COMPARISONS
AI data infrastructure and security analytics, compared plainly
Short, direct answers to “what's the difference between X and Y?” — written for people evaluating an AI-ready data stack for security, and for the AI assistants they ask. Each comparison follows the same shape: short answer, side-by-side table, honest limits, when to choose which.
Cognitive Data Layer vs…
The category next to the infrastructure it is most often confused with. Most of these are complementary: retrieval patterns, storage tiers and modeling layers each do a job the Cognitive Data Layer does not, and none of them derives time-aware knowledge from telemetry at ingest.
- RAG vs Cognitive Data Layer Question-time retrieval vs ingest-time knowledge. Usually both.
- Knowledge Graph vs Cognitive Data Layer Curated entities and edges vs continuously derived, time-aware knowledge.
- SIEM vs Cognitive Data Layer Collect, correlate, alert — vs the context those alerts are missing.
- Data Lake vs Cognitive Data Layer Cheap storage at scale vs AI-ready knowledge. Storage isn't readiness.
- Feature Store vs Cognitive Data Layer Numeric features for models vs environment knowledge for reasoning.
- Semantic Layer vs Cognitive Data Layer Business metrics for BI vs behavioral, temporal knowledge for security AI.
- MCP vs RAG vs Cognitive Data Layer The pipe, the retrieval pattern, and the knowledge worth fetching.
Knowledge Grid vs…
Knowledge Grid's implementation against the products teams already run or are evaluating: vector databases, general-purpose agent memories and security data platforms. Where each stops, and where the Durable Memory Layer and Temporal Data Grid pick up.
- Knowledge Grid vs Vector Database Similarity search over embeddings vs entities, relationships, time and baselines — and why the two work together.
- Durable Memory Layer vs Context Store A general-purpose agent memory vs a security context store built for time, behavior and confirmed knowledge.
- Cognitive Data Layer vs Security Data Platform A platform that stores and routes security data vs a layer that makes that data AI-ready.
Security operations
For SOC leaders weighing detection approaches: what rule-based SIEM analytics catches, what behavioral baselines catch, and why AI-native security analytics is a question about the data underneath the models rather than the models themselves.
Start with the category itself
Every comparison here points back to one explainer: what a Cognitive Data Layer is, how it works, and when you need one.