AI agents are being deployed into production with tool access, database connections, and API keys — but without runtime security. The same agent that processes customer requests can be manipulated into exfiltrating data, executing unauthorized commands, or leaking credentials.
Traditional security tools weren't built for this. WAFs can't inspect LLM conversations. Static analysis can't catch prompt injection at runtime. And “just write a better system prompt” doesn't work when LLMs fundamentally cannot distinguish instructions from data.
Rune is runtime security purpose-built for AI agents. We scan every input, output, and tool call in real-time using a three-layer detection pipeline — pattern matching, semantic analysis, and LLM-based judgment. Policies are defined in YAML and enforced locally. Events are shipped to a real-time dashboard for monitoring and alerting.
Install the SDK, wrap your agent, deploy. Three lines of code. Under 10ms latency for pattern and semantic scanning. Works with LangChain, OpenAI, Anthropic, CrewAI, MCP, and any Python-based agent framework.
The dashboard shows per-agent risk scores, real-time alerts, event timelines, and policy enforcement. Route alerts to Slack, email, or webhooks. Tune policies from the UI without redeploying your agent.
We believe agent security should be transparent, not opaque. Our comparison pages include honest trade-offs — where competitors have genuine advantages and where Rune falls short. Our threat database publishes real attack payloads and detection strategies, not marketing generalities.
The SDK is open-source. The scanning runs locally in your infrastructure. We never store raw prompts or conversation content — only event metadata for monitoring.
Add runtime security in under 5 minutes. Free tier includes 10,000 events per month.