SaaS used to mean multi-tenant databases and shared compute with isolated user sessions. In 2026 it means multi-tenant LLM inference, RAG pipelines, AI agents, and vector databases — and that shift introduces three problems traditional SaaS security models were never built to handle.
Unlike classic SaaS isolation failures — SQL injection, broken access control — AI systems fail in semantic isolation, not just technical isolation. A system can be technically secure at the database layer and still leak data across tenants through language models, retrieval systems, and shared memory. At the same time, AI features turn marginal cost from near-zero into highly variable, opening the door to a new category of economic abuse. And every enterprise buyer's security review now asks AI-specific questions that traditional SOC2 and ISO 27001 documentation was never written to answer.
Four real failure modes — system prompt leakage, RAG document leakage, session store contamination, and tool execution leakage — and the isolation patterns across data, retrieval, and runtime layers that actually prevent them.
Read the full guide →Prompt farming, jailbreak campaigns, and rate limit evasion can turn a profitable subscription tier unprofitable overnight. Why traditional rate limiting structurally fails for AI, and the cost-protection strategies that work instead.
Read the full guide →How SOC2 and ISO 27001 controls extend to AI features, real CAIQ questions with strong-answer examples, and the one question — "what happens to my data in your AI system" — that every CISO actually asks.
Read the full guide →The HexTyx AI Security Assessment covers tenant isolation, abuse exposure, and compliance readiness in one scored report.