In an age where artificial intelligence (AI) has grown by leaps and bounds, how we handle and store the data that powers these AI systems has become a focal point of discussion. Enter Private AI. This concept ensures the privacy and security of the data and the AI models. In this post, we’ll explore Private AI and why many organisations build AI systems within their data centres instead of leveraging public cloud services.

What is Private AI?
At its core, Private AI is the practice of implementing AI solutions so that the privacy of data and the AI models themselves are preserved. It encompasses techniques such as differential privacy, federated learning, and encrypted computation to ensure that the information used in AI systems remains confidential and secure.

Why Opt for In-house AI Systems?
While public cloud services offer scalability and flexibility, there are several compelling reasons why organisations might choose to house their AI systems within their data centres:

  1. Data Privacy and Security: One of the primary reasons is data privacy. Organisations with sensitive data, be it financial, healthcare, or proprietary research, may be reluctant to host such data on third-party platforms. By keeping it in-house, they can have complete control over data access and security protocols.
  2. Regulatory Compliance: Many industries have strict data storage and transmission regulations. Housing AI systems in-house ensures that data never leaves the premises, making compliance with such regulations easier to maintain.
  3. Customisation and Control: Organisations can design infrastructure tailored to their needs with an in-house AI system. This customisation can improve performance, efficiency, and the ability to make rapid changes as needs evolve.
  4. Costs Over Time: While initial setup costs for in-house data centres can be high, over an extended period, they can be more cost-effective than continually paying for cloud services, especially for large enterprises with vast amounts of data.
  5. Latency Reduction: For AI applications that require real-time responses, such as financial trading systems, even a slight delay introduced by internet transmission to a public cloud can be detrimental. In-house systems can offer reduced latency.
  6. Intellectual Property Protection: For organisations involved in cutting-edge research and development, keeping their AI models and data in-house can be crucial for protecting their intellectual property.

Final Thoughts
Private AI represents a move towards giving organisations more control over their data and AI processes, ensuring that privacy and security are not compromised. While public cloud services will continue to be popular due to their convenience and scalability, there’s a growing trend of organisations going the in-house route for their AI needs. A desire for greater control, security, compliance, and, in some cases, cost efficiency, drives this shift.

Whether an organisation should opt for an in-house AI solution or use a public cloud service depends on its specific requirements and priorities. However, the rise of Private AI undoubtedly offers a valuable option for those prioritising security and privacy in their AI endeavours.