for Real-Time Threat Detection: Edge ML Strategies to Secure IoT Devices Without Cloud Latency
• Edge AI for Real-Time, Privacy-Preserving Inference on Mobile Devices
• On-device Federated Learning for IoT Security: A practical blueprint for privacy-preserving, real-time threat detection at the edge
• Federated On-Device AI for Zero-Trust Threat Detection in Cloud-Native and IoT Ecosystems
• AI-Generated Synthetic Health Data: A Privacy-Preserving Governance Blueprint
• Tiny LLMs on the Edge: A practical blueprint for running on-device AI in consumer IoT devices without cloud latency
• TinyML at the Edge: Privacy-preserving, energy-efficient on-device AI for wearables and mobile
• Security and Privacy in AI-Driven Digital Twins for Smart Cities: A Practical Guide
• PromptGuard: Practical framework for adversarial testing and runtime guardrails to secure LLM-powered applications against prompt injections
• Prompt Injection in Production LLM Security Tools: A Practical Mitigation Playbook for SOCs in 2025
• On-device Tiny LLMs: How distillation, quantization, and adapters unlock private, low-latency AI on IoT and edge networks in 2025
• Tiny Transformers on the Edge: A practical blueprint for running domain-specific LLM inference on smartphones and edge devices using quantization, pruning, and privacy-preserving techniques
• Prompt-Secure AI: Building an Enterprise Defense Playbook for LLM Deployments (2025)
• On-device AI for Wearables: Federated, Privacy-Preserving Anomaly Detection
• Autonomous AI Agents at the Edge for IoT: Architectures, Safety, and Developer Workflows
• TinyML for Healthcare: On-device, privacy-preserving diagnostic inference for rural clinics powered by edge AI
• Prompt Security in Production AI: A Practical Lifecycle for Defending LLM Deployments Against Jailbreaks, Data Leaks, and Prompt Injection
• Federated Learning at City Scale: Privacy-Preserving Edge AI for Real-Time Traffic Optimization
• Privacy-first On-Device AI: A Practical Blueprint for Transformers on Edge
• On-device Federated Learning for IoT Security: A Practical Blueprint for Lightweight Edge AIs to Detect Threats Without Transmitting Raw Data
• On-device Federated Learning for Consumer IoT: TinyML, Edge AI, and Privacy-First Personalization Without Cloud
• TinyML on the Edge: On-device Anomaly Detection and Autonomous Response for IoT Security
• Edge AI for Privacy-Preserving Personalization: A Practical Guide to On-Device Inference with Federated Learning and TinyML in 2025
• Prompt Injection in AI copilots: practical defenses for production stacks in 2025
• Zero-Trust AI in the Cloud: How post-quantum cryptography, attestation, and secure enclaves enable quantum-resistant, privacy-preserving ML inference at scale
• Federated Learning at the Edge: Privacy-Preserving AI for Real-Time IoT Security in 5G/6G Smart Cities
• From Cloud to Edge: TinyML-powered Transformers for Real-Time On-Device AI in Drones, Wearables, and Industrial Sensors
• TinyML on Microcontrollers: Building Privacy-Preserving On-Device AI for Smart Home Sensor Networks
• TinyML on the Edge: Deploying Ultra-efficient AI on Battery-Powered IoT in 2025 — From Compression to Federated Learning
• Federated Learning at the Edge: Privacy-Preserving Health Insights from Wearables