As artificial intelligence continues to grow in sophistication, today’s leaders are striving to strike the right balance between innovation and security.
As artificial intelligence continues to grow in sophistication, today’s leaders are striving to strike the right balance between innovation and security.
Because at a time when governments and businesses want to exploit the potential of AI, data protection is becoming an absolute priority. AI models rely on analyzing vast volumes of information to detect patterns and make predictions, but the private data used to train them can also be at risk of misuse.
Intellectual property, customer information, sensitive data: the stakes are considerable.
To respond to this, more and more players are turning to private AI, an approach that makes it possible to take advantage of the power of artificial intelligence while maintaining control and confidentiality of data.
Private AI and public AI are not opposed to each other: they complement each other, according to needs. Where public models offer scalability and accessibility, private models are needed in the most sensitive environments, such as defense, aeronautics or healthcare, where national security and data privacy are crucial.
Five key benefits of private AI
1. Meet regulatory requirements
At a time when privacy and data security are top of mind for governments and businesses, a key driver of private AI adoption is regulatory compliance. This is particularly true for highly regulated sectors, such as aerospace, in constant interaction with the army, intelligence agencies or government subcontractors.
An example: a large European space agency implemented a scalable AI platform to improve the efficiency of its internal services. Based on a European language model hosted locally in its data center, this solution makes it possible to exploit vast volumes of data and documents while guaranteeing the security and compliance of the information processed.
2. Maintain control over sensitive and proprietary data
One of the great strengths of private AI is maintaining data sovereignty.
Rather than sending their internal information into public models, companies can define their own guardrails and control data flows within their AI systems.
This is the choice of the Portuguese MEO group, the country’s main telecom operator.
Faced with the growing complexity of regulations, its legal department spent countless hours reviewing volumes of confidential documents to respond to authorities’ requests.
The implementation of an internal AI assistant made it possible to automate the extraction and standardization of information necessary for regulatory responses, while preserving data confidentiality.
3. Strengthen customer and partner trust
Showing your commitment to data privacy and security has become an essential lever of trust, especially in sectors handling critical information. This is particularly the case in the infrastructure sector, where the construction, upkeep and maintenance of critical assets such as roads, bridges or airports directly affect public safety.
Ferrovial, a Spanish infrastructure group, has, for example, deployed a generative AI platform capable of orchestrating several intelligent agents to optimize the security and efficiency of its internal operations.
This approach made it possible to improve operational performance while guaranteeing the confidentiality of the data of its 24,000 employees, an essential requirement for a player involved in critical infrastructure projects.
4. Gain a competitive advantage
Developing and deploying your own AI systems internally can become a real strategic differentiator. By using their data in a secure and optimized way, companies gain speed, relevance and competitiveness.
This is the bet of Ventia, a key player in infrastructure services in Australia and New Zealand.
To reduce the time and resources spent preparing its calls for tenders, the company designed a GenAI solution, called Tendia, trained on its own submission archives.
Based on a RAG (Retrieval-Augmented Generation) framework, this AI is trained on the company’s submission archives. It now allows its teams to formulate responses in seconds, without compromising the confidentiality of internal data.
A time saving that frees teams to concentrate on what is essential: writing more creative and competitive proposals.
5. Reduce errors and improve data quality
In some cases, the primary goal of private AI is not so much to protect confidential data as to structure and make accessible vast collections of public data.
This is notably the case of the Italian Ministry of Culture, which developed an AI agent facilitating access to cultural heritage documents via a single portal connecting more than 6,500 libraries.
Thanks to GraphRAG technology and an interconnected knowledge architecture, the system can render precise information on cultural objects or historical events, with verifiable and sourced answers. Hosted in a secure and sovereign environment, the solution ensures that Italy’s cultural data remains on national territory.
Private AI, a responsible path for the future
As governments and businesses accelerate their investments in artificial intelligence, data privacy emerges as a pillar of trust and resilience. By combining innovation and data protection, this approach paves the way for the use of AI to be responsible, efficient and sustainable.
