AI SaaS Security: What's Different
AI applications have all the traditional security concerns (auth, injection, data leakage) plus new ones unique to LLMs: prompt injection attacks, training data extraction, and AI-generated malicious content. This guide covers both layers.
Authentication: Use a Proven Solution
Never build auth yourself. Use Clerk, Auth.js, or Supabase Auth. At minimum, implement: email/password with bcrypt hashing, OAuth (Google, GitHub), email verification, and secure session management with HTTPOnly cookies.
For B2B SaaS, add: SAML/SSO for enterprise customers, role-based access control (admin/member/viewer), and organization-level isolation.
Row-Level Security: Critical for Multi-Tenant SaaS
Every database query must be scoped to the authenticated user's organization. Never query without a tenant filter:
// ✅ Correct: always scope by organization
const documents = await db.query.documents.findMany({
where: and(
eq(documents.organizationId, session.user.organizationId),
eq(documents.id, documentId)
),
});
// ❌ Wrong: missing tenant scope = data leak
const document = await db.query.documents.findFirst({
where: eq(documents.id, documentId),
});Prompt Injection Prevention
Malicious users craft inputs that override your system prompt. Mitigations: use separate system prompt and user input fields (never concatenate), validate and sanitize user inputs, use content filtering before LLM calls, monitor for suspicious patterns (ignore previous instructions, you are now, etc.).
AI Output Sanitization
Never render raw AI output as HTML. Always: sanitize with DOMPurify before rendering, use Content Security Policy headers, render markdown safely (react-markdown with restricted plugins), validate structured AI outputs against schemas before using in logic.
Rate Limiting & Abuse Prevention
AI endpoints are expensive targets for abuse. Implement: per-user rate limits, per-organization limits, global API cost limits with alerts, and CAPTCHA for high-value actions (signup, bulk operations).