How to Build a Security Control Plane to Contain Enterprise AI Agents

Article Details

Defense Focus

Contain enterprise AI agents by governing their identities and capabilities, isolating code execution, restricting network access, mediating tool and credential use, and independently authorizing and monitoring actions.

Detection Methods

• Maintain an inventory of agents, owners, models, tools, identities, credentials, data sources, network destinations, autonomy levels, and downstream systems; review it when capabilities or integrations change.
• Test deployed agents for prompt injection, tool abuse, credential harvesting, delegation, and production-boundary violations before deployment and repeatedly afterward.
• Collect and correlate audit telemetry across the context layer, agent, policy enforcement and decision points, approval system, credential broker, sandbox, tool gateway, network controls, and destination service.
• Monitor agent operations against authorization policies and approval requirements; retain evidence when retiring agents and revoking access.

Data Sources

• Agent inventory and capability/change records
• Context-layer and agent audit events
• Policy enforcement point and policy decision point logs
• Approval-system records
• Credential-broker and tool-gateway logs
• Sandbox and execution-environment telemetry
• Network-control and destination-service logs

Rule Types

• Policy-based authorization and action approval
• Network destination allowlists and default-deny egress controls
• Execution sandboxing and workload security policies
• Agent capability classification and change-control checks

Platforms

• Kubernetes
• gVisor
• Firecracker
• Salesforce Agentforce
• NIST AI Risk Management Framework

Defensive Actions

• Treat each agent as a software principal and inventory its full identity and delegation chain, permissions, tools, data access, network reach, autonomy, and deployment environment.
• Apply least privilege; constrain permissions, tools, credentials, and network destinations to the agent's actual task, with separate development and production accounts, networks, databases, and credentials.
• Run generated or untrusted code in isolated, ephemeral environments; use non-root execution, read-only filesystems, restricted mounts, dropped capabilities, resource limits, and controls such as seccomp or AppArmor.
• Use default-deny egress and destination allowlists; block unnecessary internet and internal-network access, while recognizing that approved services can still be misused to disclose data.
• Require independent policy authorization for proposed operations; do not rely on model refusals or sandboxing as substitutes for controlling legitimate API authority.
• Layer monitoring, approval, and recovery controls; maintain usable audit logs and immutable backups, and revoke identities, tokens, integrations, and sessions when agents are retired.
• Review agent capabilities and integrations before deployment and after material changes, including new MCP servers, shell execution, repository access, external communication, or write permissions.