Input and output enforcement
Inspect user prompts, retrieved context, model responses, tool arguments, and streamed output before unsafe content reaches the next hop.
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Runtime Guardrails
General Analysis observes runtime behavior across your agents, tools, and data flows—understanding context to detect what's malicious and what's not. Sub-10ms enforcement, full traces, live posture.
Last 24h · all production agents
Output quality vs baseline · support-agent
Watch every prompt, tool call, and response in real time. Context-aware policies catch what static rules cannot.
Block, flag, or escalate inline without slowing your AI stack down. Latency that disappears into the request budget.
Every decision captured with full context, ready for compliance reviews and incident postmortems.
Built for production
# Drop this in front of your agents. # Sub-10ms enforcement, full traces. agents: finance-agent: model: gpt-4o-mini policies: - prompt_injection - data_exfil_external - tool_allowlist tools: - query_db - search_docs on_block: page_oncall trace: full
Runtime coverage
Runtime Security sits in the request path for agents, assistants, and model endpoints. It enforces policy with the context needed to distinguish normal user behavior from jailbreaks, data leaks, and unsafe actions.
Inspect user prompts, retrieved context, model responses, tool arguments, and streamed output before unsafe content reaches the next hop.
Apply organization-specific policies for PII, secrets, jailbreaks, harassment, regulated advice, tool use, and business commitments.
Classify high-risk actions, enforce tool allowlists, require approvals, and stop destructive or out-of-scope operations inline.
Capture traces, decisions, latency, policy hits, and drift signals so engineering and security teams can investigate incidents quickly.
Deploy API wrappers or SDK middleware around model calls, agent loops, MCP servers, and high-risk tools without rewriting the application.
Evaluate request intent, retrieved material, tool arguments, model output, user role, and current policy before choosing an action.
Stop unsafe flows, redact sensitive data, require human approval, or route suspicious sessions to security workflows.
Feed confirmed incidents and adversarial findings back into policies and detection models so coverage improves as the system changes.