AI startups often reach the same uncomfortable point: the product works, a buyer or investor is paying attention, and suddenly “we use AI” becomes a security question. The team needs proof that the AI feature was tested, not a generic statement that the model provider is secure.
This assessment is for startups that need buyer-ready evidence before launch, enterprise procurement, fundraising diligence, or production scale. It turns the messy question “is our AI secure enough?” into a scoped review with findings, fixes, and retest evidence.
Buyer-ready evidence index
An AI security assessment before enterprise procurement should connect each buyer question to a checkable artifact. The assessment produces this minimum evidence set:
| Buyer question | Evidence artifact | Decision signal |
|---|---|---|
| What exactly was tested? | Written scope: version, environment, roles, tenants, data classes, retrieval sources, tools, and exclusions | The buyer can distinguish tested from out of scope. |
| Which attack paths matter? | Threat model covering user, document, tenant, tool, and integration boundaries | Risks are tied to the actual product architecture. |
| Did a failure really occur? | Reproduction steps plus request, retrieval, model, or tool traces | An engineer can replay the observation. |
| What changes first? | Prioritized findings with impact and a concrete fix plan | Remediation has an ownerable order. |
| Did the fix close the path? | Retest record bound to the original reproduction | Fixed, not fixed, or not retested is explicit. |
| What remains uncertain? | Residual-risk and out-of-scope statement | The report does not imply certification or procurement approval. |
Passing this assessment is not a SOC 2 result, certification, or guarantee of safety. It is evidence about the named product version and scope that a buyer can review.
Buyer evidence needed?
Scope the product and the procurement deadline.
Bring the AI workflow, buyer questions, and evidence you already have. The first decision is what must be tested — and what stays out of scope.
Who this is for
You are a founder, CTO, or security owner at an AI-native startup. Your product may be seed-stage, Series A, or Series B; the common thread is that AI is part of what the customer is buying, not a small internal convenience.
This page is the right starting point when:
- A buyer asks for AI security evidence before procurement can move.
- A security questionnaire asks about prompt injection, RAG, tools, agents, evals, data handling, or model risk.
- You are launching an AI feature that touches customer data.
- You use RAG across private documents or tenant-specific content.
- An agent can call tools, write records, send messages, spend money, or trigger workflows.
- You need a fix plan that your team can execute before a deadline.
If the deadline is already tied to a named enterprise customer, the narrower AI security review before enterprise procurement may be the better page to share. To assemble the evidence before that buyer’s questionnaire even lands, work through how to prepare an AI security review before enterprise procurement. If the question is full product readiness, the underlying engagement is the AI Product Readiness Assessment.
What access and test accounts are required?
Most startup assessments run against staging or a production-like environment. You provide API or UI access to the AI workflow, representative accounts across the roles or tenants in scope, and read access to the relevant architecture and authentication flow, retrieval sources, tools, and MCP servers. Where available, bring the traces and logs needed to reconstruct model inputs, retrieved context, tool calls, authorization decisions, and outputs.
We agree least-privilege access and written rules of engagement before testing. You do not need to paste raw production secrets into a form or hand them to us directly.
What we test
The scope depends on the product, but startup assessments usually cover five surfaces.
AI behavior and prompt injection
We test direct and indirect prompt injection, jailbreak paths, system-prompt leakage, unsafe output, and whether model responses can cross a business or trust boundary. These tests map to the OWASP LLM Top 10 and the evidence standard in the LLM red-teaming guide.
Data, RAG, and tenant boundaries
We test whether the current user, tenant, role, document permission, and retrieval scope are enforced before content reaches the model. For RAG systems, this includes ingestion metadata, query filters, fallback retrieval, citations, cache behavior, and indirect injection through retrieved content.
Agents, tools, and MCP
If the AI can act, we map every tool, server, resource, prompt, credential, approval gate, and workflow state it can reach. We test whether the model can trigger an action the current user should not be able to take, or act through an over-broad service identity.
Evals and release gates
We look for the tests that prove a fix holds. A startup does not need a huge benchmark suite to start, but it does need a small set of targeted evals for the failures that would hurt the product, the buyer, or the launch.
Production controls
We review the controls around the AI feature: auth on AI endpoints, secrets, logging, monitoring, rate limits, cost controls, incident reconstruction, and rollback. A security finding matters more when the team cannot detect or reproduce it.
What you get
- A written scope naming the AI surfaces, accounts, environments, data classes, tools, and systems tested.
- A threat model for the attacker positions that matter to your product and buyer.
- Reproducible findings with request traces, prompts, retrieved context, or tool-call evidence where applicable.
- Framework mapping to OWASP LLM Top 10, OWASP Agentic Security Initiative, MITRE ATLAS, and NIST AI RMF where useful.
- A prioritized fix plan written for the engineers who will make the changes.
- One retest pass for confirmed fixes, so the report can show what changed.
The report follows the shape shown in the sample AI assessment report, with buyer-facing sections like the ones described in the AI security assessment report guide. The operating rules for access, confidentiality, data handling, and retest are on trust and data handling, and the public verification profile collects proof links a buyer can inspect before signing.
How this differs from a normal pentest
A standard penetration test is still useful, but it often stops at the application or infrastructure boundary. An AI startup needs that boundary tested and also needs the model-mediated surface tested: prompts, retrieval, tools, memory, evals, and buyer-facing evidence.
If a buyer uses the word “pentest,” clarify the scope before accepting the label. The practical difference is explained in AI penetration test vs AI red team. The short version: a normal pentest asks whether the app can be exploited; an AI assessment asks whether the full AI product behaves safely under adversarial use and whether the fix can be proven.
When to use a focused track
Use a narrower track when the risk is already clear:
- LLM Red Teaming Service for prompt injection, leakage, jailbreaks, output integrity, and model behavior.
- RAG Security Assessment for retrieval authorization, cross-tenant leakage, document poisoning, and citation integrity.
- AI Agent Security Assessment for tool abuse, approval gates, memory, workflow state, and audit traces.
- MCP Security Assessment for MCP servers, tools, resources, prompts, secrets, authorization, and agent workflows.
If you are not sure, start with the startup assessment and we will narrow the scope rather than selling a broader review than the product needs.
The startup constraint
Startups do not have unlimited time, and the assessment should respect that. We keep the scope fixed, prioritize evidence that unblocks the next business step, and avoid pretending that a small company needs the same process weight as a mature enterprise. If you are still sizing the spend, what an AI security audit costs breaks down the scope bands — from a focused feature review up to a multi-agent system — that actually drive the price.
What should not change is the evidence bar. If a finding matters, it should be reproducible. If a fix ships, it should be retested. If a report goes to a buyer, it should say exactly what was tested and what remains out of scope.
To scope the assessment, book a call with the product surface, the buyer or launch deadline, and the AI risks you already know about. The full testing approach lives in our methodology.