{"resourceId":"itcnp-ai-fusion-lab-bedrock-sandbox-20261021","versions":[{"version":"external-d82e7b173a4e8428c81a1f8aec3252460927ceb6894f3c77784546c9b953cbae","resource":{"id":"itcnp-ai-fusion-lab-bedrock-sandbox-20261021","title":"IT-CNP's AI Fusion Lab offers a Bedrock-based sandbox for government teams to compare foundation models before production","organization":"IT-CNP (AWS partner), described on the AWS Public Sector Blog","sector":"Cross-government (federal and DoD focus, with state and local referenced)","geography":"United States","publishedAt":"October 21, 2026","publicationDate":"2026-10-21","eventDate":null,"sourceName":"AWS Public Sector Blog","sourceLabel":"Vendor/partner blog post","sourceUrl":"https://aws.amazon.com/blogs/publicsector/transforming-public-sector-ai-adoption-through-it-cnps-fusion-lab-and-aws-technologies/","evidenceClass":"vendor-claim","outcomeClass":"emerging","topics":["governance-procurement","infrastructure","data-security"],"finding":"An AWS partner blog describes IT-CNP's AI Fusion Lab, a sandbox using Amazon Bedrock and other services where government teams test foundation models without affecting live systems. It lists plug-and-play access to models including ChatGPT-4o, Claude 3.5 Sonnet, Titan and Gemini, and describes a FedRAMP High and FISMA-compliant environment. An IT-CNP director quoted says the lab is offered to federal and DoD customers and helps avoid being locked into one AI pathway.","sledRelevance":"Illustrates the sandbox-before-production pattern that state and local agencies can adopt for model selection, though the offering itself is pitched to federal and DoD customers.","evidence":"Partner marketing; compliance claims are self-reported; no customer outcomes, usage figures or evaluation results are given.","architectureImplications":"A segregated evaluation enclave with multi-model access lets agencies compare models on use-case inventories before committing; the named model list appears dated relative to current GovCloud offerings.","governanceImplications":"Supports evidence-based model selection and reduces lock-in, aligning with use-case inventory practices; agencies should still define their own evaluation criteria.","securityPrivacyImplications":"FedRAMP High and FISMA compliance are claimed for the environment; agencies must confirm authorization packages and whether real data may be used in the sandbox.","caveats":"Vendor/partner claim; federal and DoD focus limits direct SLED transferability; model list appears dated; no independent verification of compliance claims.","streamIds":["aws"],"roles":{"sales":"Sales framing: the useful idea here is the evaluation sandbox, not the specific partner offering. State and local buyers wary of lock-in respond well to a structured, low-risk way to compare models on their own use cases before committing. Use this post to discuss how a sandbox could fit the customer's AI use-case inventory, while being clear the post is partner marketing aimed at federal and defense customers and reports no outcomes from any agency.","engineering":"Engineering framing: an evaluation enclave should be isolated from production, support several models behind a common interface, and produce repeatable, exportable results. Build it with Bedrock model access, synthetic or approved test data, an evaluation harness scoring accuracy, safety and cost, and logging for audit. Check the current model catalog rather than the dated list in the post, and confirm which authorization boundary applies before loading any sensitive agency data into the sandbox.","delivery":"Delivery framing: a sandbox only produces value if evaluation outcomes feed decisions. Define the intake process from the use-case inventory, standard scoring criteria, and a decision record per use case that names the selected model and why. Assign an owner for refreshing models and test sets. Set rules on data allowed in the sandbox, and time-box each evaluation so the sandbox does not become an indefinite pilot without a path to production or retirement."},"roleDetails":{"sales":{"customerProblem":"Agencies lack a safe, structured way to compare foundation models before selecting one for production.","stakeholders":["Chief data or AI officer","Enterprise architect","CISO","Procurement lead"],"discoveryQuestions":["How do you select models for use cases today?","What data may be used in testing?","Do you maintain an AI use-case inventory?","What lock-in concerns do you have?"],"valueHypothesis":"A shared evaluation sandbox may shorten model-selection cycles and reduce lock-in risk, to be validated locally.","potentialEngagement":"Design and stand up an evaluation enclave with a standard scoring harness for two pilot use cases.","unsupportedClaims":["SLED customer outcomes (none reported)","Compliance claims beyond self-reporting","That the listed models are current"]},"engineering":{"fit":"Good fit for multi-model evaluation using Bedrock in an isolated account.","architectureIntegration":"Separate AWS account or enclave, Bedrock model access, evaluation harness, test data store and audit logging.","prerequisites":["Approved test data","Evaluation criteria","Isolated account structure"],"constraints":["Real data restrictions","Model catalog changes","Authorization boundary"],"security":"Isolate from production, restrict data classes, and log all model invocations.","proofOfValue":["Repeatable scores across runs","Cost per evaluation tracked","Decision record produced per use case"]},"delivery":{"work":"Establish sandbox, harness and intake process; run two evaluations.","dependencies":["Security approval","Use-case owners","Data stewards"],"ownership":"Chief data or AI office.","skillsAdoption":"Train use-case owners to define test sets and interpret scores.","governanceCheckpoints":["Data-use approval","Evaluation sign-off","Model selection decision record"],"acceptanceCriteria":["Proposed: two use cases evaluated within an agreed time box","Proposed: documented model selection rationale","Proposed: no restricted data in sandbox"],"risks":["Indefinite pilots","Outdated model catalog","Sandbox data leakage"]}},"retrievedAt":"2026-10-29T22:03:05-05:00","enrichedAt":"2026-10-29T22:16:30-05:00","enrichmentBasis":"retrieved source","accessibilityWorkforceImplications":"Sandboxes can give staff hands-on exposure to AI before production, but no training or workforce outcomes are reported.","procurementImplications":"Sandbox-as-a-service may be an entry procurement for model evaluation; contracts should specify data handling, model refresh cadence and exportable evaluation results.","operatingModelImplications":"Makes model evaluation a standing function separate from production delivery.","sourceVerification":{"openedUrl":"https://aws.amazon.com/blogs/publicsector/transforming-public-sector-ai-adoption-through-it-cnps-fusion-lab-and-aws-technologies/","referenceExcerpt":"The AI Fusion Lab is a secure, cloud-based sandbox where government teams can explore and test different FMs and AI applications","promptVersion":"sled-research-v4.0","model":null,"basis":"agent-reported inspection"}}}]}