Stop Talking About AI. Start Making It Work for Your Business.

The conversation around AI security has, until now, been dominated by one major theme: data privacy. Business leaders are rightly concerned about whether their confidential data will be misused or leaked by AI providers. As we've discussed previously, this risk is manageable with the right contracts and deployment models.

But a new, more insidious threat is emerging, and it has nothing to do with a provider's privacy policy.

What if the biggest risk isn't the AI model itself, but the data you ask it to read? This new class of vulnerability, known as Indirect Prompt Injection, can turn your trusted AI assistant into an unwitting insider threat. This guide explains the risk in simple business terms and outlines the practical steps you need to take to protect your organisation. 

For any business leader exploring AI, data privacy is a primary concern. Headlines about security risks can create significant Fear, Uncertainty, and Doubt (FUD), making you hesitate to use powerful Large Language Models (LLMs) with your company's confidential information.

Let's be direct: for businesses, the widely discussed fear of a major provider like Microsoft or OpenAI misusing your data is largely a myth, backed by strong legal and technical protections. However, this doesn't mean there are no risks. Real, serious risks do exist—they just aren't the ones the headlines focus on.

We understand that the perception of risk among your team and customers is a business challenge in itself. This guide provides a practical framework to address those fears, separate the myths from reality, and focus on mitigating the risks that truly matter. 

You've decided that using AI will be useful to your business. Now you face a critical and confusing decision: which Large Language Model (LLM) should power your project? In a landscape dominated by names like ChatGPT, Claude, Gemini, and DeepSeek, choosing the right engine is crucial for success. Selecting the wrong one can lead to budget overruns, poor performance, or a solution that simply doesn’t meet your needs.

The technical choice is actually a strategic business decision. The guide below provides a clear comparison, focusing on the practical differences that matter most to your project's outcome and its ROI.