Guardrails for Production AI: Lessons from Our First Year

One of the biggest concerns we hear from teams considering AI copilots is safety: 'What if the AI does something unexpected or harmful?' It's a valid concern, and one we've spent significant engineering effort addressing.

Types of Guardrails

We've implemented multiple layers of safety controls:

Input Validation

Before any user input reaches the AI model, we validate it against known attack patterns—prompt injection attempts, jailbreaking, attempts to extract training data, etc.

Action Constraints

Each copilot has a defined set of actions it can perform. Developers explicitly register these actions, and the AI cannot execute anything outside this allowlist. You can further restrict actions based on user roles and permissions.

Approval Workflows

For sensitive operations (like deleting data or making financial transactions), we support human-in-the-loop approval. The copilot proposes the action, but a human must confirm before execution.

Output Filtering

We scan all AI outputs for sensitive information—PII, API keys, internal URLs, etc.—and redact or block responses that contain them.

Real-World Examples

One customer uses Arcten to power a copilot in their financial application. They've configured guardrails so that any transaction over $10,000 requires manual approval, and any attempt to access another user's data is automatically blocked and logged.

Another customer in healthcare has strict HIPAA compliance requirements. They use our audit logging and output filtering to ensure no protected health information ever appears in copilot responses.

The Balance

The goal isn't to make the AI completely risk-free—that's impossible. The goal is to reduce risk to acceptable levels while maintaining utility. Too many guardrails and the copilot becomes useless; too few and you're playing with fire.