Hackura
Sentinel AI
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Platform architecture

An enterprise threat intelligence pipeline

Hackura Sentinel AI is built as a layered security platform so each stage of scanning, enrichment, scoring, and graph traversal stays observable and independently scalable.

System flow

Frontend

App Router UI, docs portal, dashboard, and visualization surfaces.

API Layer

Public endpoints, request validation, and orchestration glue.

Recon Engine

URL, DNS, IP, redirect, and metadata collection pipeline.

Threat Intelligence

Enrichment, correlation, and confidence layering.

Graph Intelligence Engine

Node/edge inference, cluster discovery, and relationship scoring.

AuraDB / Neo4j

Persistent graph storage and traversal for intelligence contexts.

Supabase Auth

Supabase handles identity, session persistence, and profile onboarding. The docs portal and dashboard inherit the same auth model, which keeps UX and data access aligned.

AI risk engine

The risk engine combines deterministic indicators with AI-assisted weighting. It turns sparse signals into a defensible score with a confidence band, rather than a single opaque number.

Why this matters

Operators need to know not only what was flagged, but why the platform assigned that score.

Graph intelligence with AuraDB

Graph intelligence links infrastructure through nodes and edges, then surfaces clusters, redirect chains, and inferred relationships for analysts.

Scan orchestration

The API layer coordinates the recon engine, enrichment providers, and graph synthesis. Each scan can fan out to DNS, certificate, ASN, and intelligence providers without blocking the user experience.

Frontend visualization

The dashboard visualizes everything in a responsive, interactive surface that lets you inspect nodes, risk, and evidence in real time.