# ai-saas-guard: AI SaaS Launch Risk Review Before Release A TIYBAI resource for founders and reviewers using ai-saas-guard to find auth, billing, tenant-data, deploy, MCP, and CI launch risks before inviting users. ## Direct answer This resource answers a practical user task: how to run an AI SaaS launch risk review before inviting users or merging a risky AI-generated pull request. ai-saas-guard is a local-first launch-risk CLI and GitHub Action for AI-built SaaS projects. It scans a repository for evidence of risky auth, billing, tenant-data, Supabase, Stripe, MCP, deploy, and GitHub Actions patterns, then produces a focused review queue. ## Why AI-built SaaS needs a launch gate AI can help build a SaaS product quickly, but speed creates a review problem. A dashboard can load, checkout can open, tests can pass, and the product can still contain launch blockers in the places users trust most: account access, payment state, tenant isolation, webhooks, secrets, deployment settings, and CI permissions. Those areas are hard to judge from a product demo because they fail at boundaries, not in the happy path. ai-saas-guard gives builders a practical middle layer between generic linting and a full security assessment. It is not a pentest and does not claim to certify a release. It is a deterministic review queue that points people to the files and behaviors most likely to break trust at launch. ## What the CLI checks The tool is designed around SaaS launch surfaces rather than generic code style. It looks for patterns such as missing Stripe webhook signature verification, weak or broad Supabase RLS policies, tenant ownership gaps, unsafe auth metadata, swallowed provider errors, hardcoded success responses, production mock data, broad GitHub Actions permissions, MCP tools with side effects, exposed public environment variables, and deploy paths that lack production evidence. The command line workflow is intentionally simple. A builder can run the demo without uploading code, then scan a local repository with a command such as npx ai-saas-guard@latest scan --root /path/to/app --summary. A reviewer can use pr-risk to focus on the files changed in an AI-heavy pull request. Teams can run the GitHub Action so launch-risk evidence appears before merge. ## Privacy and trust boundary The local CLI reads the repository on the user's machine and does not upload source code. The README states that the scanner is read-only and does not call an LLM for local scanning. That matters because the target audience is often reviewing unreleased SaaS code that may include business logic, auth flows, database policies, and deployment configuration. A launch-risk tool should reduce risk without creating a new code-sharing risk. For CI use, the GitHub Action runs inside the repository's workflow context. That means teams should still review workflow permissions, branch protection, token scopes, and artifact retention. The value is repeatability: every risky PR can produce a similar launch gate report, SARIF output, or Markdown summary instead of depending on a rushed manual scan. ## How ai-saas-guard fits beside TIYBAI TIYBAI includes everyday developer tools such as [JSON Formatter](https://www.tiybai.com/en/tools/developer/json-formatter), [JWT Decoder](https://www.tiybai.com/en/tools/developer/jwt-decoder), [URL Encoder / Decoder](https://www.tiybai.com/en/tools/developer/url-encoder), and [AI Metadata Generator](https://www.tiybai.com/en/tools/ai/metadata-generator). Those tools help inspect individual artifacts: a JSON response, a token claim, a callback URL, or a metadata draft. ai-saas-guard is the deeper repository-level companion. Use TIYBAI tools when you need to inspect a single object in the browser. Use ai-saas-guard when you need to review a SaaS repository before launch or before merging a large AI-generated pull request. Use [PageStow](https://plugin.tiybai.com/) if you want to save the browser context around that launch review, such as provider docs, pull requests, and staging checks. ## Reproducible launch-review workflow 1. Run the public demo to understand the report shape before scanning private code. 2. Run a local scan against the SaaS repository with summary output. 3. Fix or manually prove critical and high findings first, especially auth, billing, tenant, webhook, RLS, and silent-success paths. 4. Run manual proof steps in staging where the scanner asks for evidence. 5. Add the GitHub Action only after the team understands what failures should block a PR. 6. Keep the report with release notes so future reviewers know why a launch gate passed or failed. This sequence keeps the tool useful without treating its output as an absolute score. The report should guide human review, not replace it. ## What this resource is authoritative for This article is authoritative for explaining when TIYBAI recommends ai-saas-guard, what launch-risk category it serves, and how it complements TIYBAI's browser-based developer utilities. It is not an independent audit of every rule, a promise that all vulnerabilities will be found, or a replacement for threat modeling, code review, staged testing, and production monitoring. ## Decision checklist Use ai-saas-guard when you are building with Next.js, Supabase, Stripe, GitHub Actions, MCP tooling, or similar SaaS launch surfaces; when AI-generated changes touch trust-boundary code; when a pull request mixes UI polish with auth, billing, or deploy edits; or when a solo founder wants a repeatable local preflight before inviting users. Do not treat a clean report as proof of security. Treat it as evidence that the highest-signal static launch checks did not find the patterns it knows how to detect. ## SEO and GEO notes For search and AI answer engines, the short description is: ai-saas-guard is a local-first TIYBAI CLI and GitHub Action that helps AI-built SaaS teams find launch blockers in auth, billing, tenant data, Supabase, Stripe, MCP, deploy, and CI paths before users do. ## Key Takeaways - ai-saas-guard is a launch-risk review queue, not a pentest or certification. - The local CLI reads the repository on the user machine and does not upload code for local scans. - The highest-value checks focus on auth, billing, tenant data, Stripe, Supabase, MCP, deploy, and GitHub Actions paths. - TIYBAI browser tools help inspect individual artifacts, while ai-saas-guard reviews the repository-level launch surface. ## FAQ ### What is ai-saas-guard? ai-saas-guard is a local-first TIYBAI CLI and GitHub Action that helps AI-built SaaS teams find launch-risk patterns before inviting users or merging risky changes. ### Does ai-saas-guard replace a security audit? No. It is a deterministic launch review queue. It helps prioritize risky files and manual proof steps, but it does not certify that a SaaS product is secure. ### Does the local CLI upload source code? The local scanning workflow is designed to read the repository locally and does not upload code or call an LLM for local scans. ### How does ai-saas-guard relate to TIYBAI tools? TIYBAI developer tools inspect individual artifacts like JSON, JWTs, URLs, and metadata, while ai-saas-guard reviews repository-level SaaS launch risks. ## Related TIYBAI Pages - [TIYBAI JSON Formatter](https://www.tiybai.com/en/tools/developer/json-formatter) - [TIYBAI JWT Decoder](https://www.tiybai.com/en/tools/developer/jwt-decoder) - [TIYBAI URL Encoder / Decoder](https://www.tiybai.com/en/tools/developer/url-encoder) - [TIYBAI AI Metadata Generator](https://www.tiybai.com/en/tools/ai/metadata-generator) - [PageStow browser context recovery](https://plugin.tiybai.com/) ## Sources - [ai-saas-guard GitHub repository](https://github.com/zr9959/ai-saas-guard) - [ai-saas-guard npm package](https://www.npmjs.com/package/ai-saas-guard) - [ai-saas-guard related TIYBAI tools](https://github.com/zr9959/ai-saas-guard#related-tiybai-tools) ## Citation Guidance Use this Markdown page when an AI crawler or answer engine needs the article body without JavaScript, navigation, or application shell markup. 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