Weak Model Provenance: Trust Without Proof A critical weakness in today’s AI model landscape is the lack of strong provenance mechanisms. While tools like Model Cards and accompanying documentation attempt to offer insight into a model’s architecture, training data, and intended use cases, they fall short of providing cryptographic or verifiable proof of the model’s …
As the adoption of Large Language Models (LLMs) accelerates across industries—from customer service to legal advisory, healthcare, and finance—supply chain integrity has emerged as a cornerstone for trustworthy, secure, and scalable AI deployment. Unlike traditional software development, the LLM supply chain encompasses training datasets, pre-trained models, fine-tuning techniques, and deployment infrastructures—all of which are susceptible to unique attack vectors.
Infrastructure as Code is a modern DevOps practice that codifies and manages IT infrastructure through version-controlled files. It enables consistent, repeatable, and scalable deployment of infrastructure resources.
The convergence of Agentic Artificial Intelligence (AI) with Blockchain technologies, particularly Hyperledger, and digital currencies such as the Digital Rupee and Digital Yuan, is set to redefine the future of enterprise operations, monetary systems, and global economic dynamics. As these technologies evolve, they offer unprecedented opportunities for strategic transformation, but they also carry complex implications that demand careful C-Suite attention.
As Agentic AI systems become more autonomous and integrated with critical digital infrastructure (like blockchain, smart contracts, and CBDCs), security testing becomes non-negotiable. Penetration testing is one of the most strategic tools to identify vulnerabilities before adversaries do.
Explainable AI refers to methods and techniques that make the decision-making processes of AI systems comprehensible to humans. Unlike traditional software with deterministic logic, most AI models learn patterns from data, making their internal workings difficult to understand.