Sentinel sits between your app and its model, inspecting every prompt before it becomes an instruction.

An AI Firewall, inline

Sentinel scrubs untrusted content, prompt injection, jailbreaks, and data-exfiltration attempts, before your model sees it, and scans tool results before they return to an agent.

• HTML and hidden-content extraction, then encoding decode, normalization, fast-path regex, and semantic similarity
• PII and secret redaction, run independently of the threat score
• Package-hallucination checks that flag it when a model recommends a package name that isn't in the registry
• Every request resolves to one of four outcomes: clean, flagged, neutralized, or blocked

Four audiences, one enforcement point

Same product. Each group leans on a different part of it.

• Developers — one base URL swap, or one REST call
• Security / InfoSec teams — a single audit trail for AI usage org-wide
• Insurers / risk assessors — every request scored, categorized, and logged
• Compliance-driven orgs — PII and secret redaction before content reaches a cloud model

Any time the model reads content it didn't write

That includes, but isn't limited to:

• Web fetches and crawled content
• Retrieved chunks from a RAG knowledge base
• End-user input in a chat or support flow
• Tool outputs and MCP server responses, and third-party agent skills

It matters most for agentic sessions that take real actions, and for any regulated data heading toward a cloud model.

Inline, self-hosted or SaaS

Nothing reaches the model unscored.

• Self-hosted — a Docker Compose stack you run on your own infrastructure. Content never leaves your network.
• SaaS — hosted at sentinelaifirewall.com, nothing to run yourself
• Integration is a REST call to /v1/scrub, an SDK base-URL swap, or the Clawhub sentinel-proxy skill