How I'd build Security AI
I'd reach for Next.js on the frontend, FastAPI on the backend with Python, Postgres for the database, and Stripe for billing. The core product is a log ingestion and LLM analysis layer that sits between an MSB's security tools (SIEM, endpoint detection, access logs) and their team, so I'd also plan on AWS S3 for log storage and Temporal for reliable, async job processing of log analysis. Rough estimate: 160-200 hours to get from zero to a working multi-tenant SaaS with working auth, customer onboarding, three pricing tiers, and the first integration (most likely CloudTrail or generic syslog ingestion). That's aggressive but doable if we stay focused on the core loop.
Day-by-day plan
Day 1-2: Provision the Postgres schema for multi-tenancy, set up auth via Clerk, and wire the basic account creation flow. Day 3: Integrate Stripe billing for three pricing tiers and ensure webhook handling for subscription events. Day 4-5: Build the log ingestion API in FastAPI, starting with a generic syslog endpoint and CloudTrail as the first concrete integration. Day 6-7: Scaffold the Next.js dashboard UI with a log viewer, alert creation, and basic metrics. Connect the frontend to the FastAPI backend and test end-to-end. Day 8: Set up Temporal for async log processing and wire Claude API calls to analyze ingested logs for threat patterns and anomalies. Day 9-10: Build the customer onboarding flow, including step-by-step integration setup and an API key management page. Deploy a basic admin dashboard to monitor ingestion health and account usage.
What's hard about this build
The main technical risk is getting log schema interpretation right. Every security tool outputs logs differently, and normalizing them before LLM analysis is where bugs hide. I'd need to handle Postgres JSON columns carefully and write extensive edge-case tests around timestamp parsing, timezone handling, and schema drift. The second hard part is the trust layer. Connecting a customer's security logs to an unknown AI vendor requires SOC 2 documentation and privacy certifications that take weeks to complete and audit. Most SMBs won't onboard until they see those docs, so I'd plan a compliance sprint around week three, not week one. Third is the legal risk around handling customer security data. I'd need audit logging, encryption at rest, secure key management, and a clear data retention policy before launch. That's unglamorous work, but it's non-negotiable and often gets underestimated.
What's fast because of AI
Claude accelerates the threat detection logic substantially. Instead of hand-rolling detection rules or maintaining a rule engine, I'd use Claude to reason over ingested logs and identify anomalies via structured prompts. That compression alone saves a week of traditional development. AI also speeds up scaffolding: Claude generates the Postgres schema, FastAPI endpoint stubs, Next.js form components, and database migrations faster than I'd hand-code them. For the onboarding copy, integration docs, and help text, Claude writes the first draft of runbooks for each log source in hours instead of days. Edge-case enumeration is fast too: I'd share a log sample with Claude and ask it to identify all the ways that schema could break, then use those cases to seed comprehensive test data. Debugging is faster because Claude can trace through stack traces and suggest fixes before I run ten local iterations myself.
How I'd hand it off
I'd leave a Loom walkthrough of the entire admin flow and a detailed runbook covering how to ingest new log sources and integrate with new MSP partners. The FastAPI code would include clear docstrings and deliberate separation between ingestion, analysis, and billing logic. I'd set up the Linear board with 30 days of prioritized roadmap items, starting with the two missing integrations and the compliance sprint. All Stripe, AWS, and Clerk credentials would be handed over in a shared 1Password vault with full read and rotation access. I'd commit to a 30-day pager rotation to field critical bugs and answer product questions before fully handing off the product.