Your GenAI, Your Data: How Local Models Put You Back in Control

When discussing GenAI one concern that consistently comes up is a worry around using public models and what happens to my data – there are genuine concerns about data privacy and security when using public AI models. Luckily there are solutions that address these concerns that have been around for quite a while – Ollama , and Open WebUI – tools that empower organisations to run AI models on their own infrastructure.

What is it?

Ollama is an open-source tool that allows users to run large language models (LLM’s) locally on their own hardware. This capability is particularly valuable for industries and individuals that require stringent data privacy. By enabling local deployment of GenAI models, Ollama ensures that sensitive data and prompts remain within the organisation’s control, mitigating risks associated with transmitting information to external servers.

What makes Ollama particularly powerful is its simplicity. With just a few command-line instructions, users can download, run, test and interact with sophisticated GenAI models. When paired with Open WebUI, a user-friendly interface for interacting with these models, even non-technical team members can leverage advanced AI capabilities without specialised training.

What does it mean from a business perspective?

Adopting local GenAI models with tools like Ollama offers several business advantages:

Enhanced Data Privacy: Reducing risk by keeping data on-premises reduces exposure to potential breaches.
Compliance Assurance: Local models help with adherence to industry-specific regulations and standards, simplifying compliance efforts.
Cost Predictability: By eliminating the need for continuous cloud service subscriptions or per token costs, businesses can optimise operational costs.
Operational Independence – No reliance on external API availability, reducing disruption risks from service or Internet outages.
Performance: With a local model the communications latency and response times are within your control.

What do I do with it?

To effectively integrate local AI models into your business, consider the following steps:

Invest in Infrastructure: Ensure your IT infrastructure can support the computational demands of running AI models locally. Do you need local servers (with GPU’s) or access to cloud VM’s with GPU’s? (It’s still a private model if you run it on a native cloud VM.)
Download and test Ollama: Visit the Ollama site, download models and test the functionality.
Develop Expertise: Build or hire a team with the necessary skills to manage and maintain local AI deployments.
Train Your Team: Ensure team members understand both the capabilities and limitations of local AI models to set appropriate expectations.
Implement Security Measures: Establish robust security protocols to protect your AI models and the data they process.
Monitor Performance: Track performance metrics and user feedback to continually refine your implementation strategy.

For organisations that prefer a fully managed AI experience with enterprise-grade security, Microsoft Copilot offers a compelling alternative. Copilot integrates seamlessly with Microsoft 365, ensuring compliance and data protection while delivering AI-powered insights across your desktop suite. Copilot operates with multiple protections, including:

Honouring existing Microsoft 365 permissions to help prevent data leaks between users and tenants.
Respecting encryption and usage rights applied through Microsoft Purview.
Implementing rigorous physical security and multi-layered encryption strategies.

By leveraging solutions like Ollama for local GenAI or Microsoft Copilot for a managed approach, businesses can confidently harness GenAI’s potential- without compromising one of the organisations most valuable assets – its data.

What is it?

What does it mean from a business perspective?

What do I do with it?

Further Reading and Links