AI security assessment for LLM features
An AI security assessment checks an LLM feature, meaning the prompts, retrieval, tools and the code around the model, against the known ways these systems fail, and tells you which of them apply to yours.
An AI security assessment costs $4,000 to $15,000 CAD and takes one to four weeks. It maps your AI feature (model, system prompt, retrieval sources, tools, and what your code does with the output) and tests it against the OWASP Top 10 for LLM applications. It is the right first test for any product that sends user input or customer data to a language model, and it is increasingly what enterprise buyers ask about before they sign.
What it covers
| Layer | Questions it answers |
|---|---|
| Threat model | What can the model read, what can it do, and who can influence either |
| Prompt layer | Can users override instructions, extract the system prompt or change the model's role |
| Data layer | Can one user's data appear in another's answer, and what is sent to the model provider |
| Tools and agents | What each tool can change, whether actions need confirmation, and whether permissions are the minimum |
| Output handling | Whether model output is escaped before it becomes HTML, SQL, a URL or a command |
| Cost and abuse | Rate limits, token caps and loop limits on anything that calls a paid model |
| Supply chain | Model provider terms, data retention, third-party plugins and model files |
| Privacy | What personal information reaches the model, under PIPEDA and Law 25 |
Assessment, red team or pentest
The assessment is the structured review. LLM red teaming is the open-ended attack, most useful when the model can take actions. An AI penetration test covers the application and infrastructure the AI feature sits on. A chatbot over public content needs the assessment. A multi-tenant assistant over customer data needs the assessment and a pentest of the app. An agent with tools needs all three, scoped together. The test finder sorts it out in five questions.
What enterprise buyers ask
Security questionnaires now carry an AI section. The usual questions: which model and provider, whether customer data trains the model, where prompts are stored and for how long, how prompt injection is handled, and whether the feature has been independently tested. A documented assessment with remediated findings answers the last one and makes the others easy. More on what buyers ask.
The Canadian angle
Sending personal information to a model provider is a disclosure to a service provider under PIPEDA, and a transfer outside Quebec under Law 25 if the provider processes it elsewhere, which requires an assessment before it happens. Law 25 also requires telling people when a decision about them is made exclusively by automated processing. An assessment should note which of these your feature triggers. Detail on privacy law for AI apps.
What it costs
| Feature | Typical range |
|---|---|
| Single chatbot or summarizer, no customer data | $4,000 to $6,000 |
| Assistant with retrieval over customer data, multi-tenant | $6,000 to $10,000 |
| Several AI features or an agent with tools | $10,000 to $15,000 |
TrazTech lists AI and LLM security assessments from $4,000 CAD, with an executive and technical report, findings ranked by exploitability, and a retest on remediation.
Get your AI feature assessed
Tell us what the feature does, what it reads and what it can change.
Get matchedCommon questions
Do we need an assessment if we use a big provider like OpenAI or Anthropic?
Yes. The provider secures the model. You are responsible for the prompt, the data you send, the tools you connect and what your code does with the answer, and that is where findings come from.
Does the assessment include a fix?
It includes the specific fix for each finding. Making the changes is your team's or a separate scope, and a retest confirms them.
Is there a standard we can cite to customers?
The OWASP Top 10 for LLM applications is the one buyers recognise. ISO 42001 is the management system standard for AI if you need something certifiable.