AI risk mitigation

LLM-SCM-Vulnerabilities-KrishnaG-CEO

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

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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.

Attack-Scenarios-Prompt-Injection-KrishnaG-CEO

🧠 Attack Scenarios and Risk Implications of Prompt Injection

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Prompt injection is not just a vulnerability — it’s a multi-headed threat vector. From overt attacks to inadvertent leakage, each scenario introduces unique risks, requiring tailored strategies to safeguard operational integrity, regulatory compliance, and business reputation.

Agentic-AI-IaC-KrishnaG-CEO

Agentic AI and Infrastructure as Code (IaC): Pioneering the Future of Autonomous Enterprise Technology

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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.

Explainable-AI-KrishnaG-CEO

Explainable AI (XAI): Building Trust, Transparency, and Tangible ROI in Enterprise AI

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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.

Prompt-Injection-LLM-KrishnaG-CEO

Prompt Injection in Large Language Models: A Critical Security Challenge for Enterprise AI

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Prompt injection occurs when malicious actors manipulate an LLM’s input to bypass security controls or extract unauthorised information. Unlike traditional software vulnerabilities, prompt injection exploits the fundamental way LLMs process and respond to natural language inputs.