Learn how retina scan security works, the privacy risks of biometric authentication, template protection, anti-spoofing controls, and biometric data protection.
Learn secure data destruction methods based on NIST SP 800-88 Rev. 2, including data sanitization, cryptographic erase, SSD sanitization, degaussing, and physical destruction.
Build practical AI infrastructure skills across 12 hands-on stages covering Kubernetes, NVIDIA GPUs, LLM inference, autoscaling, observability, security and more.
Learn how to build a bare metal GPU cloud for NVIDIA DGX SuperPOD with tenant isolation, Metal3, Ironic, Kubernetes, vCluster, Run:ai, dynamic GPU provisioning, and automated workload scheduling.
Learn how NVIDIA DGX SuperPOD architecture works by building a mini SuperPOD lab with VMs, Ansible, Kubernetes, NVIDIA GPU Operator, scheduling, and Mission Control concepts.
Compare inline vs out-of-band API security architecture, API gateway enforcement, eBPF monitoring, threat detection, latency, and deployment trade-offs.
Demystifying VeloCloud: A Comprehensive Guide to Licensing, Support and BOQ As enterprises move away from rigid legacy WAN architectures, SD-WAN has become an important part of modern network transformation. VeloCloud SD-WAN provides organizations with a flexible way to connect branches, data centers, cloud environments, and remote locations while improving application performance and network visibility. However,
A Comprehensive Technical Deep-Dive into Passive vs. Inline Decryption, Modern TLS 1.3 Challenges,Offloading ROI, and Security Tool Acceleration. Introduction to Enterprise TLS Visibility Enterprise networks carry more encrypted traffic than ever. As a result, security teams face a difficult visibility challenge. Encryption protects data integrity and user privacy. However, it can also hide malware, command-and-control
AI-powered firewall troubleshooting can transform how infrastructure teams investigate complex network security incidents. However, building a reliable troubleshooting platform requires more than simply adding an LLM to existing network data. This field report explains how I built an AI-assisted network troubleshooting pipeline using NSX-T, VRNI, Neo4j, vector search, reranking, Redis, machine learning, and LLMs. The
