One drop-in gateway endpoint makes your whole AI estate visible — every model, agent, tool, team, and dollar — and puts a safety net under how your people actually use AI: masking sensitive data, blocking prompt injection, and flagging risky flows, all inline.
median time from signup to first visibility
coverage of traffic through the gateway
data classes detected and masked inline
security checks on the request path
Teams adopt models, agents, and AI tools faster than any registry can track — 60% of enterprise AI is undocumented shadow AI.
Customer records, source code, and credentials flow into AI tools every day — invisible until the breach notification.
Attackers don’t hack your agents; they talk to them. Indirect injection through email, documents, and tool results is the new phishing.
Swap a base URL and keep your code. The gateway speaks the APIs your teams already use — and everything that flows through it becomes visible, secured, and governable.
Every model, agent, tool, and MCP connection registers itself from real traffic — shadow systems flagged the moment they touch the gateway, with usage, latency, and spend by team, project, model, and provider.
PII, secrets, API keys, source code, and customer records are detected and masked inline — before they leave your perimeter for any model or AI tool.
Multi-turn attack detection on every interaction — including poisoned tool results, malicious retrieved content, and indirect injection through MCP connections.
Prompt patterns, AI-assisted coding flows, tool adoption, and productivity signals — derived from gateway traffic, not from surveilling anyone’s editor.
Swap your base URL, keep your code. The gateway speaks the APIs your teams already use, with SDK hooks for LangGraph, CrewAI, AutoGen, and MCP — or runs as a plugin behind LiteLLM, Portkey, Kong, and Azure APIM.
Models, agents, tools, and MCP connections inventoried automatically from real traffic — with shadow AI surfaced by team, by app, and by data sensitivity, the moment it touches the gateway.
Requests, tokens, latency, and cost by team, project, model, and provider — live dashboards, scheduled reports, export anywhere.
PII, secrets, API keys, source code, and customer records detected and masked inline — before they leave your perimeter for a model or an AI tool.
Multi-turn attack detection on every interaction — including poisoned tool results and indirect injection through retrieved content and MCP connections. Findings stream to your SIEM as OCSF.
See how engineering actually uses AI — prompt patterns, AI-assisted coding flows, tool adoption, and productivity signals — from gateway traffic, not from surveilling anyone's editor.
No. IRIS8 runs as a drop-in endpoint or as a plugin behind LiteLLM, Portkey, Kong, or Azure APIM — whichever path is least disruptive.
Visibility is passive and security checks run inline in a sub-100ms budget at p95. Your users won’t notice; your dashboards will.
Yes. Most teams run a week of alert-only to tune policies, then flip to enforce per data class and team. Masking is format-preserving, so downstream tools keep working.
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