Deployment AI Mitigations

When deploying tools that will be shared across multiple AI agents, it is important to implement robust policies and controls on permissions for the tools. These controls include applying the principle of least privilege along with delegated access, where the tools receive the permissions, identities, and restrictions of the AI agent calling them. These configurations may be implemented either in MCP servers which connect the agents to the tools calling them or, in more complex cases, directly in the configuration files of the tool.

Deploying AI models to edge devices can increase the attack surface of the system. Consider serving models in the cloud to reduce the level of access the adversary has to the model. Also consider computing features in the cloud to prevent gray-box attacks, where an adversary has access to the model preprocessing methods.

Implement logging of inputs and outputs of deployed AI models. When deploying AI agents, implement logging of the intermediate steps of agentic actions and decisions, data access and tool use, installation commands, and identity of the agent. Monitoring logs can help to detect security threats and mitigate impacts.

Additionally, having logging enabled can discourage adversaries who want to remain undetected from utilizing AI resources.

Detect and block adversarial inputs or atypical queries that deviate from known benign behavior, exhibit behavior patterns observed in previous attacks or that come from potentially malicious IPs. Incorporate adversarial detection algorithms into the AI system prior to the AI model.