AI SaaS Security.

None of these require a sophisticated attacker. A retrieval query that isn't scoped correctly, an evaluation suite that doesn't cover a real failure mode, or cost monitoring that only checks aggregate spend, all create exposure through ordinary gaps, not novel attacks.

• Cross-tenant data leakage — retrieval or context construction that isn't strictly scoped per tenant, especially in shared vector stores or caching layers.
• Prompt injection within a tenant — user-supplied or retrieved content manipulating model behavior within that tenant's own session.
• Margin erosion — usage growth eroding gross margin silently because cost isn't attributed and monitored per tenant.
• Provider dependency risk — a single upstream model provider outage or behavior change taking down the product with no fallback.

• Tenant isolation enforced at the database layer, tested directly with adversarial cross-tenant queries, not assumed from application code alone.
• All retrieved and user-supplied content treated as untrusted input when constructing model context.
• Per-tenant and per-feature cost attribution visible in dashboards from launch, with cost regression testing on every change.
• Model abstraction with fallback routing so a single provider's outage or degradation doesn't take down the whole product.

• How is tenant isolation tested, and can you show adversarial cross-tenant test results?
• How is cost attributed per tenant, and how quickly would margin erosion be caught?
• What happens if the primary model provider has an outage?

See our AI SaaS development guide for how these controls fit into the full product architecture.