The rise of Artificial Intelligence

The revolution in AI has been rapid and ground-breaking. Many corporations and individuals have rushed to try the new technology to both test and deploy. While the concepts behind the technology have been around for many decades, the combination of advanced purpose-built hardware with new software models trained on the content of the web has led to significant advances in speed and application. The pace of development has led to ever-shortening product cycles with many new applications and services entering the market on an almost weekly basis. The latest release of the most popular system, ChatGPT, now has its own ‘store’ where users can create their own AI applications without any coding experience. But what does it mean for both the individual and corporation? Particularly as regards risk and security. . 

For the casual user, if information is not private or particularly sensitive, and as long as it takes place away from work, then there is probably little risk. But for companies and certain individuals this is not the case. Serious security breaches have already occurred, from deep fake videos to the exposure of product plans for large corporations.


Enterprise AI

There are powerful new Enterprise grade options available from the large AI companies, but these too come with some specific features that may not fully match the needs of individuals or companies.. 

– Cloud based systems typically require client data to be moved off premise and actively managed.

– Secure access and performance via the internet.

– Unresolved Regulatory compliance issues, particularly with open source.

– Sustainability profile of solutions.

– Ownership and control of the underlying AI system. 

Whilst these points may not be of much concern, there is an alternative: private AI.


Private aI

This concept ensures the privacy and security of the data and the AI models. It typically does not leverage public AI services and data remains within organisations..

The advantages of this are:

– No exposure of proprietary information. – Existing in-house security protocols can be observed without additional expense in time or money.

– Intellectual Protection is easier to achieve.

– Regulatory compliance is easier to achieve. Whilst the law is evolving, ‘proof of source’ becomes simple, reducing management burdens at every level.

– Training models do not use Open-Source software which is subject to security and regulatory hurdles. .

Potential benefits:

Performance – System is highly tuned and less likely to produce ‘hallucinatory’ outputs. – No latency from internet access – No latency from Enterprise access which is still a shared resource

Sustainability – Models are less resource intensive reducing power requirements and emissions.

Costs – Long-term running costs are typically reduced. Whilst there may be some initial outlay costs the additional expense of active cloud management is not required

Your client security – Some clients may mandate some of these features which necessitates that your client data does not leave the premises or designated areas.

Ownership – Control and ownership reside with you. 

Secure private AI

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