From my experience designing and launching AI sandbox products for cloud environments, I’ve observed that organizations frequently overlook the critical guardrails required for safe and scalable GenAI experimentation in sandboxes. This often leads to failed GenAI prototypes, compliance risks, overwhelming technical burden, and uncontrolled costs. To address these challenges, I created the SANDPIT framework—a practical and actionable blueprint that outlines the critical areas every organization must master to run AI sandboxes responsibly and successfully.
- The Singapore Government’s Infocomm Media Development Authority created a GenAI sandbox in 2023 to evaluate AI products. Using this sandbox, GenAI use cases will be evaluated with upstream model developers (such as Amazon Web Services, Google, Microsoft, and Anthropic), application deployers (such as DataRobot, OCBC, Global Regulation Inc, and Singtel) and third-party testing teams (including Resaro.AI, Deloitte, EY and TÜV SÜD) to evaluate how various players come together to deliver an end-to-end GenAI application.
- The European Union (EU) AI Act (Article 57) requires all EU member states to establish at least one AI regulatory sandbox by 2026, to provide a safe environment to develop, train, and test innovative AI systems for regulatory compliance, before releasing it to the broader public.
- Harvard University provides AI sandboxes to enable faculty, students, and researchers to experiment with the latest large language models (LLMs) from OpenAI, Anthropic, Google, and Meta, while maintaining security and privacy controls.
- Mayo Clinic, one of the premier hospital systems in the U.S. uses ‘safe sandboxes’ to enable their staff to assess the applications of the latest GenAI technology (such as running simple queries to interpret a patient’s healthcare records and imaging data), while maintaining strict patient data privacy ahead of wider adoption.
To tighten cost and security guardrails, sandboxes should only expose the minimum set of resources necessary for users to complete their GenAI experiments. This means that all cloud services will be blocked by default in sandboxes and admins would need to maintain a curated list of allowed services such as virtual machines, log management, databases, and more. Further, admins should restrict the types of instances that can be provisioned (such as c5.4x large, g4dn.8xlarge, and more), so that no user accidentally spins up 15 different c5ad.24xlarge instances. The allow list should also indicate the specific cloud regions in which users can run sandbox experiments, so that organizations are able to comply with data residency requirements.
| The SANDPIT Framework | |
| S | Spend Controls |
| A | Access Restrictions & Governance |
| N | Near Production Parity |
| D | Defined Boundaries on Resource Use |
| P | Policy-Based Cleanup |
| I | In-built Observability |
| T | Time-Bound Environments |
S — Spend Controls:
Sandbox environments are susceptible to spend mismanagement, often resulting in silent draining of cloud budgets and surprise bills. This is because users, who only need sandbox accounts for the limited duration of the experiment, often forget to terminate cloud resources after use. To prevent cost overruns, organizations should establish preventive and corrective spend controls for every sandbox account from the get-go.
Abandoned sandbox accounts are a common byproduct of GenAI experimentation, leading to resource inefficiencies and cost sprawl. To control this risk, it is essential to have a policy-based rules engine that automatically decommissions resources when predefined time, cost, or quota limits are exceeded. Default resources—such as logging, security, cost reporting, and any additional services specified by the admin—should remain unaffected by the policy engine.
A — Access Restrictions & Governance:
GenAI experiments are typically short-lived, so the sandbox account lifecycle should be configured to align with the specific duration and objectives of each experiment. Sandbox accounts should be provisioned with an explicit end-of-life policy that defines expiration timelines, controlled extensions as needed, resource migration to other long-term accounts based on business needs, automatic resource cleanup, and eventual account closure.
N — Near Production Parity:
While organizations rigorously focus on isolating sandbox environments from production, they often overlook the need for those sandboxes to closely mirror production, in order to enable meaningful, real-world experimentation. Let’s take an example of a LLM-powered customer support product. In a production environment, this product might be using a specific tuned version of the LLM with defined token limits, context windows, safety guardrails, latency requirements, and tight integration with retrieval augmented generation (RAG) sources such as vector databases, email systems, and order management platforms. If sandbox environments fail to mirror this production setup, developers might inadvertently experiment with older model versions, higher token limits, simplified retrieval sources, relaxed safety controls, and reduced network latency constraints. As a result, the GenAI prototype used in the sandbox environment might appear successful in isolation but fail to scale in production—particularly from a memory consumption, compute requirements, and unit economics perspective.
D — Defined Boundaries on Resource Use:
As organizations rush to harness the full potential of Generative AI (GenAI), they are recognizing that responsible experimentation at scale requires a controlled approach. This strategic necessity is driving the widespread adoption of cloud-based GenAI sandboxes, that provide an isolated environment for learning, prototyping, and testing of GenAI products, before moving to production. Because of its foundational capability, cloud GenAI sandboxes have made its way into organizations of every size, industry, and geography. Here are a few examples:
P — Policy-Based Cleanup:
Sandbox account management can quickly get out of hand when organizations start scaling its use across various business units for different types of use cases – such as regulatory AI sandboxes, GenAI training labs, ethical AI hackathons, agentic product testing etc. It can go from 10s of accounts to 1000s of accounts within just a few months. Centralized sandbox visibility is a key part of effective sandbox management. A single pane of glass view should allow admins to track all active sandbox accounts, including details on user access, regions in use, cost centers assignment, quotas remaining, cost and time consumed, as well as possible security threats.
I — In-built Observability:
AI sandboxes have become indispensable and powerful engines for organizations to safely experiment, learn, and scale GenAI innovation. However, without intentional guardrails, these environments can quickly become unwieldly and difficult to govern at scale. The SANDPIT framework provides a structured blueprint for designing and operating AI sandboxes, to balance experimentation velocity with enterprise-grade control. By applying this framework, organizations can bridge the critical GenAI pilot-to-production gap, while maintaining security, cost efficiency, and operational discipline.
T — Time-Bound Environments:
Preventive controls include structuring sandbox accounts by cost center, automated tagging for all cloud resources, creation of spend budgets for every account, and proactive usage-based notifications. Corrective controls should be activated when budget thresholds are reached to automatically shut down active resources, restrict user access to resources, and prevent new resource provisioning.
Conclusion
By Rakshana Balakrishnan
One of the critical aspects of sandbox management is figuring out who gets access to the environments and for how long. Some use cases, such as GenAI prototype testing, may only need single-user access to a sandbox account. For other use cases such as Agentic AI hackathons, it may be necessary to allow multiple users to access a shared account as a team. Organizations need to set up a central governing workflow to empower account administrators to authorize user access to sandbox environments for single-user and shared accounts and automatically remove user access after the period of sandbox use.
