Every B2B SaaS company we talk to has shipped, or is about to ship, some form of AI feature. A support copilot. A RAG-backed search over customer data. An agent that can call internal APIs on a user's behalf. These features close deals and speed up product roadmaps, but they also open an attack surface that most security programs were never built to test for.
If your SOC 2 audit or penetration test scope was written before you added an LLM to your product, it almost certainly does not cover the ways that LLM can be abused. That gap matters more than most teams realize, and it is starting to matter to your buyers too.
Why this is a B2B SaaS problem specifically
Consumer AI tools get to fail gracefully. A chatbot that says something odd is embarrassing. A B2B SaaS platform that hands a customer's AI assistant access to another tenant's data, or lets a crafted prompt trick an agent into exporting records it should not touch, is a breach. The stakes are different because the data is different: contracts, financial records, PII, and in many cases the exact regulated data your compliance program exists to protect.
Your customers' security teams have caught up to this. Enterprise buyers now ask pointed questions in security questionnaires about how AI features handle tenant isolation, what happens when a user tries to jailbreak the assistant, and whether the model can be manipulated into taking actions outside its intended scope. If you cannot answer those questions with evidence, the deal stalls, the same way it used to stall without a SOC 2 report.
Where the actual risk lives
AI and LLM security is not one thing. In practice, the risk in a typical B2B SaaS product concentrates in a few specific places.
- Prompt injection, where instructions hidden in a document, email, ticket, or web page override the system prompt and redirect the model's behaviour, sometimes without the user ever noticing.
- RAG leakage, where a retrieval-augmented generation pipeline pulls content across permission boundaries and surfaces it in a response, effectively bypassing your application's own access controls.
- Agent tool abuse, where an AI agent with access to internal functions, APIs, or databases can be coaxed into calling them in ways the developer never intended, from data exfiltration to unauthorized writes.
- Insecure output handling and excessive agency, two of the categories in the OWASP LLM Top 10, where model output is trusted and executed downstream without validation, or the model is given more autonomy than the use case requires.
These are not theoretical. They map directly onto the OWASP Top 10 for Large Language Model Applications, which is quickly becoming the reference framework auditors and enterprise security teams use to evaluate AI features, the same way the OWASP Web Top 10 became the baseline for application security a decade ago.
How traztech scopes an AI security assessment
We built our AI security assessments around how B2B SaaS companies actually build with AI: a system prompt, a retrieval layer over customer data, and increasingly, tool calls or agent actions. The engagement is scoped around your actual architecture, not a generic checklist.
That means testing your own AI directly. We attempt prompt injection against your live system prompt and guardrails, not a demo sandbox. We test whether your RAG pipeline respects tenant and role boundaries, or whether the retrieval step can be tricked into pulling content a user should not see. If your product includes an agent with tool access, we test what that agent can be manipulated into doing, from calling functions out of scope to chaining actions in ways that bypass application-level controls. We map every finding back to the relevant OWASP LLM Top 10 category so the results are usable evidence, not just a list of bugs.
The output is a report your engineering team can act on and your sales team can point to when a prospect's security questionnaire asks how you test AI features. For companies that are also working toward a broader compliance milestone, this work fits alongside our wider compliance engagements rather than sitting off to the side as a one-off audit.
When to run this
The right time is before your AI feature ships to a customer with real data behind it, and again whenever the architecture changes meaningfully, a new tool gets added to an agent, a new data source gets connected to RAG, or a system prompt gets rewritten. Waiting until a customer asks the hard question in a security review means you are testing under deadline pressure instead of on your own schedule.
If you are a founder or CTO who shipped an AI feature fast and has not had it tested by anyone outside the team that built it, that is the normal starting point, not a red flag. The goal is to close the gap before an enterprise buyer, or an attacker, finds it first.
Get your AI features tested properly
If your product has an AI feature handling customer data, whether that is a chat assistant, a RAG pipeline, or an agent with tool access, we can scope an assessment against your actual architecture and give you a report you can act on and show to buyers. Contact traztech to talk through what you have built and what a focused AI security assessment would look like for it.