Mythos Aegis: Architectural Analysis of an Enterprise-Grade AI Security Gateway and Intent Parsing System

Abstract: This project is an enterprise-level AI SaaS security gateway focusing on multi-tenant RAG architecture, Agent runtime, visual intelligence, workflow automation, etc., to address LLM-specific security risks.

Original Author/Source:

• Author/Maintainer: fokrulanthro16-eng
• Source Platform: GitHub
• Original Title: mythos-aegis-v2
• Link: https://github.com/fokrulanthro16-eng/mythos-aegis-v2
• Release/Update Time: 2026-06-06T15:46:49Z

Core Modules: Multi-tenant RAG, Agent runtime, visual intelligence, SQL Airlock, workflow automation, CI/CD & DevSecOps.

With LLM's rapid adoption in enterprises, AI application security (prompt injection, sensitive data leakage, unexpected behavior) becomes a key challenge. Traditional API gateways can't handle LLM-specific risks.

Mythos Aegis, built on FastAPI, is an enterprise intent parsing & security boundary gateway providing comprehensive security protection and traffic management for AI SaaS apps. It integrates multi-tenant architecture, RAG, Agent runtime, visual intelligence, workflow automation, SQL access control to offer production-ready security infrastructure.

Core Architecture

Mythos Aegis uses modern microservices architecture with FastAPI as core framework. Key components:

• Redis: Distributed rate limiting & session cache
• JWT: Key rotation for authentication security
• OpenTelemetry: Full-link tracing
• Prometheus: Metrics collection for observability

Tech Stack Selection

• FastAPI: High-performance async web framework with native OpenAPI support
• Redis: Ensures multi-instance state consistency
• PostgreSQL: Main data storage with Alembic for DB versioning
• Docker & Kubernetes: Containerization and horizontal auto-scaling

Tenant Isolation Mechanism

Uses namespace-level isolation + JWT tenant ID to ensure each request accesses only its tenant's data, balancing security and resource efficiency (no per-tenant instances).

RAG Flow Integration

When a user query enters, the gateway parses intent first. If RAG is needed, it retrieves relevant docs from vector DB and passes to downstream LLM, enabling centralized RAG management for optimization and monitoring.

Agent Workflow Orchestration

Built-in Agent runtime supports complex workflow orchestration (condition branches, loops, parallel execution) via declarative configs. Gateway schedules execution and inserts security checkpoints at key nodes.

Visual Intelligence Processing

Handles image inputs: preprocessing, feature extraction, multi-modal model interaction. Unifies visual input processing at gateway layer, letting downstream services focus on business logic.

Query Review & Rewrite

SQL Airlock reviews all DB queries: syntax analysis for injection patterns, parameterization, permission check before execution. Supports query rewrite (natural language to safe SQL).

Data Desensitization & Audit

Automatically identifies sensitive fields (ID, credit card) and masks them. All DB access is logged for compliance.

Code Quality Gate

CI uses ruff (formatting/static check), mypy (type check), pytest (≥80% coverage) on each commit.

Container Security

Docker builds follow best practices: non-root user, minimal image, vulnerability scans (pip-audit, bandit, detect-secrets).

Kubernetes Deployment

Complete K8s manifests: namespace isolation, ConfigMap/Secret management, HPA, PDB. Supports blue-green/canary releases and zero-downtime JWT key rotation.

Mythos Aegis represents the direction of enterprise AI gateways: integrating traditional traffic management with AI-specific security and operation capabilities. Its multi-tenant RAG, Agent runtime, SQL Airlock provide solid infrastructure for production AI apps.

For tech teams planning AI architectures, it offers a full reference implementation (from local to K8s production) with strict security practices and observability design worth learning from.