JumpCube launches AI Gateway for regulated industries on AWS

JumpCube has launched an AI Gateway on Amazon Web Services to help banks, insurers and healthcare providers control how employees and copilots use generative AI. The platform centralizes governance over access, data, usage and spending as regulated companies move AI from pilots into production.

Why it matters: - Banks, insurers and healthcare providers are under pressure to use generative AI without exposing sensitive customer or patient data. - JumpCube’s AI Gateway is designed to reduce compliance risk, track spending and make AI usage visible across the organization. - The platform matters most for regulated companies that need governance before AI is deployed broadly.

What happened: - JumpCube launched its AI Gateway on Amazon Web Services through Amazon Bedrock. - The company describes the product as a governance platform that gives enterprises a single point of control over how employees and AI copilots use generative AI models. - The AI Gateway is available now on AWS to companies in the United States.

The details: - The platform sits between users and the AI models they access through Amazon Bedrock. - JumpCube built the system around four pillars: visibility, protection of sensitive information, control over data location and access, and value measurement. - IT and compliance teams can define what each copilot may do independently, what needs approval and what is blocked. - Automated checks flag credentials and sensitive data, with each finding confirmed by AI. - The platform tracks spending and unusual activity and alerts clients when usage falls outside the norm. - On usage-based plans, cloud spending is attributed to each person, team and model. - Each client gets a dashboard with up to 12 views covering adoption, productivity, costs and risks. - The dashboard shows counts and categories only, not the text of conversations. - Client data is stored in an isolated AWS environment with its own encryption keys and separation from other clients. - JumpCube keeps up to one year of organized history for audits and internal policies. - JumpCube does not use client data to train AI models. - Regulated organizations can deploy the platform in an AWS account dedicated to the client. - JumpCube says it tested the platform internally before offering it to customers.

Between the lines: - The launch reflects a shift in enterprise AI from experimentation to control, with governance becoming a core requirement rather than an add-on. - JumpCube is targeting the practical failures that show up in real workflows: sensitive data leakage, unclear adoption and runaway costs. - The company is also positioning AI oversight as an operations problem, not just a software deployment problem. - JumpCube pairs the platform with Forward Deployed Engineers who support assessment, implementation, governance and ongoing operations.

What's next: - JumpCube plans to keep serving regulated industries with AI implementations in financial services and healthcare. - In financial services, the company is focused on intelligent document processing, loan origination, customer onboarding and insurance claims. - In healthcare, JumpCube is focusing on prior authorizations, billing and patient communications. - The company’s support model suggests the platform will be maintained and adjusted after launch, not left to run on its own.

The bottom line: - JumpCube is betting that regulated enterprises want AI adoption, but only with tighter controls over what AI can see, do and spend.