AI Governance

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LLM09:2025 Misinformation – The Silent Saboteur in LLM-Powered Enterprises

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In the digital-first world, Large Language Models (LLMs) such as OpenAI’s GPT series, Google’s Gemini, and Anthropic’s Claude are redefining how businesses operate. From automating customer service and accelerating legal research to generating strategic reports, LLMs are integrated into critical enterprise workflows.
LLM misinformation occurs when the model generates false or misleading information that appears credible. This is particularly dangerous because of the model’s inherent linguistic fluency—users often assume that well-phrased responses are factually correct.

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LLM04: Data and Model Poisoning – A C-Suite Imperative for AI Risk Mitigation

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At its core, data poisoning involves the deliberate manipulation of datasets used during the pre-training, fine-tuning, or embedding stages of an LLM’s lifecycle. The objective is often to introduce backdoors, degrade model performance, or inject bias—toxic, unethical, or otherwise damaging behaviour—into outputs.

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Weak Model Provenance: Trust Without Proof

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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 …

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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.