AI Security Analyst
that understands YOUR infrastructure
Ask security questions in natural language. Get answers grounded in your real infrastructure data — not generic advice. Powered by Retrieval-Augmented Generation (RAG) over your Neo4j attack graph, CVE database, and compliance scores.
Available on Pro and Enterprise plans · Powered by Gemini 1.5 Pro
What is RAG in Cybersecurity?
Retrieval-Augmented Generation (RAG) is an AI architecture that combines the reasoning power of Large Language Models (LLMs) with real-time data retrieval from your actual systems. In cybersecurity, this means the AI doesn't just rely on its training data — it actively queries your infrastructure graph, vulnerability database, and compliance scores before generating a response.
Traditional AI security tools either give generic advice based on training data, or require manual context — you have to copy-paste CVE lists, compliance reports, and asset inventories into a chat. RAG eliminates this: the AI automatically retrieves the relevant context from your environment in real-time.
EchelonGraph's RAG implementation is unique because it uses direct graph database queries (Neo4j Cypher) and structured SQL queries (PostgreSQL) instead of vector embeddings. This means zero hallucination risk on factual data — every CVE ID, asset name, and CVSS score in the response comes directly from your database, not from a fuzzy similarity match.
4 data sources, one intelligent response
Every query retrieves context from your real infrastructure data — not pre-trained knowledge.
Neo4j Attack Graph
Real-time infrastructure topology — assets, VPCs, subnets, security groups, internet-facing nodes, and blast radius attack paths.
CVE Database
Thousands of vulnerabilities with CVSS scores, severity ratings, exploit availability, and asset-to-CVE mapping.
Compliance Scores
17 frameworks — SOC 2, NIST 800-53, CIS AWS/GCP/Azure/K8s, Pod Security Standards, PCI-DSS, HIPAA, ISO 27001, GDPR, plus 5 AI-specific (NIST AI-RMF, EU AI Act, ISO 42001, MITRE ATLAS, OWASP LLM Top 10) — with per-control pass/fail status.
Risk Factors
Weighted risk scoring across 5 dimensions: CVE exposure, misconfigurations, compliance gaps, blast radius, and SLA breaches.
Ask anything about your security
Each answer is locked to your account.
The AI Analyst only reads from your environment. We never mix your data with another customer's, never train on it, and never let the model wander outside what you've asked about. Audit-ready logs are scoped to your team alone.
EchelonGraph RAG vs. Generic AI
| Feature | EchelonGraph RAG | Generic AI Chatbots |
|---|---|---|
| Data Source | ✓ Your actual Neo4j graph + PostgreSQL | Generic training data |
| Accuracy | ✓ References real CVE IDs, asset names | May hallucinate |
| Retrieval Method | ✓ Direct graph + SQL queries | Vector similarity search |
| Freshness | ✓ Real-time (queries live DB) | Stale embeddings |
| Context | ✓ Tenant-specific infrastructure | Shared/generic knowledge |
How EchelonGraph's RAG Pipeline Works
Intent Classification
Your question is classified into intent categories — CVE/vulnerability, compliance, risk, or general. This determines which databases to query, avoiding unnecessary round-trips.
Context Retrieval (RAG)
Based on intent, the system queries your Neo4j attack graph (Cypher queries for blast radius and topology), PostgreSQL (findings, CVEs, compliance scores), and risk scoring engine — all scoped to YOUR tenant.
Context Assembly
Retrieved data is assembled into a structured context document: infrastructure summary, top riskiest assets, critical attack paths, open findings, compliance gaps, and risk factor breakdown.
LLM Generation
The context + your question + a security-expert system prompt are sent to Gemini 1.5 Pro (temperature 0.1 for deterministic output). The model is instructed to ONLY reference data from the context — never hallucinate.
Source Attribution
Every response includes source badges showing exactly which data sources were used — so you know whether the answer came from your Graph, Findings, Compliance scores, or Risk analysis.
See the rest of the EchelonGraph platform
Ready to try RAG-powered security?
Start asking your AI Security Analyst questions today. Get intelligence grounded in YOUR infrastructure data — not generic advice.
AI Analyst available on Pro & Enterprise plans · No credit card required