Building an AI-Powered SOC Platform From Scratch

Over the last phase of development, I transformed the SECOPS Platform from a basic incident dashboard into an AI-enhanced security investigation pipeline capable of ingesting, enriching, correlating, and investigating security incidents automatically.

The platform architecture now includes:

Elasticsearch

↓

FastAPI Backend

↓

Incident Normalization

↓

MITRE ATT&CK Mapping

↓

Risk Scoring Engine

↓

Incident Correlation

↓

Context Collection

↓

AI Triage Engine

↓

Autonomous Investigation

↓

Timeline Reconstruction

↓

Threat Intelligence Enrichment

↓

Live SOC Dashboard

Key Features Implemented

Real-Time Incident Ingestion

Created authenticated FastAPI ingestion endpoints capable of receiving security incidents from Elastic detections and external systems.

Example ingestion test:

curl -X POST http://localhost:..../incident \

-H "Authorization: Bearer token \

-H "Content-Type: application/json" \

-d '{

"title":"SSH Bruteforce",

"severity":"high",

"category":"unauthorized_access",

"ip":"192.168..."

}'

MITRE ATT&CK Mapping

The backend now automatically maps incidents to MITRE ATT&CK tactics and techniques.

Examples:

• T1110 → Brute Force

• Credential Access

• Remote Access Abuse

• PowerShell-related activity

AI Triage Engine

Implemented an AI triage layer capable of generating:

• incident summaries

• severity reasoning

• investigation recommendations

• attack explanations

Example output:

"This incident appears to involve PowerShell RDP Bruteforce activity..."

Timeline Reconstruction

Built timeline generation logic to reconstruct attack progression:

• initial detection

• suspicious activity

• escalation events

• AI triage execution

• investigation execution

Threat Intelligence Enrichment

Added IOC enrichment and attacker context generation:

• malicious IP reputation

• suspicious activity patterns

• known attack behavior

• Recommended response actions

Real Engineering Challenges Solved

This phase involved solving multiple real-world backend engineering and infrastructure problems:

• FastAPI routing failures

• Uvicorn process conflicts

• PM2 reload issues

• Elasticsearch version mismatches

• API synchronization inconsistencies

• frontend/backend state conflicts

• ingestion debugging

• live dashboard synchronization

• route registration issues

• Python virtual environment recovery

Core Technologies

• Python

• FastAPI

• Elasticsearch

• Uvicorn

• PM2

• Nginx

• JavaScript frontend

• MITRE ATT&CK

• AI-assisted investigation workflows