As countries begin to treat AI as critical infrastructure, one of the most overlooked, yet essential, layers is privacy governance. OpenAI’s OpenAI for Countries (AI For Countries) initiative signals a new phase where national governments are no longer just regulating AI, but actively co-creating it.
As the public becomes more aware of data privacy laws and rights, business needs to get ahead of the curve in its ability to comply as well as to help consumers access their privacy rights.
AI for Countries is the perfect opportunity as it proposes building localized AI infrastructure, including customized ChatGPT models and data centers, to enhance healthcare, education and other sectors. The initiative expands beyond the U.S. to support other countries whereby OpenAI systems and software would become part of national AI infrastructure design and build-out and likely embedded into tech policies of national governments.
For OpenAI to succeed, they will need to make sure that data privacy stays enshrined as a right by instilling privacy freedoms. Data trust, accountability and agility will therefore need to be foundational to this project.
Privacy Principles
The freedom for people to choose how their personally identifiable information (PII) is shared and handled by private companies is one example of the long-standing democratic principles that OpenAI for Countries could help to protect.
Provisions for privacy rights are already key components of regulations such as China's Personal Information Protection Law (PIPL), Brazil's General Personal Data Protection Law (LGPD), the EU’s General Data Protection Regulation (GDPR), and most recently the European Union AI Act.
MineOS’s Gal Ringel: Governance as a security domain.
Countries joining AI for Countries can expect strict territorial data processing regulations, unique to each jurisdiction, as well as complex questions around data ownership, trust and accountability, especially when these models are trained on or deployed within sensitive, population-level contexts. Localizing large language models are not only about linguistic accuracy. LLMs require a deep understanding of each country’s regulatory landscape, cultural values and digital rights frameworks.
In the enterprise world, we’ve already seen how regional privacy laws can overlap, conflict and evolve quickly. The same complexity now exists at a national level. New AI-specific rules and a patchwork of existing frameworks not originally designed for AI – i.e., data localization mandates, consent laws, sector-specific privacy requirements – will govern new AI deployments.
“Governance from the Start”
Technology scale and agility are must-haves for global application. Organizations that treat AI data responsibly are those that embed governance from the start, knowing what data is used, where it is stored, who has access, and how its usage meets regulatory mandates and ethical expectations.
Language barriers in AI deployment aren't just a matter of translating interfaces; they are about enabling meaningful, rights-based interactions across linguistic and cultural contexts. Technology firms will need to support multilingual environments, ensuring that individuals can access, understand, and exercise their privacy rights in their own language. This is a prerequisite for inclusion, transparency and public trust.
Security and privacy are compounding forces. The strongest tech and security teams today are treating AI governance as any other critical security domain, knowing which models are in use, where data flows, who has access to it and how decisions are being made. It’s not glamorous work, but it’s foundational.
Guiding Frameworks
Governance need not be perfect, but it must be proactive, transparent and embedded into the core of how AI is built and deployed. Frameworks and processes like Records of Processing Activities, Data Protection Impact Assessment and Data Subject Requests will prove to be useful guides for building up AI governance.
If AI for Countries is to succeed in the broad and democratic distribution of AI, safeguarding personal information will need to happen with ease. OpenAI will need to demonstrate it has built-in frameworks that prioritize the management of privacy, risk and compliance according to each jurisdiction or nation-state they want to do business with. Only then will national partnerships follow and give way to trust with citizens and brand loyalty from consumers.
Ultimately, if the goal of the OpenAI for Countries initiative is to democratize the benefits of AI, then privacy can’t be seen as a barrier, but rather as part of the design.
Gal Ringel is co-founder and chief strategy officer of MineOS, a pioneer and global leader in data privacy and governance management. He has worked for years to empower individuals and enterprises with robust data privacy solutions that address the complexities of data governance in a rapidly changing regulatory environment.
