RAGuard governs what enterprise AI systems are allowed to do in production. It mediates prompts, retrieved context, tool access, and policy decisions across agents, RAG pipelines, and MCP-enabled workflows in under 300ms.
Azure
MCP
Traditional tools evaluate risk one prompt at a time. But your AI isn't a chatbot — it's a stateful agent operating across sessions, tools, and time.
These aren't theoretical edge cases. They're the attack patterns active in production AI deployments today — invisible to tools built on stateless assumptions.
See how RAGuard stops themMalicious inputs embedded in user messages, retrieved docs, or tool outputs that hijack model behaviour at inference time.
PII, credentials, and proprietary data flowing through prompts and responses without detection or redaction.
Authority silently accumulated across multi-turn sessions — agents following instructions from sources never re-validated.
No audit trail. No evidence. No way to prove what your AI did, why it did it, or who authorised it.
Each turn is evaluated in isolation. Context accumulates undetected.
Every turn tracked. Intent provenance continuously validated.
RAGuard sits inline between your applications, models, tools, and workflows to inspect risk, mediate actions, enforce policy, and retain governance evidence. No model changes. No provider lock-in.
OpenAI/v1/chat/completionsAWS Bedrockconverse APIAzure OpenAIdeploymentsTwo-tier detection: fast regex patterns combined with ML classification (Meta Prompt Guard 2). Catches known attack patterns, encoded payloads, and novel injection attempts with configurable confidence thresholds.
Named Entity Recognition plus pattern matching detects and redacts PII (emails, SSNs, phone numbers, credit cards), unstructured entities (names, orgs, locations), and secrets (API keys, tokens, credentials).
Identifies harmful content across five categories: hate speech, insults, sexual content, violence, and misconduct. Per-category actions — block, mask, or log — are configurable to your product's risk tolerance.
Post-processing layer catches what the model itself failed to prevent: credential leakage in generated code, hallucinated PII in responses, and policy violations in model outputs before they reach your users.
Define granular, tenant-specific rules in Rego using Open Policy Agent (OPA). Control detection thresholds, feature flags, redaction rules, and rate limits. Version-controlled and instantly deployable like any infrastructure policy.
Every interaction is logged with immutable records, risk scores, and Zero-Knowledge Proof-based evidence bundles. Prove compliance to auditors without exposing interaction content. Designed for GDPR, HIPAA, and EU AI Act.
Two-tier detection: fast regex patterns combined with ML classification (Meta Prompt Guard 2). Catches known attack patterns, encoded payloads, and novel injection attempts with configurable confidence thresholds.
Identifies harmful content across five categories with per-category configurable actions.
NER plus pattern matching for PII, credentials, and secrets — bi-directional across prompts and responses.
Post-processing catches credential leakage in generated code, hallucinated PII, and policy violations in model outputs.
OPA/Rego tenant-specific rules, version-controlled and instantly deployable.
Zero-Knowledge Proof evidence bundles. Prove compliance without exposing content. Designed for GDPR, HIPAA, EU AI Act.
RAGuard requires no changes to your AI models, no renegotiation of provider contracts, and no infrastructure overhaul. Change one environment variable. Start protecting immediately.
Replace your LLM provider base URL with your RAGuard proxy endpoint. One environment variable.
Start with secure-by-default templates or define custom Rego policies for your tenant's risk profile.
Every interaction is logged, scored, and cryptographically attested. Your dashboard surfaces threats in real time.
Ship fast without inheriting security debt. Enterprise-grade controls at a price point that makes sense before Series A. Full feature access on the free tier — no capability limits, no surprises.
Learn moreSatisfy your CISO, pass your security review, and maintain the audit trail your compliance team needs — without slowing down your AI roadmap. SSO, SLAs, self-hosted deployment available.
Learn moreGDPR, HIPAA, and EU AI Act compliance with cryptographic proof. Know exactly what your AI processed, when, and why — and prove it to auditors without exposing a single sensitive record.
Learn moreExternal content, tool output, and cross-system actions all create runtime trust boundaries. RAGuard governs retrieved context, agent actions, and MCP tool access instead of stopping at prompt filtering.
Learn morePrompt defence, AI gateways, MLSecOps platforms, cloud-native shields, and red-team tools each solve a different layer. RAGuard is designed for governed AI behaviour in production.
Lakera protects AI conversations. RAGuard governs what agents are allowed to do after prompts become plans, tool calls, and workflow actions.
Read comparisonPortkey routes AI traffic. RAGuard adds runtime trust boundaries, tenant-aware policy enforcement, and governance evidence above infrastructure.
Read comparisonProtect AI secures the AI lifecycle. RAGuard focuses on production agent behaviour, tool execution, and runtime policy enforcement.
Read comparisonMicrosoft protects Azure AI workloads. RAGuard provides vendor-neutral runtime governance across models, clouds, tools, and tenants.
Read comparisonGray Swan discovers AI weaknesses. RAGuard enforces safe behaviour when production agents operate across tools, memory, and workflows.
Read comparisonAkto helps discover and test APIs and agentic exposure. RAGuard governs how AI agents use those APIs at runtime.
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