AI, Privacy, and Cross-Border

2026 is a turning point for AI and privacy, as organisations face a wave of new and tightening global regulations. The EU AI Act and GDPR are being updated, with the European Commission’s proposed digital omnibus reforms aiming to simplify compliance but also raising debate over potential impacts on privacy safeguards and AI development. China’s PIPL is intensifying enforcement and cross-border data controls, while India’s DPDP Act is now operational, setting strict standards for consent, data retention, and breach notification. In the US, the DOJ Final Rule restricts sensitive data transfers to “countries of concern,” elevating privacy to a national security issue. As global standards evolve, organisations must adapt quickly to remain compliant and competitive, and it is crucial to understand how these global shifts are reshaping the rules for data transfers and compliance.

Recent regulatory developments are transforming the way international data transfers are viewed and managed. In the United States, Executive Order 14117, which establishes the most comprehensive U.S. regime yet to restrict cross-border transfers and access to sensitive personal and government data, and similar laws are shifting the focus toward national security, reframing data transfers as critical compliance issues. The European Union is intensifying its scrutiny of data transfers, particularly to countries such as China and India. At the same time, the United States is putting stricter rules in place to control how sensitive personal and government data can be shared with certain foreign countries. For multinational organisations, these controls mean they must be extremely careful about where their data goes and who can access it. They need to put in place strong safeguards, carefully vet business partners, and keep up with fast-changing rules to avoid legal trouble and protect their reputation. As global consensus on data governance remains elusive, bilateral, and regional data agreements are expected to become increasingly common. Against this backdrop of tightening regulations, the role of AI in both creating and solving privacy challenges has never been more pronounced.

AI-driven data processing has introduced significant privacy risks, including the potential for re-identification, profiling, and bias. Yet, AI also serves as a powerful catalyst for strengthening compliance across the enterprise. Modern AI solutions automate routine compliance tasks such as regulatory monitoring, policy reviews, and audit trail generation, dramatically reducing manual effort and minimising human error. For example, AI-powered platforms can continuously scan regulatory sources for updates, flagging new obligations in real time and mapping them to internal policies. Machine learning models can detect anomalies in transactional data, helping compliance teams identify fraud, money laundering, or policy breaches before they escalate. Natural language processing enables rapid analysis of contracts and regulatory texts, supporting faster, more accurate decision-making. Additionally, AI-driven quality assurance (QA) tools can systematically evaluate machine learning models for bias by analysing input data, model predictions, and outcomes across demographic groups. These tools help organisations identify and mitigate unintended discrimination, ensuring that AI systems remain fair, transparent, and compliant with evolving regulatory standards.

AI further supports privacy-by-design initiatives by automating data classification, enforcing access controls, and facilitating transfer impact assessments for cross-border data flows. Predictive analytics and risk scoring tools empower organisations to proactively identify emerging compliance risks, while AI-enhanced whistleblower systems and automated reporting foster transparency and accountability. In high-risk sectors, agentic AI can streamline complex investigations, automate evidence collection, and ensure audit-ready documentation.

Despite these benefits, firms face persistent pain points. Legacy compliance systems often generate excessive false positives, overwhelming teams with alerts and burying critical signals. Integrating AI into fragmented data environments can be challenging, especially when scaling across multiple jurisdictions with divergent regulations. Many organisations struggle with explainability and transparency, as some AI models operate as “black boxes,” making it difficult to audit decisions or demonstrate compliance to regulators. There are also concerns about algorithmic bias, data leakage, and the need for robust governance frameworks to ensure ethical and responsible AI use. Finally, the rapid pace of regulatory change means compliance teams must continuously adapt, often with limited resources and expertise.

organisations must navigate the delicate balance between driving innovation through AI and ensuring responsible, compliant data use. Success depends on embedding AI governance, continuous monitoring, and human oversight into every stage of the compliance lifecycle. Looking ahead, new legislation is anticipated to specifically address the use of personal data by AI systems. AI governance frameworks are poised to become standard practice for organisations managing cross-border data risks. Companies will need to demonstrate their compliance with both emerging AI regulations and established privacy laws. While AI introduces new risks, it also unlocks innovative tools and approaches -particularly in privacy-by-design and the adoption of privacy-enhancing technologies (PETs) - that help organisations stay ahead of compliance demands.

PETs such as federated learning and homomorphic encryption are enabling compliant cross-border collaborations without the need to transfer raw data. These PETs are evolving beyond mere compliance tools, becoming strategic assets for secure data sharing, especially in sectors like AI, healthcare, and finance. Their adoption is expanding in both private and public domains, supporting cross-agency collaboration, and helping to build public trust. Despite these advancements, organisations continue to face significant hurdles in operationalising compliance, especially as they integrate AI into complex, multi-jurisdictional environments.

Across major jurisdictions, new and tightening legislation is anticipated to specifically address how AI systems use personal data. These laws will likely mandate risk assessments, transparency, and robust governance frameworks, with country-specific requirements for cross-border data flows, algorithmic accountability, and privacy-by-design. To effectively assess and mitigate cross-border AI privacy risks, organisations should prioritise robust data governance frameworks. Investing in PETs and AI-driven compliance solutions will help keep pace with evolving regulatory requirements. By integrating privacy and AI compliance into broader business strategies, organisations can secure a competitive advantage and foster greater consumer trust. To address these persistent challenges, embedding robust governance and proactive monitoring throughout the compliance lifecycle is becoming essential.

As organisations look ahead to 2026, several pain points are set to intensify. The rapid evolution of global privacy and AI regulations will create a complex compliance landscape, especially for firms operating across multiple jurisdictions. Many will struggle with integrating new requirements into legacy systems, managing the volume of regulatory changes, and ensuring transparency in AI-driven decisions. The risk of regulatory penalties, reputational damage, and operational disruption will rise for those unable to adapt quickly. To prepare, firms should prioritise building agile compliance frameworks that can accommodate frequent updates, invest in explainable AI and privacy-enhancing technologies, and foster a culture of continuous learning within compliance teams. Proactive engagement with regulators, regular risk assessments, and cross-functional collaboration will be essential to navigate uncertainty and maintain trust in an era where data governance is both a business imperative and a competitive differentiator. With these trends in mind, it is important to recognise the specific pain points that the 2026 outlook will bring - and to consider practical steps firms can take to prepare.

The convergence of AI, privacy concerns, and cross-border regulatory requirements is set to shape data strategy in 2026. Leading organisations have mobilised or are in the process of planning their 2026 AI and privacy programmes. Maintaining vigilance and adaptability will be essential as both regulatory and technological environments continue to evolve. Looking forward, the convergence of AI, privacy, and cross-border regulation will require organisations to adopt even more agile and transparent approaches to data governance.

In navigating this complex landscape, organisations can benefit from experienced partners who translate regulatory requirements into actionable strategies and foster a culture of responsible AI use. Baringa’s experience in advising organisations across sectors enables us to support clients in building agile compliance frameworks, integrating privacy-enhancing technologies, and fostering a culture of responsible AI use. By collaborating with clients, we help translate regulatory requirements into practical strategies - empowering organisations to adapt confidently, minimise risk, and unlock the full value of their data in a rapidly changing environment.