Ghost in the Machine: Building an Open-Source Privacy Boundary for AI Agents
AI agents are useful precisely where the stakes are high: customer support, financial operations, clinical workflows, and internal tooling. That is also where they can accidentally send the wrong thing to an external model. A bank-transfer agent may receive a customer’s name, phone number, account number, BVN, or NIN. A support agent may handle email addresses and sensitive tool results. Most AI…
AI agents excel when handling sensitive information, such as customer data or financial transactions. However, they risk exposing this data when transmitting it to external models. To address this, Aegis is an open-source, inspectable data boundary designed for AI agents dealing with Nigerian-specific identifiers. This proxy sits between an application and an AI provider, replacing sensitive values with temporary placeholders.
When the response returns, the Reinjector restores these placeholders for the calling application, ensuring only placeholders are visible to the external model. This approach aims to reduce unnecessary data exposure while preserving evidence of what was transmitted. The project, released under Apache-2.0 license, focuses on providing a visible boundary for the "Ghost in the Machine" issue, where sensitive data silently follows an AI agent through a workflow.
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