Runtime AI Data
Prompts, inference inputs, RAG content, vector database records, and persistent agent memory are the data your AI systems handle in the moment — and prompt injection, RAG poisoning, and memory tampering target exactly that layer.
This category covers prompt-injection defense, RAG sanitization, and AI-aware DLP for the runtime data flowing through every model call — the layer where most user-facing AI incidents actually happen.
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What Runtime AI Data covers
The subcategories, in practitioner terms.
Prompt-injection & jailbreak defense
Detection and blocking of adversarial inputs targeting the model at runtime.
RAG & vector DB protection
Access control and sanitization for retrieval content and the vector stores holding it.
Persistent memory integrity
Defenses against memory-poisoning attacks that survive across sessions or model swaps.
Compliance mappingMaps to NIST IR 8596's runtime prompt and inference-data concepts, the CSA AI Controls Matrix's Data Security and Application & Interface Security domains, the OWASP LLM Top 10's prompt-injection and sensitive-information-disclosure risks, the OWASP Agentic Security Top 10's memory & context poisoning, and MITRE ATLAS's prompt-injection and system-prompt-extraction techniques.
Category structure adapted from the AI Defense Matrix by Lenny Zeltser and Sounil Yu, licensed CC BY-SA 4.0. For a broader view of the vendor landscape, see the AI Defense Matrix Catalog.
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