Secure AI Platform — Architectural Overview

Architectural definition of all modules, repos, and interconnections.

Platform Vision & Goals

The Secure AI Platform (SAI Platform) is a modular, production-style ecosystem designed to demonstrate secure-by-design AI engineering across the full lifecycle of machine learning and large language model systems.

Vision: To build a reference architecture that unifies, AI, security, and operations, showcasing how a Staff/Principal-level AI Security Engineer would design, deploy, and defend an AI-driven platform.

Design Principles

• Security-first: Every component integrates identity, logging, secrets management, and compliance.
• Reproducibility: All pipelines, models, and deployments can be rebuilt deterministically.
• Transparency: Clear observability and auditability across systems.
• Modularity: Each function exists in its own repo, but integrates through shared infrastructure.
• Pragmatism: Built using open-source, self-hosted, and production-relevant technologies.

Platform Modules

Module	Purpose	Key Tech	Repository
Documentation & Governance	Program specifications, architecture, standards, and policies	Markdown, GitHub Actions, MkDocs	sai-platform-meta
Platform Infrastructure	Unified infrastructure: Vault, Caddy, Loki, Grafana, MLflow	Docker Compose, Vault, Caddy, Grafana, Loki, OTel	sai-platform-infra
ML Foundations	Core machine learning pipelines and datasets	PyTorch, scikit-learn, MLflow	sai-ml-foundations
Inference API	Secure model-serving API with JWT + RBAC	FastAPI, Pydantic, PyTorch, MLflow SDK	sai-inference-api
Agent SecOps	Secure LangChain agents for SOC automation	LangChain, FastAPI, Vault, OTel	sai-agent-secops
Adversarial Lab	Adversarial ML, red-teaming, and security tests	Adversarial Robustness Toolbox, PyTorch, LLM Red Team tools	sai-adversarial-lab
MLOps Pipeline	CI/CD, scanning, SBOMs, and release governance	GitHub Actions, Trivy, Syft, OPA, Cosign	sai-mlops-pipeline

Repository Definitions

sai-platform-meta

Purpose: Acts as the control plane and governance layer for the entire platform.

• Contains program specifications, templates, CI/CD baselines, and policies.
• Source of truth for architecture, security, and observability standards.
• Provides reusable .github/ workflows and .dev/ scaffolds.

Key Contents:

• /docs/ -> specs, architecture, narratives
• .github/ -> CI/CD and security automation templates
• .dev/ -> pre-commit hooks, linting, make targets

sai-platform-infra

Purpose: Provides local infrastructure for all other services.

• Hosts Vault, Caddy, Loki, Grafana, and MLflow under Compose profiles.
• Exposes telemetry and secret-management endpoints for other repos.

Key Technologies:

• Docker Compose (profiles: observability, secrets, registry, proxy)
• Caddy (reverse proxy), Loki/Grafana (logging & metrics), Vault (secrets)
• OTel Collector (traces)

Deliverables:

• docker-compose.yml, Caddyfile, otel-collector.yaml, vault-bootstrap.sh
• /docs/tech/runbook.md (how to operate locally)

sai-ml-foundation

Purpose: Reproducible, secure ML training pipelines.

• Demonstrates secure model development lifecycle.
• Integrates MLflow for experiment tracking.
• Documents dataset provenance and lineage.

Key Technologies: PyTorch, scikit-learn, MLflow, pre-commit, ruff

Deliverables:

• /src/ -> model training & evaluation
• /data/ -> dataset cards & manifests
• /docs/ -> threat model, model cards, architecture

sai-inference-api

Purpose: Securely serve ML models as APIs.

• Implements JWT authentication and RBAC.
• Logs inferences with correlation IDs for traceability.
• Integrates with OTel and the observability stack.

Key Technologies: FastAPI, Uvicorn, PyTorch, Pydantic, JWT, OTel

Deliverables:

• /src/ -> API code
• /tests/ -> security and functional tests
• /docs/ -> OpenAPI spec, runbook, threat model

sai-agent-secops

Purpose: Build and secure LangChain agents for SOC automation.

• Enforces tool allowlists, RBAC, and policy-based prompt filtering.
• Integrates Vault for secret injection and audit logging.
• Provides auditability of prompt, tool, and result chains.

Key Technologies: LangChain, FastAPI, Vault, OTel, RegexGuard

Deliverables:

• /src/agent/ -> core agent logic
• /src/security/ -> guardrails & filters
• /docs/ -> security controls, chain visualization

sai-adversarial-lab

Purpose: Adversarial testing and AI red teaming.

• Tests model and agent resilience using adversarial attacks.
• Simulates prompt injections, jailbreaks, and data poisoning.
• Produces structured attack reports and metrics.

Key Technologies: Adversarial Robustness Toolbox (ART), PyTorch, LangChain Red Team tools

Deliverables:

• /attacks/ -> attack harness
• /tests/ -> regression and defense tests
• /reports/ -> structured output
• /docs/tech/threat-model.md -> red team findings

sai-mlops-pipeline

Purpose: Centralized CI/CD pipelines and policy enforcement.

• Provides reusable GitHub Actions workflows.
• Automates SBOM generation, container scanning, and artifact signing.
• Integrates OPA policies to enforce security gates.

Key Technologies: GitHub Actions, Trivy, Syft, Cosign, OPA/Conftest

Deliverables:

• .github/workflows/ -> reusable CI/CD templates
• /policy/ -> OPA policy bundles
• /docs/ -> CI/CD standards and release governance

Security & Compliance Foundations

Security Architecture Principles:

• Identity-first (JWT + RBAC on all APIs)
• Secrets in Vault only; short-lived tokens, no .env files committed
• SBOMs and signing enforced for every image (Trivy + Cosign)
• CI/CD policies enforced by OPA (fail closed)
• Static and dependency scanning (CodeQL, Dependabot)

Threat Modeling: Each repo maintains /docs/tech/threat-model.md following a shared template. Platform-level threat models aggregate into: /docs/architecture/THREAT_MODEL.md within sai-platform-meta.

Observability & Operations

Telemetry Standard: OpenTelemetry (OTel) is instrumented across every service.

Signal	Collector	Sink
Logs	Loki	Grafana Loki UI
Metrics	Prometheus exporters -> Grafana	Dashboards
Traces	OTel Collector	Grafana Tempo

Key Dashboards:

• API latency and inference errors
• Model accuracy vs drift
• Agent tool usage statistics
• Adversarial lab attack outcomes

Runbook: /docs/tech/runbook.md in sai-platform-infra describes log/trace collection.

Integration Topology

Trust Zones:

• public -> API Gateway / Agent endpoints (JWT auth)
• internal -> MLflow, Vault, Observability, CI/CD
• secure -> Signing keys, OPA policies, SBOM registry

Secrets Flow:

1. Vault issues short-lived tokens for APIs and CI/CD.
2. Services retrieve secrets dynamically via OIDC or AppRole.
3. No static keys or .env files under version control.

Network Summary:

Service	Port	Description
Grafana	3000	Observability UI
Loki	3100	Logs ingestion
OTel Collector	4317 / 4318	Telemetry input
Vault	8200	Secrets API
Caddy	80 / 443	Reverse proxy

Development Lifecycle

Flow Overview:

Code -> Lint/Test -> SBOM -> Scan -> Sign -> Deploy -> Monitor -> Feedback -> Retrain

Stages:

1. Code & Commit -> pre-commit hooks enforce lint, typing, and secret scan.
2. CI Build -> SBOM generation (Syft) + scanning (Trivy).
3. Policy Gate -> OPA checks; fail if HIGH/CRITICAL unwaived.
4. Sign & Release -> Cosign signature, Git tag, changelog update.
5. Deploy -> Compose or K8s.
6. Monitor -> OTel + Grafana dashboards.
7. Feedback Loop -> retraining, adversarial testing, or model updates.

Versioning: Semantic (v<major>.<minor>.<patch>)

Future Expansion

Planned Enhancements:

• Containerized K8s Helm deployment
• SOC Integration: alert triage bot and LLM-driven response workflows
• Compliance-as-code via Open Policy Agent extensions
• Distributed tracing with Grafana Tempo
• Public documentation site via MkDocs or Docusaurus

Long-term Goals:

• Demonstrate full AI security lifecycle: design -> defense -> detection -> governance
• Publish the architecture as a public reference for secure MLOps.