Tag: Artificial Intelligence
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What Enterprises Need to Know about Generative AI and Intellectual Property
The rise of generative AI has unlocked extraordinary possibilities for creativity, productivity, and automation across industries. From marketing content to software code and product designs, AI-generated outputs reshape how work gets done. But with this transformation comes a critical, often overlooked, concern: intellectual property (IP). As organisations integrate large language models (LLMs), image generators, and…
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Shadow AI: Identifying and Managing Unapproved LLM Usage in Teams
As generative AI tools become more accessible and powerful, many enterprise teams are experimenting with large language models (LLMs) like ChatGPT, Bard, and Claude to boost productivity, automate tasks, and generate insights. But with this surge in usage comes a rising risk: shadow AI. Just as “shadow IT” describes unsanctioned technology usage outside formal IT governance,…
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Augmenting Data Catalogs and Building a Smart Inventory of Enterprise Knowledge
As enterprises scale their digital operations, visibility into their data landscape becomes a strategic priority. Modern businesses generate and consume data across cloud platforms, legacy systems, APIs, and third-party tools, yet many struggle to answer a simple question: What data do we have, and where is it? This is where data catalogs come into play.…
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The Role of Semantic Layer in LLM-Ready Enterprise Data Stacks
As large language models (LLMs) become integrated into enterprise workflows, the focus has shifted from just building models to making enterprise data ready for them. One of the most critical enablers of this transformation is the semantic layer, a structured, business-friendly abstraction that sits between raw data and applications or models consuming it. Why Enterprises…
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Federated Learning in Enterprises: Training AI Without Moving Data
In the age of data privacy, cloud sprawl, and ever-tightening regulatory frameworks, enterprises are grappling with a fundamental tension: how to train AI systems on sensitive or geographically dispersed data without violating compliance mandates or introducing new security risks. Enter Federated Learning (FL), a paradigm shift in machine learning that enables model training across decentralised…