AI gateway, policy engine, MCP supply-chain protection, real-time DLP and cost control — consolidated into one auditable control plane that sees and governs every AI request as it happens.
Built in Helsinki for regulated European enterprises — no US Cloud Act exposure.Governing every model your teams already use
Watch requests, blocked attempts and DLP redactions in real time.
Learn more →Every principal mapped, connected and continuously risk-scored.
Learn more →Govern every Model Context Protocol server your agents touch.
Learn more →Discover, score and govern every AI agent and MCP server.
Learn more →Route any model through the gateway with one set of keys.
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Write a rule once — it protects every team, every model and every request, from a single control plane.
Every request from every model — chat, agents and MCP tool calls — routed through one inline enforcement point, so nothing reaches a provider unseen or unlogged.
Composable CEL policies evaluated at runtime, versioned and testable, so the same rule protects every team and every model without a single code change.
PII, secrets and source code detected and redacted in flight — on every prompt and every response — before a single token crosses your perimeter.
Trust graphs and lethal-trifecta detection across every Model Context Protocol server your agents reach, with policy enforced on each individual tool call.
Access certifications and the full lifecycle for every principal — human and non-human — with periodic reviews, attestations and one-click revocation.
Per-team token attribution, hard ceilings and a throttle mode that keeps enforcement running even after the budget is spent — security never lapses.
Route 2,000+ models and 500+ MCP servers & tools — OpenAI, Anthropic, Gemini, Mistral and any OpenAI-compatible endpoint — through a single control plane.
Built and operated in Finland, deployable as EU-hosted SaaS or fully on-premise — engineered against the regulations your auditors actually ask about.
Everything you need to know about the Roder control plane.
Spin up an EU-hosted trial in under an hour, or talk to the team that built it. Either way, you'll see every request by the end of the day.
How a single poisoned document turns a helpful agent into a data-exfiltration tool, and the three places you can break the chain.
An AI agent is rarely hacked the way a server is. It gets talked into it. The model reads attacker-controlled text, cannot tell it apart from your instructions, and helpfully does what it is told.
That is the uncomfortable core of indirect prompt injection. A language model has no reliable boundary between instructions and data. Everything it ingests, whether a web page, a PDF, a calendar invite or a Jira ticket, arrives as the same stream of tokens. Anyone who can write to a surface your agent reads can try to steer it. Google’s April 2026 sweep of the Common Crawl found injection payloads sitting in ordinary public pages, and reported a 32% rise in malicious attempts between November 2025 and February 2026. In June 2026, OWASP went further and described indirect prompt injection as a likely permanent architectural flaw, not a bug that a future model release quietly patches.
Security researcher Simon Willison named the pattern the lethal trifecta. An exfiltration only becomes possible when an agent has all three of these at once.
The attacker plants instructions where the agent will read them: white text in a PDF, a hidden HTML comment, a poisoned support ticket.
Your agent ingests the content to summarise or act on it. To the model, the hidden line is simply more context to follow.
The agent obeys the injected instruction and reaches for a tool it legitimately has, such as a file read, a database query or an API call.
It ships what it found through a channel that was never blocked, at machine speed, with no human in the loop.
In 2026 a fake Sentry error report hijacked more than 100 AI coding agents across enterprise teams, quietly exfiltrating AWS keys and GitHub tokens, with zero alerts raised. The agents did exactly what they were asked. That was the problem.
Because the model itself cannot be trusted to refuse, the control has to sit outside it. Meta’s safety team framed the goal as the Rule of Two. An unsupervised agent may hold any two of the trifecta properties, but never all three at once. Remove one leg and the exploit collapses.
That is exactly where an inline gateway earns its place. Roder breaks each leg independently. Real-time DLP redacts secrets and PII before they can leave, so the exfiltration vector carries nothing useful. Least-privilege policy on every tool call stops the pivot to a file read or network post the agent should never make. A live trust graph and injection detection flag untrusted content the moment it tries to act. Every decision is logged, signed and EU-resident.
If indirect prompt injection really is unpatchable at the model layer, then reaching for a better model is not a security strategy. The durable answer is to assume the model will be fooled, and to put the guarantees somewhere it cannot reach: at the gateway, on the request itself.
See how Roder breaks the lethal trifecta on every agent and MCP server, inline, signed and EU-resident.