{"id":77617,"date":"2025-08-13T08:00:00","date_gmt":"2025-08-13T07:00:00","guid":{"rendered":"https:\/\/serviceexpress.com\/uk\/?p=77617"},"modified":"2025-08-11T15:37:01","modified_gmt":"2025-08-11T14:37:01","slug":"introducing-generative-ai-as-a-service-data-security-in-a-world-of-ai","status":"publish","type":"post","link":"https:\/\/serviceexpress.com\/uk\/resources\/introducing-generative-ai-as-a-service-data-security-in-a-world-of-ai\/","title":{"rendered":"Introducing Generative AI as a Service: Data security in a world of AI"},"content":{"rendered":"\n

Whether it\u2019s helping you fine-tune an important email or create action figures of you and your pets, there\u2019s no doubt that artificial intelligence (AI) is becoming an integral part of our lives. But what about the data we upload? How safe is it? Who else can access it?<\/p>\n\n\n\n

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What is AI?<\/h2>\n\n\n\n

Simply put, AI is a field of science that explores the combination of data, math and computing power to enable machines to think, act, and, most significantly, learn<\/strong> like humans. Whilst the earliest iterations of AI were used to play chess and checkers in the early 20th<\/sup> century, recent years have seen exponential growth in AI complexity and advancements in machine learning (ML). <\/p>\n\n\n\n

ML is a sub-field of AI focusing on algorithms enabling systems to learn from data. For example, Netflix recommends a movie you might like based on your recent viewing history. <\/p>\n\n\n\n

What are large language models (LLMs)<\/h3>\n\n\n\n

LLMs are a specific type of ML model taught to specialise in processing and generating human-like content. Think ChatGPT and those cute photos of your dog as an astronaut. <\/p>\n\n\n\n

The key part of LLMs is the training. LLMs can be taught to write and talk like humans using vast data. They cannot think or know but can use learnt patterns to predict a suitable response to a prompt. But there\u2019s a problem: LLMs can only use the data they\u2019ve been trained on, which can be incomplete, inaccurate or outdated. When it doesn\u2019t have the relevant data to produce the correct response, it\u2019ll produce outputs that can be false, misleading or nonsensical but present them as factual and coherent. These \u201challucinations\u201d can lead to mistrust in the capability of your LLM and undermine the value of AI to your organisation. This is where retrieval-augmented generation (RAG) comes in. <\/p>\n\n\n\n

Retrieval-augmented generation (RAG)<\/h3>\n\n\n\n

RAG is a process by which the quality of LLMs\u2019 responses is enhanced through specific data sources that can lie outside of the LLM\u2019s training data. In short, the user\u2019s prompt is used to query the knowledge base (document, database, internet) and the relevant passages, documents and data are retrieved. The retrieved information is then combined with the original prompt to create an augmented prompt. The LLM users this augmented prompt to add additional context to its own internal knowledge and create a coherent response.  <\/p>\n\n\n\n

RAG has many advantages over simpler GenAI models, notably that the results are far more accurate (fewer hallucinations) and that the LLM doesn\u2019t need to be retrained each time it needs to be updated. <\/p>\n<\/div>\n\n\n\n

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Data security & AI sovereignty<\/h2>\n\n\n\n

Regardless of how you utilise AI, the truth remains that AI uses data and the more data it has, the better it\u2019ll perform. This raises important questions around the security of the data you upload. If it\u2019s a photo of your pet or an email to a friend, you can likely accept the risk. However, if you use a public LLM to upload your company\u2019s financial data to help you develop insights into company performance, is that an acceptable risk to take?<\/p>\n\n\n\n

AI sovereignty refers to an organisation’s control over its AI technologies, data, and the infrastructure used to develop and deploy them. Without the proper controls in place, sensitive data could be inappropriately uploaded to public AI models and could be repurposed or even shared with others. Such a compromise in your sovereignty could leave your organisation vulnerable to competitors, bad-actors or facing legal action over mishandling of data.  <\/p>\n\n\n\n

The need to independently create, manage, and utilise AI systems, aligning with local priorities, values, and security needs is growing in prevalence across all sectors. But can you protect your organisation\u2019s AI sovereignty whilst still harnessing the power of AI?  <\/p>\n<\/div>\n\n\n\n

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GenAI as a Service<\/h2>\n\n\n\n

We’ve built a service to help your organisation explore the benefits of AI in a way that\u2019s secure, private, and built to scale with you. It\u2019s designed to give you full control of your data while supporting you through: <\/p>\n\n\n\n