AI and LLM Security for B2B SaaS

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.

Guardrails are not a control, and treating them as one is the root problem

Most teams respond to prompt injection by strengthening the system prompt or adding a classifier that screens inputs and outputs. Both are useful and neither is a control you can put in front of an auditor or a customer's security team with a straight face, because both are probabilistic. A control that works most of the time against an adversary who can retry indefinitely is a speed bump.

The controls that hold are the ones that sit outside the model. If a user cannot see a document in your application, the retrieval layer should never return it to the model in the first place, which means access filtering happens at query time against the requesting user's actual permissions, not as a post-processing step on results the model has already seen. If an agent has a tool that deletes records, the permission check belongs inside the tool implementation, evaluated against the calling user's session, and it must fail closed regardless of how convincingly the model was persuaded to call it. If model output is rendered as HTML, executed as SQL, or passed to a shell, the safety comes from encoding and parameterization exactly as it would with any untrusted input, because that is what model output is.

The useful mental reframe is that the model is a confused deputy with the persuadability of a new intern and none of the accountability. You do not solve that by writing a better job description. You solve it by not granting the deputy permissions it does not need and by checking its instructions against your own authorization logic before acting on them.

The injection paths that are not the chat box

Teams test the chat input and stop there. In a B2B product, the interesting injection paths are the ones where content arrives from somewhere else and gets placed in front of the model automatically.

Support tickets are the obvious one. A customer submits a ticket whose body contains instructions, your agent summarizes the queue, and the instructions execute with the agent's permissions rather than the submitter's. Uploaded documents behave the same way, including text in PDFs that is invisible on screen because it is white on white or sized at one point. Scraped web pages pulled in by a research feature, email bodies processed by an assistant, calendar invite descriptions, filenames, code comments in a repository your assistant reads, and the contents of a database field an admin edited are all inputs the model treats as authoritative unless you have separated data from instruction.

The second half of this problem is the exit path. An attacker who can influence model output frequently does not need the model to call a tool, because the rendering layer will do the exfiltration. A chat interface that renders markdown images will fetch a URL the moment the response displays, and the model can be led into embedding stolen data in that URL's query parameters. The same applies to clickable links, iframes, and any automatic link preview. Restricting outbound fetches from your rendering layer to an allowlist, and not auto-loading remote content in AI responses, closes a channel that a great many products still leave open.

Testing something that does not behave the same way twice

Non-determinism is the honest technical difficulty in this work. A payload that succeeds once and fails on the next four attempts is still a finding, and a payload that fails ten times is not proof of safety. This changes how testing has to be run and how results should be read.

In practice, that means running each attack class repeatedly rather than once, varying phrasing and encoding rather than replaying a single string, and recording the success rate rather than a binary result. It means testing at the temperature and model version you actually run in production, because a finding against one model version tells you little about the next one. And it means treating a low-probability success against tenant isolation as serious, since an attacker automating a thousand attempts does not care that it works one time in fifty.

The durable answer is a regression suite. Collect the payloads that ever worked against your system, plus a standard library of injection and jailbreak patterns, and run them automatically whenever the system prompt changes, a tool is added, a data source is connected, or the model version is upgraded. This is the same discipline as a security regression test in CI, and it is the only way to know that a prompt tweak shipped on Thursday did not reopen something you closed in the spring.

Model and prompt changes are change management, whether or not you call it that

System prompts are frequently edited by product managers in a config file or an admin console, deployed without review, and never versioned in a way anyone can reconstruct. That is a change to the security behaviour of your product made outside your change management process, and once an auditor understands what a system prompt does, it becomes a question you will be asked.

Bring it inside the process you already have. Prompts in version control, changes reviewed like code, a record of who changed what and when, and the injection regression suite gating the deploy. The same applies to the model version itself. Providers deprecate and update models on their own schedule, and an automatic upgrade to a new version can change behaviour that your guardrails depended on. Pin versions where the provider allows it, and treat an upgrade as a change requiring testing rather than as something that happens to you.

None of this needs a new framework. It maps onto your existing change management, access control and vulnerability management controls, and describing it that way is far easier to evidence than inventing a parallel AI governance program.

Your model provider is a subprocessor

This is the part that trips up compliance rather than engineering. The moment customer data flows into a third-party model API, that provider is processing your customers' data on your behalf. The obligations that follow are unglamorous and specific.

Your subprocessor list needs updating, and most customer agreements require notice before adding one. Your data processing agreement with the provider needs to exist and to cover the right things, particularly whether your inputs and outputs are used for training, what the retention period is, and whether zero-retention or no-training options are available on your plan rather than merely on the provider's marketing page. If you operate under HIPAA, a business associate agreement with the model provider is not optional, and our HIPAA guidance for digital health covers where that fits. If you have customers in Quebec or handle Canadian personal information, cross-border processing disclosures and the transparency requirements around automated decision-making are live questions, not future ones.

Then there is your own logging. Teams instrument AI features heavily during development, which usually means full prompts and responses written to logs with long retention. Those logs now contain customer data, often including whatever personal information the user pasted in, sitting in a system that was scoped as operational telemetry rather than as a data store. Decide deliberately what you log, redact where you can, set a retention period, and bring the log store into scope for access control. Several of the worst AI data exposures we have looked at were not model failures at all. They were prompt logs.

What buyers actually ask, and what a good answer looks like

The AI section of enterprise security questionnaires has settled into a recognizable set of questions. Which models and providers do you use, and are they subprocessors on your published list. Is our data used to train models. How long is data retained by the provider. How do you prevent one tenant's data appearing in another tenant's responses. What testing do you perform against prompt injection, and when did you last do it. Can AI features take actions on data, and what approval is required. Can we turn the AI features off entirely.

That last one is worth preparing for, because a surprising number of buyers ask for a tenant-level switch to disable AI processing, and the answer "not currently" costs deals in regulated sectors. Building the toggle is a product decision with a compliance payoff.

The answers that move a review forward are specific and evidenced. Naming the provider, stating the retention configuration, describing authorization at the retrieval layer, and attaching a dated assessment report beats any amount of policy language about responsible AI. Reviewers are looking for signs that someone technical has thought about this, and generic assurance reads as the opposite.

When you should not buy an AI security assessment

If your AI feature is a thin wrapper that sends the user's own text to a model and returns the answer, with no retrieval over other people's data, no tools, and no actions, the honest scope is small. Read the OWASP LLM Top 10, check your output handling, confirm your provider settings, and spend the money elsewhere. There is not much attack surface to find.

If the feature is internal-only, used by your own staff over data they can already access, the risk is real but it is a different and usually lower priority than a customer-facing surface. Putting it behind the customer-facing work is reasonable.

If you already know the architecture is wrong, fix it before testing it. When the retrieval layer has no permission filtering and everyone knows it, an assessment will produce an expensive document telling you what you told us on the scoping call. Build the authorization in first, then test whether it holds. We will say this on the call rather than after the invoice, and it is the most common reason we tell a prospect to come back in two months.

The inverse is also worth stating plainly. If your product has an agent that can write to customer data, call internal APIs, or send messages on a user's behalf, and nobody outside the team that built it has tried to break it, that is the scope where testing repays itself, and waiting until an enterprise buyer's security team asks the question puts you on their clock instead of your own. Whether that work sits alongside a broader security engagement or runs as a focused piece, the scoping call starts with your architecture rather than a template. Tell us what you have built and we will tell you which parts are worth testing.