The Power and Peril of Tool Use
When an LLM is given access to a "tool"—such as the ability to search the web or query a database—it gains the ability to impact your organization's infrastructure. While this enables powerful automation, it also introduces a massive new attack surface. If an attacker can manipulate the model's instructions (prompt injection), they can potentially use your agent's tools to exfiltrate data, delete records, or move laterally through your network.
Three Pillars of Secure Tool Use
- Least-Privilege Provisioning: Just as you wouldn't give every employee admin access to your production database, you shouldn't give every AI agent broad tool access. Provision agents only with the tools they need for their specific task.
- Runtime Policy Enforcement: Every tool call must be validated at the moment of execution. This is the core function of Shield Control. If an agent tries to use a tool in an unauthorized way, the request is blocked before the action occurs.
- Redacted Data Flows: Many tools handle PII or confidential data. Secure systems implement data filtering at the tool call boundary, ensuring that sensitive information is redacted or transformed before it is sent to external model providers.
How to govern agentic tool use
To govern tool use effectively, you need a central visibility point. You must be able to see every tool an agent has attempted to use, the parameters it provided, and whether the action was allowed or denied. This level of auditability is the prerequisite for moving autonomous AI into regulated production environments.
