How to isolate AI agents that have access to company data

AI agents handling company data require isolation across identity, data retrieval, runtime execution, memory, networking, credentials, and audit evidence because hidden instructions, excessive permissions, or compromised credentials can lead to cross-system incidents. The recommended architecture assigns agents distinct, narrowly scoped identities; places deterministic policy brokers between models and data sources; uses short-lived, task-specific credentials; isolates risky code in ephemeral environments; partitions and governs retained memory; restricts network access; and maintains independent mechanisms to terminate runs and revoke access. Isolation levels should reflect the sensitivity of accessible data and the impact of permitted actions, ranging from permission-filtered retrieval for read-only assistants to dedicated boundaries and complete evidence trails for production agents. Northflank presents its microVM-backed sandboxes, workload identity, secret injection, private networking, RBAC, audit logs, and managed or bring-your-own-cloud deployment options as infrastructure controls, while emphasizing that applications remain responsible for record-level authorization, tool policy, memory governance, and data-access decisions.

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