LLM Capsule vs Prompt Security Gateways

Prompt security gateways sit between the user and the AI model, scanning prompts for sensitive content and filtering or blocking requests that contain flagged patterns. They operate at the API interaction layer. LLM Capsule operates as an AI enablement data layer — transforming the data itself before it enters any AI workflow, enabling AI adoption rather than blocking it.

Prompt gateways intercept API calls to LLM providers. They scan the prompt text for PII patterns, blocked topics, or compliance violations. If sensitive content is detected, the gateway either blocks the request, strips the flagged content, or alerts an administrator. Some gateways also scan AI responses for data leakage.

• Prompt-only scope. Gateways only see the final prompt, not the underlying data pipeline. Documents processed through RAG systems or batch pipelines may bypass the gateway entirely.
• Block or pass binary. Most gateways make a binary decision — block the request or pass it through. There is no mechanism to transform data while preserving usability.
• No output restoration. If a gateway strips sensitive content from a prompt, the AI output will lack that context. There is no restoration mechanism.
• Pattern-based detection. Gateways rely on pattern matching which misses context-dependent sensitivity and generates false positives on non-sensitive content that matches PII patterns.

LLM Capsule operates upstream of the prompt. It encapsulates sensitive data at the document level before any AI processing occurs. The encapsulation is structure-preserving — AI receives complete, processable documents. After AI generates outputs, local restoration (restoration) restores enterprise context automatically.