The Call for Global Artificial Intelligence Regulation
While international bodies debate ethical treaties and regulatory frameworks for artificial intelligence, public and private organizations face an immediate technical challenge: making their information systems auditable. Recently, Canada's ambassador to the United Nations (UN), David Lametti, advocated strongly for a global, secure, and equitable framework for AI. According to reports by The Globe and Mail, this diplomatic initiative aims to harmonize safety standards and prevent technological abuses on an international scale, building on discussions initiated at the G7 Summit.
However, implementing these ethical principles faces a pragmatic reality. For a Quebec municipality, a school board, or a local business, AI governance cannot be limited to signing a code of conduct. It must translate into concrete IT mechanisms capable of ensuring data sovereignty and complying with strict legislation such as Quebec's Law 25. The real challenge is no longer just knowing what AI should do, but being able to verify exactly what it is doing in real time.
From Abstract Ethics to Technical Auditability
To understand the obstacles to governable AI, we must analyze current software architectures. Most commercial solutions rely on centralized black boxes. When a user queries a conversational agent, the request passes through intermediate servers, often located abroad, without the organization being able to trace the exact path of the data. According to the annual report of the Communications Security Establishment Canada (CSE), relying on unverifiable cloud infrastructures centralized outside national borders exposes organizations to increased risks of data exfiltration and regulatory non-compliance.
Another major risk lies in the phenomenon of silent fallbacks. If an AI provider experiences an outage or server overload, the system may automatically redirect the query to another model or data centre, which is sometimes subject to extraterritorial laws such as the US CLOUD Act or Section 702 of the FISA law. For public institutions, this lack of direct control is incompatible with the obligation to conduct a privacy impact assessment (PIA), as prescribed by Quebec's Commission d'accès à l'information.
To address these vulnerabilities, scientific research is turning toward grounding techniques such as Retrieval-Augmented Generation (RAG). This method involves coupling a language model with an internal vector database. Thanks to embeddings, which are mathematical representations that capture the semantic meaning of texts, the system first searches for relevant documents within the organization before formulating a response. The AI no longer attempts to invent or draw from uncontrolled external sources: it relies exclusively on verifiable, local facts, drastically reducing the risk of hallucinations and information leaks.
Governance Built into the ProductivIA Architecture
The ProductivIA platform approaches AI governance not as an afterthought or a legal constraint, but as a pillar of its technical design. The platform's architecture is based on strict partitioning through logical silos. Each organization has its own sealed space, ensuring that session data, documents, and queries are never mixed with those of other entities. This partitioning prevents collateral leaks and ensures native compliance with Law 25 requirements.
This transparency is demonstrated concretely through the Nuage application. Unlike proprietary office suites where data storage remains opaque, ProductivIA's Nuage application offers total visibility over file structures and AI interactions. Every document ingested to feed the organization's memory is traceable. Users know precisely where their data resides and can export or delete it at any time, without intermediaries.
Furthermore, the platform categorically rejects the principle of silent fallbacks. Using the Comparateur IA application, administrators can transparently configure and test different language models. If a service becomes unavailable, the system raises an explicit error rather than passively redirecting queries to an unapproved third-party server. This rigour makes it possible to safely integrate sovereign AI engines like Matania, which is physically hosted in Quebec, ensuring that sensitive data never crosses national borders.
Toward a Sovereign and Verifiable Stack
The governance efforts initiated at the application level by ProductivIA take on full meaning when combined with the other layers of Quebec's sovereign ecosystem. For organizations wishing to control their entire technology stack, the alternative is to combine the hardware, the work environment, and the artificial intelligence engine.
By installing the native Boréal OS operating system on their computers, institutions free themselves from the mandatory telemetry of commercial systems and extend the useful life of their hardware. On this secure hardware foundation, ProductivIA's no-code application environment runs directly in the browser, while the sovereign Matania engine processes AI queries locally. This modular and transparent approach demonstrates that the safety and equity of AI, called for at the UN, are not distant utopias, but technical architecture choices accessible today.