Guardrail
A compliance firewall for AI workflows. It sits between a model and the people who read its output, checks every response against written policy, and keeps a record of what it decided and why.
Problem
Enterprises are rushing to adopt AI, but without proper guardrails, outputs can expose sensitive data, introduce bias, or generate hallucinations that undermine trust. Regulators are tightening compliance standards, yet most organizations lack a practical way to monitor, log, and control AI behavior in real time.
That leaves a gap between innovation and governance, and it leaves businesses open to costly mistakes, reputational damage, and regulatory fines.
Approach
Guardrail proxies model calls and intercepts the response before it reaches an end user. Each response runs through safety and compliance checks (PII and PHI, toxicity, jailbreak attempts, and hallucination heuristics), and the policy decides one of four outcomes: allow, redact, block, or escalate.
- Policies are written in YAML, so behavior is versioned, reviewable, and auditable like any other code.
- Every decision is logged with a reason code and metadata, ready for a governance report.
- Designed as an adapter that can sit in front of any LLM or agent stack.
Live demo
This is a prototype, not a production system. It exists to test the idea and to show how I think about governance: write the rule down, enforce it automatically, and log the evidence.