Guardrails are the protective systems wrapped around AI models to prevent harmful, inappropriate or off-topic outputs. Rather than relying solely on the model's own training to prevent problems, guardrails add external checks and constraints. Input guardrails screen incoming requests for harmful intent, prompt injection attempts or out-of-scope queries. Output guardrails check the model's responses before they reach the user, filtering for harmful content, personally identifiable information or policy violations. Topic guardrails keep the conversation within appropriate boundaries - your customer service AI shouldn't be offering medical advice, regardless of what the user asks. These are typically implemented as separate smaller models or rule-based systems that act as gatekeepers. Frameworks like NeMo Guardrails and Guardrails AI provide off-the-shelf tooling. For businesses, guardrails are not optional - they're a fundamental part of responsible AI deployment. The model itself is one layer of defence; guardrails provide additional layers that are more predictable, auditable and controllable. Think of them like input validation in traditional software: you wouldn't expose a database directly to user input without sanitisation, and you shouldn't expose an AI model without guardrails. Like many security approaches, the investment in building and maintaining good guardrails typically pays for itself many times over in prevented incidents.