AI-Agentic-RAG-Agentic-KrishnaG-CEO

AI Agents vs Agentic AI vs Agentic RAG: Demystifying the Next Frontier for Data Scientists

Artificial Intelligence has transformed from a theoretical construct into a practical powerhouse driving real-world applications across sectors. At the forefront of this revolution lies a new taxonomy of intelligent systems: AI Agents, Agentic AI, and the emergent concept of Agentic Retrieval-Augmented Generation (Agentic RAG). For data scientists—tasked with building intelligent systems, driving innovation, and ensuring scalable impact—it is crucial to differentiate and understand these evolving paradigms.

LLM-Unbound-KrishnaG-CEO

LLM10:2025 – Unbounded Consumption in LLM Applications: Business Risk, ROI, and Strategic Mitigation

At its core, Unbounded Consumption refers to an LLM application’s failure to impose constraints on inference usage—resulting in an open door for resource abuse. Unlike traditional software vulnerabilities that might involve code injection or data leakage, Unbounded Consumption exploits the operational behaviour of the model itself—by coercing it into performing an excessive number of inferences.

Weak-Model-Provenance-KrishnaG-CEO

Weak Model Provenance: Trust Without Proof

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 …

Continue

LLM-SCM-Vulnerabilities-KrishnaG-CEO

LLM03:2025 — Navigating Supply Chain Vulnerabilities in Large Language Model (LLM) Applications

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.

LLM-Integrity-KrishnaG-CEO

Secure System Configuration: Fortifying the Foundation of LLM Integrity

When deploying LLMs in enterprise environments, overlooking secure configuration practices can unintentionally expose sensitive backend logic, security parameters, or operational infrastructure. These misconfigurations—often subtle—can offer attackers or misinformed users unintended access to the LLM’s internal behaviour, leading to serious data leakage and system compromise.