Regulatory Sandboxes for AI: Global Lessons and Policy Considerations 

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Regulatory Sandboxes for AI: Global Lessons and Policy Considerations

I. Introduction

Governments and regulators have been grappling over how best to spur innovation while at the same time safeguarding public safety, consumer protection, and market stability in the age of digital technologies rapidly evolving all around us. Regulators increasingly lean toward approaches such as the so-called “regulatory sandbox.”

Regulatory sandboxes are legal frameworks that enable limited testing of innovations under regulatory supervision. They can be used to provide individualized legal guidance for innovators or to make legal exceptions for certain innovations to let innovators and the general public experience the effects of novel technologies in real life, “as if” these technologies had already been proven safe and effective.[1] These frameworks present an opportunity for innovators to test their products, services, or business models in a controlled environment, with such regulators overseeing the process.

Sandboxes allow regulators to update themselves with emerging technologies, thus helping them identify regulatory blind spots and take a proactive response toward risk.[2] For the innovators, the prescriptive establishment of sandboxes could build certainty and a roadmap for compliance, reducing regulatory uncertainties and time-to-market considerations. This means that the sandboxes promote innovation and serve the public interest.[3]

Due to its opacity, unpredictability, and scale of impact, Artificial Intelligence (AI) presents a unique regulatory challenge. AI slates stand as a promising mechanism to test algorithmic systems in real-world settings while securing their responsible development and deployment.[4] This paper investigates the evolution, global experiences, and policy design considerations for AI sandboxes.

II. Overview of Regulatory Sandboxes

Origin and evolution of regulatory sandboxes

Regulatory sandboxes were introduced in the aftermath of the 2008 global financial crisis, when regulators looked to promote innovations in fintech while ensuring financial stability was maintained.[5] The United Kingdom was the first to have an official sandbox set up by its Financial Conduct Authority (FCA) in 2015,[6] followed by many other jurisdictions, including Singapore, Australia, and Canada.

Key principles[7]:

  • Controlled Experimentation: Giving innovators the chance to test new technologies in the real world, but under very rigid conditions.
  • Regulatory Flexibility: Temporarily suspending or amending applicable regulatory standards.
  • Risk Mitigation: Ensuring there are safeguards to protect consumers as well as mitigate systemic risks.
  • Collaborative Learning: Encouraging co-learning between innovators and regulators.

Sectors where sandboxes have been implemented

  • Finance: Fintech solutions like digital payments, peer-to-peer lending, and robo-advisory.[8]
  • Healthcare: Digital health devices, AI diagnostic tools, telemedicine platforms.[9]
  • Mobility: Autonomous vehicles, drone delivery systems.[10]
  • Blockchain: Crypto assets, smart contracts, decentralized finance (DeFi).[11]

III. Global Experiences with Regulatory Sandboxes

Fintech as a Precedent

The most mature sandbox ecosystems have been developed around fintech as its disruptive innovation introduces new risks and requires a controlled environment to test new models and products which is provided by sandboxes through experimentation, promoting innovation for fintechs and allowing regulators to adapt policies to technological advancements[12]:

  1. United Kingdom (FCA): The FCA has offered its sandbox environment to over 700 firms, speeding up product launches and improving consumer outcomes.[13]
  2. Singapore (MAS): MAS has been using its sandbox to attract global fintech players and foster financial inclusion.[14]
  3. United Arab Emirates: In 2016, it established the ADGM RegLab to foster FinTech innovation within a controlled setting. It broke new ground in the Shariah-compliant digital finance space, drawing global startups to the MENA region.[15] The sandbox provided testing grounds for platforms like Yielders and Nester to trial Islamic finance and real estate investment models. This, in turn, promoted diverse growth in FinTech and positioned the UAE as a regional center for innovative regulatory interfaces.[16]

Broader Applications

  • Healthcare: To certify digital health software based upon developer accountability, the United States Food and Drug Administration has piloted a Software Precertification Program, which strays from the traditional premarket review procedure. It reduces the regulatory hurdles faced by developers of Software as a Medical Device (SaMD), i.e., software-only products that meet the Federal Food, Drug and Cosmetic Act definition of a medical device through up-front evaluation of a company’s processes and capabilities.[17]
  • Autonomous Vehicles: States such as California and even countries like the United Kingdom have launch-tested sandboxes for self-driving cars in open settings.[18] The primary limits of sandboxes include regulatory uncertainty, limited scope and scalability, resource intensity for authorities and potential for regulatory arbitrage.
  • Blockchain: Bahrain and Lithuania have put in place sandboxes for blockchain-based solutions[19] in an effort to stimulate their adoption while curbing the inherent financial risks. However, sometimes issues arise with sandboxes having overly burdensome restrictions that do not fit well within the blockchain and DeFi industries.

These global examples given of developed countries like USA, UK, California as well as developing countries like Bahrain and Lithuania illustrate some of the biggest challenges faced with regulatory sandboxes. Pilot-scale sandboxes may struggle to transition innovations towards national deployment, and therefore might not lead to a clear picture regarding population-scale deployment of technological solutions. The absence of legal sanctity for approvals granted under the sandbox may discourage investment. Additionally, the monitoring of compliance under sandbox regulations poses practical problems.

IV. Regulatory Sandboxes for AI: Opportunities and Challenges

Before engaging with the opportunities and challenges that are posed by the regulatory sandboxes for AI, it is imperative to brush over the concept of how sandboxes are used for the AI system. Essentially, sandboxes are controlled environments where AI systems can be developed, tested, and validated before being released to the market.

Opportunities

  • Responsible Experimentation: These sandboxes provide a low-risk setting for real-world testing of AI before deployment on a large scale.
  • Regulator-Industry Collaborations: Iterative feedback loops between developers and regulators can lead to more effective co-creation of regulatory frameworks.[20]
  • Evidence-Based Policymaking: Regulatory decisions can be taken based on data about the actual performance of the AI system and its societal impact.

Challenges

  • Data Privacy and Security: Real-world testing of AI in some instances requires the penetration of sensitive personal data, which might raise compliance-related issues under laws like the GDPR and the DPDP Act of India if the data is not anonymized before being used for testing. 
  • Regulatory Capture: Close collaboration between regulators and companies opens the possibility of biased policymaking or preferential treatment towards first-movers who can liaise with government stakeholders.

V. Designing an AI Regulatory Sandbox

What Best Practices Should AI Sandboxes Follow?

As countries explore the integration of regulatory sandboxes into their AI governance models, certain international best practices emerge as critical elements of successful design. For one, only AI systems adhering to the principles of responsible innovation, particularly those acting in high-impact sectors like healthcare, finance, or law enforcement, should be allowed into the sandbox. This provision makes sure that the sandbox is a sandbox for socially consequential technologies under regulatory supervision.

No less important is that the ethical governance structure be put in place with a view to performing its functions with full strength. This may be in the shape of an ethics oversight committee or a multi-stakeholder review panel charged with scrutinizing proposed AI models for technical soundness and their broader societal impacts. In essence, it is a safeguard against risks like algorithmic discrimination, breaches of data privacy, and opacity in human decision-making.

The other key principle for consideration concerns compatibility with global regulatory frameworks. Sandboxes need compatibility with existing international standards, including but not limited to the OECD AI Principles that stress values and transparency centered upon human beings, and the yet-to-be-approved EU AI Act, which ranks AI risks and imposes proportionate obligations. The idea of dovetailing with these norms further refines coherence in regulation and hence aids in therapeutic governance and collaboration across borders in AI.

Lastly, the sandbox must be established with clear exit strategies. When an AI model has tested satisfactorily for major ethical, legal, and technical parameters, it must be ushered out into a formal world of regulations, accepting it and letting go of regulatory limbo in order to nurture new innovations within legal certainty.

Case study: Singapore’s AI Governance Testing Framework

Singapore’s SMDA prepared a sandbox called “AI Verify”[21] aimed at transparency and accountability in AI. A company could run checks on its AI system against principles for responsible AI, and regulators would reshape their view of expectations with regard to explainability, robustness, and fairness.

Key components of trust, including robustness, explainability, fairness, and transparency, are the focus of the sandbox. In order to produce assurance reports for stakeholders, regulators, and customers, sandbox companies can voluntarily evaluate their models through a battery of technical tests and process checks. For example, a financial algorithm may be evaluated for user communication and decision logic clarity, while an AI-driven hiring tool may be examined for possible bias in candidate selection.

AI Verify is a co-learning platform in addition to a compliance tool. Both developers and regulators can improve their expectations through feedback loops. While developers receive early advice on how to adhere to ethical and legal standards, regulators obtain a better understanding of emerging technologies and real-world implementation challenges. This dynamic model illustrates how innovation and accountability can coexist in a regulatory sandbox.[22]

Case study: NATO’s Data Science and AI Sandbox, also known as SANDI

NATO’s Data Science and AI Sandbox (SANDI) serves as an example of how these frameworks can be customized for defense and security contexts. NATO members and affiliated organizations can test AI models in scenarios that mimic military and operational applications in a safe, cooperative environment provided by SANDI.

SANDI places a strong emphasis on mission-specific explainability, interoperability, and trustworthiness, especially in AI systems used for logistics, threat assessment, surveillance, and autonomous decision-making. The sandbox environment helps guarantee that AI technologies meet strict requirements for transparency and dependability prior to deployment, which is important given the delicate nature of defense use cases. An AI tool that forecasts battlefield logistics, for example, needs to be able to work accurately, give decision-makers understandable justifications, and work with systems from various NATO forces.

Additionally, SANDI encourages international cooperation, enabling its members to jointly create technical and ethical standards appropriate for high-risk settings. This is especially important in situations where people’s lives are on the line and public confidence in AI use needs to be gained and maintained. The SANDI model demonstrates that sandboxing provides a practical route to ethical and congruent AI development, even in security-sensitive domains.[23]

Designing AI Sandboxes for India:

Balancing Innovation and Consumer Protection: Regulatory sandboxes in India need to carefully balance promoting innovation with safeguarding the general welfare. No regulatory flexibility should come at the expense of consumer safety, data privacy, or equity, as demonstrated by SEBI’s innovation sandbox and RBI’s fintech sandbox.[24] AI sandboxes must function within the guidelines set forth by the upcoming Digital India Act and the Digital Personal Data Protection Act, 2023, to prevent algorithmic bias and exclusion and other societal harms from being exacerbated by experimental deployments.

Harmonization with Broader AI Governance: India’s AI sandboxes shouldn’t function independently. In order to ensure that sandboxed AI models can be responsibly mainstreamed, they must instead act as transitional spaces into larger legal and sectoral frameworks, such as regulations pertaining to health, finance, and education. The sandbox design should be in accordance with any upcoming AI legislation as well as national ethical frameworks such as the NITI Aayog’s Responsible AI for All strategy. Additionally, this would bring India’s domestic strategy into line with international regulatory trends like the OECD Principles or the EU AI Act.

Capacity Building: The lack of technical expertise among regulators is a significant obstacle to successful sandboxing in India. AI sandboxes should be accompanied by organized programs to train regulatory officials in AI oversight, similar to how SEBI and RBI have started hiring tech experts and setting up innovation units. Collaborations with academic establishments such as public policy think tanks, IITs, and IIITs can offer interdisciplinary inputs to help regulators become more proficient in impact assessment, data governance, and algorithmic accountability.

Cross-Border Coordination: Indian sandbox initiatives must collaborate with international counterparts to foster regulatory interoperability and mutual learning, considering the cross-jurisdictional nature of AI technologies. It is possible to establish common assessment criteria and compliance routes by utilizing India’s involvement in organizations such as the Global Partnership on AI (GPAI), the G20 Digital Economy Working Group, and its cooperation on digital governance with nations like the UK and Australia. This is particularly crucial for AI solutions created for cross-border populations that are multilingual, culturally diverse, and resource-constrained.[25]

Conclusion

Good regulatory sandboxes are an innovation to be cultivated, especially in the context of complex and dynamic technologies such as AI. While the fintech experience serves as a preceding model, AI-specific sandboxes confront greater ethical, legal, and societal stakes. Carefully curated and harmonized on an international level, AI sandboxes would provide the much-needed toolkit to regulators and innovators to responsibly and flexibly govern emerging technologies.

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[2] William Wright, David Schroh, Pascale Proulx, Alex Skaburskis and Brian Cort, “The Sandbox for analysis: concepts and methods” (ACM Digital Library, 22 April 2006) <https://dl.acm.org/doi/abs/10.1145/1124772.1124890> accessed 10 May 2025. 

[3] Michael Maass, Adam Sales, Benjamin Chung and Joshua Sunshine, “A systematic analysis of the science of sandboxing” (PeerJ Computer Science, 27 January 2016) <https://peerj.com/articles/cs-43/#table-6> accessed 7 May 2025.

[4] Michael Sammier, Deepak Garg, Derek Dreyer and Tadeusz Litak, “The high-level benefits of low-level sandboxing” (2019) 4 ACM Journal POPL, 1.

[5] Deirdre Ahern, “Regulation nurturing fintech innovation: global evolution of the regulatory sandbox as opportunity-based regulation” (2019) 15 Indian J.L. & Tech. 345.

[6] Joy Macknight, “FCA regulatory sandbox fosters disruptive innovation” (The Banker, 23 August 2016) <https://www.thebanker.com/content/68dc2356-128f-58ed-8e44-37d50489bc2b> accessed 10 May 2025.

[7] FinTech Department, “Enabling Framework for Regulatory Sandbox” (FIDC India, 28 February 2024) <https://www.fidcindia.org.in/wp-content/uploads/2019/06/RBI-ENABLING-FRAMEWORK-FOR-REGULATORY-SANDBOX-28-02-24.pdf> accessed 15 August 2025.

[8] Luke Scanlon, “FCA AI ‘sandboxing’ strategy a positive step for financial services” (Out-Law News, 13 June 2025) <https://www.pinsentmasons.com/out-law/news/fca-ai-sandboxing-strategy-financial-services#:~:text=The%20sandbox%20is%20designed%20to,scaffolding%20needed%20to%20innovate%20responsibly.> accessed 16 August 2025.

[9] Yingpen Qiu, Han Yao, Ping Ren, Xueqing Tian and Mao You, “Regulatory sandbox expansion: Exploring the leap from fintech to medical artificial intelligence” (2025) 1 Intelligent Oncology 2, 120.

[10] Ariane Fucci Wady and Flavia Luciane Consoni, “Regulatory sandbox for electric mobility: Enhancing charging infrastructure and innovation” (2025) 213 R&S Energy Reviews.

[11] Shuai Wang & Others, “Blockchain – Powered Parallel FinTech Regulatory Sandbox Based on the ACP Approach” (2020) 53 IFAC-PapersOnLine 5, 863.

[12] Ahmad Alaassar, Anne-Laure Mention and Tor Helge Aas, “Exploring a new incubation model for FinTechs: Regulatory sandboxes” (2021) 103 Technovation.

[13] Victoria Bell, “The FCA Reveals Project Innovate has Benefitted Almost 700 Firms” Compliance Consultancy (22 May 2019).

[14] ‘How Singapore’s FinTech Regulatory Sandbox is helping fintech innovators accelerate time to market’ EDB Singapore(22 August 2022).

[15] ADGM FSRA, “ADGM Reglab attracts record number of applications for its 3rd Cohort” ADGM News (4 June 2018).

[16] IBF Net Group, “Shariah Sandbox for Islamic Fintech Regulation?” (Medium, 9 October 2022) <https://ibfnet.medium.com/shariah-sandbox-for-islamic-fintech-regulation-91ceb770aa5a> accessed 2 July 2025.

[17] Monica R. Montanez, “Digital Health Software Pre-Certification Update: Final FDA Report Revealed” (Namsa, 28 September 2022) <https://namsa.com/resources/blog/digital-health-pre-cert-update-fda-final-report/#:~:text=On%20September%2026%2C%202022%2C%20the,track%20digital%20health%20products%20by> accessed 20 August 2025.

[18] Nynke E. Vellinga, “From the testing to the deployment of self-driving cars: Legal challenges to policymakers on the road ahead” (2017) 33 CL&SR 6, 847.

[19] ‘EC launches regulatory sandbox for blockchain projects’ Moody’s (14 February 2023).

[20] Jon Truby, Rafael Dean Brown, Imad Antoine Ibrahim and Oriol Caudevilla Parellada, “A Sandbox Approach to Regulating High-Risk AI Application” (2022) 13 European Journal of Risk Regulation 2, 270.

[21] AI Verify Foundation <https://aiverifyfoundation.sg/> accessed on 22 August 2025.

[22] “Singapore introduces the world’s first AI Governance Testing Framework and Toolkit” IndiaAI (Singapore, 30 May 2022).

[23] ‘Summary of NATO’s revised AI Strategy’ NATO (10 July 2024) <https://www.nato.int/cps/en/natohq/official_texts_227237.htm> accessed on 10 June 2025.

[24] Debajyoti Chakravarty, “Powering Fintech: The Case for Unified Regulatory Sandboxes in India” (ORF, 16 April 2025) <https://www.orfonline.org/expert-speak/powering-fintech-the-case-for-unified-regulatory-sandboxes-in-india> accessed on 2 July 2025.

[25] Sidar Yaksan, ‘Regulating AI with a Legal Policy Fuelled by Innovation and Accountability: Regulatory Sandboxes as the Way Forward’ (SSRN, 1 December 2024) <https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5068132> accessed 15 June 2025.