AI systems now call tools, retrieve data, persist instructions, execute workflows, and act across enterprise systems. Compare RAGuard with leading platforms to see where governed AI execution begins.
Controls trusted instructions, context access, tool execution, and audit evidence once AI systems start taking action.
Protect prompts, documents, and model interactions before or during generation.
Routes traffic, manages providers, and gives engineering teams observability and fallback.
Secures models, datasets, registries, pipelines, and enterprise AI asset posture.
Finds adversarial weaknesses and agentic exposure before the production runtime takes over.
RAGuard focuses on runtime governance: what an AI system is allowed to do, in this context, for this tenant, with this tool, using this instruction.
Detecting prompt injections, jailbreaks, unsafe content, and data leakage before they influence model behaviour.
Routing model traffic, observing requests, managing providers, and controlling cost and reliability.
Securing models, datasets, pipelines, registries, and AI assets across the broader MLSecOps estate.
Discovering how models and agentic systems can be manipulated before or outside production runtime.
| Platform | Primary Layer | Strongest Fit | RAGuard Difference |
|---|---|---|---|
| Lakera | Prompt defence | Prompt injection, jailbreaks, unsafe interactions | Governs agent actions, tool calls, and runtime policy |
| Portkey | AI gateway | Routing, observability, reliability, cost control | Adds governance and trust enforcement above infrastructure |
| Protect AI | MLSecOps | Lifecycle AI and ML security | Focuses on production agent behaviour and runtime controls |
| Microsoft Prompt Shields | Cloud-native prompt protection | Azure AI prompt and document attack detection | Vendor-neutral and action-oriented across clouds and tools |
| Gray Swan AI | Red teaming | Adversarial testing and attack discovery | Enforces policy continuously in production |
| Akto | API and agent exposure | API discovery, testing, MCP and agent posture | Governs permissions and runtime execution |
RAGuard mediates API access, tool execution, retrieved context, and runtime workflow decisions with tenant-aware policies.
Agentic threats often emerge across memory, retrieved content, tool output, and multi-step execution rather than one turn at a time.
Determine whether an instruction came from a trusted user, policy, retrieved document, memory artefact, or untrusted source.
Turn AI governance into enforceable runtime controls with records that support audits, assurance reviews, and internal policy checks.
Prompt defence versus runtime governance for teams moving from conversational apps into operational AI systems.
Open page →AI gateway infrastructure versus runtime trust boundaries, tool mediation, and governance evidence.
Open page →Lifecycle AI security versus runtime governance for autonomous systems acting in production.
Open page →Azure-native prompt protection versus vendor-neutral governance across clouds, models, tools, and tenants.
Open page →Adversarial AI red teaming versus continuous production mediation and policy enforcement.
Open page →API and agentic posture discovery versus runtime permissions, tool use governance, and action-level control.
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