Cloud deployments are inevitable for any organization looking to expand its digital business in a cost-effective and sustainable way. The privacy and security concerns are no longer hindering the trend towards more cloud computing. Opting for a hybrid cloud allows organizations to leverage the size and scalability provided by the public cloud while addressing privacy, security, and compliance concerns through the integration of a private cloud component. Hybrid cloud and multicloud environments, like those being developed through the Strategic Cloud Initiative by EBRC, POST, Elgon and Digora, represent the Trusted Sovereign Path forward for any digital business.
Balancing Privacy and Unlimited Power:
Why settle for one when you can have both?
When it comes to AI, an extra concern arises, particularly when dealing with personal data. Organizations must exercise extra caution to comply with the data privacy and security regulations and address any potential concern surrounding this issue. AI has been the talk of the town for quite a while now, and it is generally known that powerful AI tools can unlock significant business opportunities.
However, for many organizations, accessing the size, scalability, and cost-effectiveness of the public cloud where these AI solutions thrive, remains a challenge. The public cloud, unfortunately, does precisely raise these concerns around privacy and security that organizations seek to avoid.
So, how can you combine the power of the public cloud while adhering to strict data privacy and security regulations, especially for personal data? One potential solution is to embrace Private AI. With Private AI, as promoted by providers like POST Group, organizations can maintain the privacy and security of sensitive data in a secure cloud environment. These data can still be used for analysis and for training analytic models to derive new insights and business opportunities even when such training requires substantial computing power.
Rescuing data privacy with synthetic data solutions
Private AI can combine these two seemingly opposite business goals by making use of synthetic data. These data are functionally identical to real data, but all synthetic data are created by AI and have no direct relation to private data. In other words: the data are a reflection of real data (like your image in a mirror) and cannot therefore be traced back to any individual personal data. Making use of these synthetic data, AI models can create syntactic data that can be used to train a model in the public cloud, with all the computing power needed but without having to invest in buying or leasing massive private clouds to realize this computational effort. Once an AI model is trained and validated, it can seamlessly transition to working with real data in a live environment.
Smaller companies that cannot afford to train their own models will eventually also benefit from such Private AI environments. Once AI models have been developed in this privacy-friendly way and have proven their worth, they can be set as base model to bring benefits
to a broader range of organizations. This democratization of AI, facilitated by Private AI offerings, makes advanced AI capabilities more accessible and affordable for organizations of all sizes and types.
Data quality: unlocking the potential of AI requires strategic preparation
Does this mean that AI has become a simple and straightforward tool, ready to be used by all at any time? Sadly, it doesn’t. Before you
can reap the benefits of AI, you will first need to prepare your data thoroughly. This means analyzing all data, classifying them, providing them with metadata and more. Only after this preparatory work are your data ready for use in any AI model. You also need to ensure that any future use of AI models will comply with the AI Act. This means you have to test your AI-based application with other models correctly, making sure that you application works with other models as a cross-check. Applying AI to your data will never be child's play.
Fortunately, there are AI and data specialists available to help you treat your data and AI models correctly. At the POST Group level, for instance, we have an entire team of data and AI consultants at your service, even when you are still in the early phase of figuring out how AI could be of use with your specific available data.
Maximize the opportunity, minimize the risk
AI is a huge opportunity. It can help you detect anomalies in huge sets of structured and unstructured data. Generative AI can
automate numerous tasks, enhancing the cost-effectiveness of your business. If you are interested in discovering the opportunities AI
has to offer without compromising your data’s safety, security and privacy, delving into our Private AI environment or similar offerings
is undoubtedly worthwhile.
