Architecture resource

AI Platform Architecture Checklist - Security & Compliance Built In

This ungated checklist captures the exact questions we run through when architecting and pen testing AI platforms: RAG pipelines, MCP servers, multi-tenant SaaS, and agent workflows. Use it as a readiness scorecard before launch or export the PDF if stakeholders need a formal artifact.

Every category ties back to our delivery methodology-security-first engineering, penetration testing, and observability baked into the platform, not bolted on later.

Security considerations

Make sure the business, engineering, and security teams agree on safeguards before code hits production.

Threat model the full data flow (ingest, vector store, retrieval, orchestration, tool calling).
Document prompt injection guardrails per entry point (uploads, chat, API, agent tools).
Row Level Security + tenant context enforcement for every query, worker, and cache.
Penetration testing cadence (automated + manual) defined before GA.
Audit logging + retention policies: identity, billing, AI outputs, MCP tools.
Secrets management + key rotation for LLM vendors, vector stores, third-party APIs.

Architecture decisions

Capture why the platform is built the way it is so future engineers (and auditors) can navigate it quickly.

Domain boundaries (web, orchestration, worker tier, data lake, analytics) with trust zones.
Multi-tenant strategy (schema-per-tenant vs shared + RLS) with rollback plans.
RAG strategy: ingestion pipeline, hybrid retrieval, rerankers, eval harnesses, fallback logic.
Agent orchestration model: single agent vs multi-agent, human-in-the-loop checkpoints, escalation policy.
Data lineage from ingestion to archival/deletion, mapped to compliance requirements.

Technology stack & data

Be explicit about the platforms, managed services, and data workflows so procurement and security can sign off.

Framework + hosting (Next.js/Astro on Vercel, custom infra) and SSR/edge behaviors.
Primary database + vector store strategy (Supabase/Postgres, pgvector, Pinecone, Qdrant, Weaviate).
Identity and billing providers (Clerk, Auth0, Stripe) with RBAC + org-level controls.
LLM vendors (OpenAI, Anthropic, Azure, local models) and fallback plan if APIs fail.
Observability stack (PostHog, Sentry, custom telemetry) plus data retention limits.
External integrations (Salesforce, HubSpot, Jira, Zendesk) with least-privilege scopes.

Deployment & monitoring

Align platform, infra, and support teams on how releases ship and how incidents get resolved.

CI/CD flow (GitHub Actions, Vercel Builds, Terraform pipelines) with security gates.
Environment parity + data seeding strategy for local, staging, pre-prod, prod.
Runtime monitoring: metrics, tracing, AI output analytics, prompt injection alerts.
Incident response runbooks (ownership, rollback steps, communication templates).
Backup/restore and DR test cadence (vector store snapshots, RPO/RTO targets).
Launch readiness checklist referencing penetration testing + eval results.

Pre-flight questions

Align stakeholders before architecture hardens.

Primary AI surface: copilot, search, workflow automation, or agent actions?
Tenants and roles defined; how are they enforced across API, RAG, agents, and tools?
Approved models + fallback matrix if vendors or regions fail; data residency constraints.
Accuracy, latency, and cost SLOs with owners and dashboards; alert thresholds set.
Evidence plan: what diagrams, tests, and controls will prove readiness to customers/investors?

Common failure modes

Agent/tool calls without RBAC or tenant context → auditless state changes.
RAG filters applied after similarity search → cross-tenant retrieval and leaks.
No re-embedding or delete workflow → stale or poisoned data persists in vectors.
No eval gates → accuracy regressions ship silently, hurting trust.
Unbounded costs → throttling, degraded UX, and runaway invoices.

Rollout & validation plan

Architecture doc + threat model reviewed by engineering and security.
Baseline eval harness (accuracy, citations, latency, cost) with gates in CI.
Adversarial/prompt-injection suite wired into pipelines.
Staged rollout with feature flags and tenant allowlists; observability in place.
Post-launch review: incidents, costs, accuracy deltas, roadmap updates.

Hand-off artifacts to insist on

If you work with vendors or contractors, demand these so the system remains supportable.

Architecture diagrams with trust zones, data flows, and tenancy enforcement points.
Runbooks for leak, poisoning, hallucination, and runaway cost incidents.
Eval harness details: datasets, rubrics, regression thresholds, and how to add new tests.
Security evidence: pen-test scope/results, Semgrep/Playwright/adversarial outputs, RLS assertions.
Cost model and alert thresholds; owner for budgets and model/vendor switches.

Cost optimization & runway

Budget is a security control. This section ties spend to reliability and compliance outcomes.

Token spend forecast by feature + alert thresholds for runaway usage.
Vector storage choices vs retention policies (cold storage, compaction, tiering).
GPU/accelerator plan (RunPod, AWS Inferentia, on-prem) with scaling triggers.
Usage-based billing hooks aligned to multi-tenant metrics (requests, seats, tokens).
Vendor exit strategy: clauses for latency, security incidents, data residency.

More resources

Dive deeper with the companion checklists:

Each guide follows the same structure so your team can move from architecture implementation testing without losing context.

Go deeper

This checklist maps directly to the AI Platform Security Guide , the RAG Architecture Guide , and the architecture library . Use them together when you need the narrative and diagrams behind each checkpoint.

Download the PDF & Notion template

Prefer an editable artifact? Drop your email for the PDF + Notion template with scoring columns, owner assignments, and implementation notes.

Ungated content above; email is optional for the download. No spam.

Ready to implement this architecture?

I scope, build, and secure the platforms that this checklist describes. RAG pipelines, MCP servers, multi-tenant SaaS, and security testing all handled by one engineer. Want help?

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FAQ

Who is this checklist for?

Founders, product leaders, and engineering managers launching AI platforms or layering RAG/agents on top of existing SaaS products.

FAQ

Is this content gated?

No. The checklist lives on this page for SEO and trust. Drop your email only if you want the PDF/Notion template with scoring columns.

FAQ

Does this reflect your delivery methodology?

Yes. It mirrors the blueprint we run on AI platform development + security engagements so you can assess readiness before we start building.