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Introducing Generative AI as a Service: Data security in a world of AI

Tom Childs — Wed, 13 Aug 2025 07:00:00 +0000

Whether it’s helping you fine-tune an important email or create action figures of you and your pets, there’s 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?

What is AI?

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 like humans. Whilst the earliest iterations of AI were used to play chess and checkers in the early 20^th century, recent years have seen exponential growth in AI complexity and advancements in machine learning (ML).

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.

What are large language models (LLMs)

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.

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 But there’s a problem: LLMs can only use the data they’ve been trained on, which can be incomplete, inaccurate or outdated. When it doesn’t have the relevant data to produce the correct response, it’ll produce outputs that can be false, misleading or nonsensical but present them as factual and coherent. These “hallucinations” 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.

Retrieval-augmented generation (RAG)

RAG is a process by which the quality of LLMs’ responses is enhanced through specific data sources that can lie outside of the LLM’s training data. In short, the user’s 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.

RAG has many advantages over simpler GenAI models, notably that the results are far more accurate (fewer hallucinations) and that the LLM doesn’t need to be retrained each time it needs to be updated.

Data security & AI sovereignty

Regardless of how you utilise AI, the truth remains that AI uses data and the more data it has, the better it’ll perform. This raises important questions around the security of the data you upload. If it’s 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’s financial data to help you develop insights into company performance, is that an acceptable risk to take?

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.

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’s AI sovereignty whilst still harnessing the power of AI?

GenAI as a Service

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

Private and secure AI infrastructure
Enterprise-ready integration
Custom model selection
Retrieval-Augmented Generation (RAG) tuning
Advanced security layer through Identity Access Management (IAM)
A future-proof AI strategy

The post Introducing Generative AI as a Service: Data security in a world of AI appeared first on Service Express UK.

Adopting generative AI in the data centre

Jackie Kirkwood — Mon, 22 Jan 2024 17:14:14 +0000

As economic uncertainty continues, cost reduction remains an ever-present goal among global organisations. Adopting strategies to streamline processes, optimise spending and maintain resiliency is crucial for businesses within the IT and data centre space. With IT and finance departments at the forefront, developing and implementing efficient strategies are imperative.

AI: A tool for efficiency

Companies are feeling the pressure to drive efficiency through data and automation, where generative AI can be an effective solution. Many CIOs already implement generative AI by utilizing ChatGPT and other tools to enhance workstreams. According to a 2023 report by Freshworks, over 90% of IT directors and upper management use AI to support their work.

Nearly half of IT professionals agree that this technology significantly reduces their workloads, saving more than five hours per week, according to Freshworks. As software providers continue integrating AI capabilities into their offerings, now is the time for experimentation to discover where AI fits in your company strategy.

What is generative AI?

Generative AI is a subset of artificial intelligence that focuses on creating content in text, images, music or videos. At the core, generative AI models understand patterns in data and generate new, similar data based on what they’ve learned. This process isn’t about direct replication but about understanding the underlying structure and nuances of the input data to produce fresh, unique outputs.

Although generative AI is still in its early phases, its evolving capabilities can substantially change business operations. In the future, AI may write code, design new drugs, develop products and transform processes like supply chains.

Key components of generative AI:

Training: The model is exposed to vast amounts of data to understand patterns and variations within the data.
Generation: Once trained, the model can produce new content that reflects these patterns and structures.
Applications: Generative AI finds applications in various fields, from creating artwork to generating music, simulating realistic human voices and even designing innovative products.

How organisations can leverage generative AI

Generative AI can play several pivotal roles when it comes to IT organisations:

Automate content creation: IT companies with large-scale content creation can leverage generative AI to produce drafts or templates, speeding up content production.
Data augmentation: For organisations that work with machine learning, generative AI can create additional training data, enhancing the robustness of other models.
Simulation and testing: In software development and testing phases, generative AI can produce various user inputs or scenarios to test the robustness and flexibility of software solutions.
Innovative problem solving: Generative models can brainstorm and generate solutions or approaches that might not be immediately evident to human developers, opening doors to innovation.

Further, data centres are uniquely positioned to benefit from generative AI and advance operations through:

Predictive analytics that optimise power usage and network traffic, adding efficiencies and improving resource management
Enhanced security that detects issues and threats in real-time
Predictive maintenance and workload management that improves service delivery and minimises downtime

For example, predictive analytics can combat the substantial heat that computing demands produce. Operators can use AI to cool hardware more efficiently, reducing costs and improving energy efficiency. Predictive analytics can reduce infrastructure inefficiencies by fine-tuning power allocation and rack space to cut operational costs and improve power usage effectiveness (PUE).

Navigating forward: Strategic and cautious AI adoption

Cost reduction amidst economic constraints is a shared goal across the industry. With the right strategy, minimising costs while propelling business growth is possible. The journey begins with collaboration among stakeholders, an understanding of top priorities, and strategic AI integration to unlock value, surpass expectations and achieve business objectives.

Implementing generative AI in data centres not only presents an opportunity to enhance efficiency and reduce costs but also positions organisations to be more resilient and adaptable in facing future economic challenges. With careful planning and execution, AI can be a valuable ally in navigating economic downturns while ensuring data centres continue to operate efficiently and effectively. The IT industry must continue to evolve to support emerging data centre challenges and maximise generative AI’s potential.

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