What is AI-FW?

AI-FW is an AI firewall and governance gateway that sits between your AI agents and the large-language-model backends they call. Every prompt and response flows through the gateway, where it is inspected, scored against policy, routed to the right model, and audited, all in real time, with no agent-side code changes.

What it does#

Capability	What it means for you
Prompt inspection	Jailbreak and prompt-injection attempts are blocked, PII is masked, and your own rules are enforced before anything reaches the model.
Response inspection	Toxicity, leaked credentials, and data exfiltration (system prompts, internal IPs, private keys) are caught on the way back.
Model routing	Each request is routed to the right model and backend, with per-model API keys held server-side.
Semantic intent analysis	Traffic is scored against natural-language policies, not just matched against keywords.
Identity & access	Agents and users authenticate with API keys, JWT, mutual TLS, or Kerberos; RBAC scopes the admin surface.
Risk & audit	Rolling risk scores per user, agent, and IP; automatic blocking; a metadata-only audit log.
Reliability & caching	Per-model retries, failover, and load distribution, plus an opt-in completion cache.

Two surfaces#

AI-FW brings two integrated surfaces together:

AI-FW, the AI firewall & governance gateway. The traffic layer described above: an OpenAI-compatible and Anthropic-compatible endpoint that agents point at instead of the model provider directly.

Agent Trust, the agent registry and A2A protocol. A registry where AI agents onboard with an identity, discover services, and request PKI operations, certificate issuance, renewal, and revocation, over the Agent2Agent (A2A) task-based protocol. Agents authenticate with certificates (mTLS), and the same governance, risk, and audit machinery applies to them.

How it fits into your stack#

Your agents & tools (OpenAI SDKs, Claude Code, Cursor, MCP, custom agents)
                          │
                          ▼
              ┌───────────────────────┐
              │   AI-FW gateway     │   inspect → govern → route → audit
              └───────────────────────┘
                          │
                          ▼
     LLM backends (OpenAI, DeepSeek, Anthropic, Mistral, Groq,
                   OpenRouter, Ollama, vLLM, LM Studio, …)

Clients keep using their normal SDKs, the only change is the base URL. The gateway speaks the OpenAI chat-completions protocol and the Anthropic Messages protocol, and translates between them, so Claude-native tools can use OpenAI-style models and vice-versa.

The operating philosophy#

AI-FW is fail-closed by design:

• A scanner error or missing configuration blocks the request, it never forwards traffic unfiltered.
• The semantic tier blocks on any scoring error or timeout rather than passing a request through unjudged.
• New models start disabled until an administrator explicitly enables them.
• Raw prompt and response content is never persisted, the audit log keeps metadata and truncated previews only.

That means an outage or misconfiguration can never silently turn your AI pipeline into an ungoverned one.