Your team is using Claude, ChatGPT, Gemini, or Grok. Without governance, one compromised connection, one tired engineer, one angry customer can break compliance or lose data. Guardrails makes it explicit: what this AI can access, what it's allowed to do, and how we prove it followed the rules. 45-minute workflow. Machine-readable policy. Works for startups and enterprises. Free. Open source.
Status: Phase 1 — live Launch: September 2026 Schema v1.0 — stable ontology License: MIT
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Live sinceSept 10, 2026
Guardrails doesn't care which AI you use — it cares what it can access and what it's allowed to do. That includes AI you didn't realize you had.
| Where it shows up | Examples | What it's typically used for |
|---|---|---|
| Direct AI tools | Claude, ChatGPT, Gemini, Copilot, Grok, Perplexity | Drafting, analysis, code, research, customer support |
| Embedded in tools you already own | Salesforce Einstein, Microsoft 365 Copilot, Zendesk AI, GitHub Copilot | CRM predictions, meeting summaries, support answers, code suggestions |
| Internally built systems | Custom agents, RAG systems, API calls to Claude or OpenAI | Document review, incident response, forecasting bots |
Six ways this is happening right now, inside companies that never decided to take these risks.
Production database down. Panicked. Asks Claude to fix it. Claude has prod credentials. No guardrails. Database corrupted. Nobody knows: Claude did it? DBA did it? No audit trail.
Uploads 50 applicant résumés to ChatGPT to screen candidates. Personal ChatGPT account. No governance. Applicant data now on OpenAI servers. GDPR violation.
Pastes unreleased financial projections into Claude to "sense check" the forecast. Quarter-end projections are material non-public information. Insider trading exposure. Regulator liability.
Customer is furious. AI suggests an exception to company policy. Support agent accepts it. Now the company honored a policy exception. Customer expects it. Company liable.
Deployed to handle Sev-1 incidents. Has prod access. Incident occurs. Agent attempts action outside its intended scope. Security controls didn't exist. System compromised.
Critical infrastructure AI should never modify security settings. Attacker compromises the AI's infrastructure. AI gets weaponized. Operator can't tell if it's been exploited. No audit trail.
The AI actor's information access or authority exceeds the controls appropriate to the risk.
AI authority must be explicit, bounded, observable, and proportionate to risk.
What AI systems does your company use? What can they access? What are they allowed to do?
How do we actually enforce those boundaries? In the system itself, not just in policy?
When something happens, can we audit exactly what occurred, who authorized it, and whether controls worked?
Same ladder works for code changes, financial decisions, hiring recommendations, manufacturing operations, critical infrastructure, and everything else.
| Level | Authority | Meaning |
|---|---|---|
| 0 | Observe | Access approved information; no derived action |
| 1 | Analyze | Interpret, classify, summarize, or diagnose |
| 2 | Recommend | Propose a decision or course of action |
| 3 | Prepare | Create an artifact/change that cannot take effect without another actor |
| 4 | Execute Bounded | Perform predefined, reversible actions within explicit constraints |
| 5 | Execute Gated | Perform higher-impact actions only when required conditions/approvals are satisfied |
| 6 | Prohibited | Action is unavailable to the AI actor regardless of instruction |
Source of truth. Every AI system your company uses, every use case, classified.
Human-readable baseline policy document, one section per use case.
What's missing? Prioritized by risk, with fix-by timelines.
How we prove this works: tests, owners, and review cadence.
You don't need a CTO or a security title to use this. You need to be worried about how AI is being used at your company and not know where to start.
Guardrails is the governance framework beneath NIST, ISO, and OWASP. Not replacing them. Implementing them. We turn abstract AI governance requirements into company-specific controls, enforcement rules, and audit evidence.
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