Safety, Guardrails, and Responsible AI

Why Safety Matters

LLMs can generate harmful, biased, or misleading content. Production applications need multiple layers of protection to ensure safe, reliable operation.

Input Guardrails

• Content filtering: Detect and block harmful/inappropriate inputs before they reach the LLM
• Injection detection: Watch for prompt injection attempts ("ignore previous instructions...")
• Input validation: Length limits, format validation, PII detection
• Rate limiting: Prevent abuse by limiting requests per user/IP

Output Guardrails

• Content classification: Scan outputs for harmful/inappropriate content before returning to users
• Factuality checking: Cross-reference claims against trusted sources
• Format validation: Ensure outputs match expected schemas
• PII redaction: Remove any personally identifiable information from outputs

System-Level Safety

• Principle of least privilege: Give agents minimal permissions needed
• Human-in-the-loop: Require approval for high-stakes actions
• Audit logging: Record all inputs, outputs, and tool calls
• Kill switches: Ability to immediately disable AI features
• Sandboxing: Isolate code execution and limit system access

Responsible AI Practices

• Transparency: Tell users when they're interacting with AI
• Bias testing: Evaluate model outputs across different demographics
• Red teaming: Actively try to break your system before users do
• Incident response: Have a plan for when things go wrong
• User feedback: Make it easy to report problems

🌼 Daisy+ in Action: Safety by Design

Daisy+ implements multiple safety layers: digital employees have Odoo access rights (they can only access data their role permits), system prompts include explicit boundaries ("never share financial data with unauthorized users"), the webhook system validates event signatures, and all AI actions are logged in the ERP's audit trail (chatter messages on records). Safety isn't an afterthought — it's baked into the architecture.