Enterprise & regulated environments
Guardrail API is being designed for organizations that have to treat AI as part of a regulated, auditable system—not a lab experiment. If you're responsible for AI safety, governance, or platform risk, this is the entry point for deeper technical and security conversations.
Tell us a bit about your enterprise AI environment and we'll follow up with next steps tailored to your security and governance needs.
Who typically talks to us
- Security & risk: CISOs, risk officers, and security architects looking for an AI-aware control that fits alongside firewalls, WAFs, and SIEM tooling.
- AI platform & engineering: teams operating shared LLM platforms for multiple business units, needing tenant isolation, quotas, and guardrails.
- Governance & compliance: legal, policy, and audit teams responsible for GDPR, EU AI Act, HIPAA, or internal AI usage policies.
Example deployment models
- Shared services firewall: Guardrail runs as an internal service. All LLM traffic from apps passes through Guardrail before reaching external providers.
- Regulated enclave: Guardrail sits inside a restricted environment with outbound-only access to model providers, enforcing strict policy packs and logging.
- Hybrid / multi-cloud: Guardrail fronts multiple model providers (e.g. OpenAI, Azure, GCP), normalizing decisions, headers, and audit events.
What an evaluation typically covers
- Current AI stack, key models, and critical workflows.
- Regulatory and policy constraints (GDPR, EU AI Act, HIPAA, internal governance).
- Threats you care about: prompt injection, data exfiltration, jailbreaks, model misuse, abuse of tools/agents, and audit requirements.
- How Guardrail's clarify-first model and dual-arm enforcement would fit into your existing controls and observability stack.
If you're at the "sketching the architecture" stage, reaching out early is useful. We can talk through reference patterns and how Guardrail is intended to behave before you commit to a specific deployment path.