AI agents run untrusted code. Here’s how to secure them using isolation, least privilege and proven cloud-native patterns.

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Running AI agents in cloud-native infrastructure introduces a new multi-tenancy security challenge: agents execute untrusted, LLM-generated code that can be influenced by adversarial prompts. The solution is applying proven defence-in-depth and least-privilege principles adapted for agentic workloads. Practical measures include running agents in isolated containers on dedicated nodes with separate Docker networks, scoped filesystem access, allowlist-based egress proxies, and hardened container configurations (user namespace remapping, dropped capabilities, disabled privilege escalation, resource limits). The core insight is that AI agent security is fundamentally a multi-tenancy problem, and the industry already has the tools — the challenge is applying them rigorously and consistently before a high-profile breach forces the issue.

The New Multi-Tenant Challenge: Securing AI Agents in Cloud-Native Infrastructure

Familiar Patterns in Unfamiliar Territory