EnforceGrid Enterprise
Enforcement infrastructure
for regulated enterprise AI.
Self-hosted in your environment — no shared compute, no inference data leaving your network. The Apache 2.0 Core enforcement engine with SSO, management plane RBAC, SIEM-integrated audit, and compliance reporting mapped to EU AI Act, OWASP, and NIST AI RMF obligations.
Deployable in your environment.
Auditable end to end.
The same Apache 2.0 Core enforcement engine — deployable in your own infrastructure, with the access control, audit integration, and compliance reporting your security and legal teams require.
Deployment
Self-hosted in your environment
Deploy via Docker or Helm into your own cloud account (AWS, Azure, GCP) or on-premise infrastructure. No shared compute. Inference data never leaves your network boundary.
Access control
SSO & management plane RBAC
SAML/OIDC integration for management plane login. Role-based access control on policy authoring, audit review, and tenant administration. Access to the audit trail is itself audit-logged.
Audit
SIEM-integrated audit chain
Hash-chained enforcement records push to your existing SIEM — Splunk, Microsoft Sentinel, IBM QRadar — via native webhooks. The tamper-evident chain is verifiable in your own infrastructure. No audit data routed through EnforceGrid.
Compliance reporting
Regulatory evidence packs
Pre-built evidence packages mapped to EU AI Act deployer obligations (Art. 9, 10, 11, 13, 14, 15, 26), OWASP Agentic AI ASI-01 through ASI-10, and NIST AI RMF. Updated as obligations evolve.
Implementation
Policy authoring & onboarding
Managed onboarding and custom policy library development mapped to your specific regulatory obligations. We work with your team until enforcement is running and your first audit package is ready.
Support
Direct access to the team
Not account management theater. You reach the engineers and policy authors building the product — the people who can answer a regulatory mapping question or unblock a deployment issue. We respond within one business day.
Built for scale · Written in Rust
<0.1ms
Enforcement overhead per request — Cedar eval, PII scan, and content detection. Enforcement cost is noise vs upstream LLM latency.
1,374+
Requests per second sustained with zero errors at 750 concurrent sessions, full enforcement pipeline active. Upstream LLM rate limits are the binding constraint before Steer saturates.
Rust
Written ground-up in Rust for high concurrency and minimal allocations. The enforcement hot path has no garbage collection pauses and no dynamic dispatch on the critical route.
Benchmarks run with Criterion.rs on Apple M-series (arm64). Throughput ceiling tested with k6, 60ms mock upstream, full enforcement enabled (Cedar + 5 content detectors + PII). Full methodology and raw results in BENCHMARKS.md in the open-source repo.
Start the conversation.
We respond within one business day. Tell us about your deployment environment, regulatory obligations, and timeline — we'll propose a structure that fits.