Find Vulnerabilities.
Strengthen Resilience.
Adversarial testing for AI systems that are already connected to your data, your tools, and your customers, by people who build them.
Is this the right fit?
Who this is for
You have an AI system in or near production that can read business data, call tools, or reach customers. You need to know whether its permissions and safeguards hold when someone pushes on them.
Who this is not for
- An AI experiment that is not connected to business data, systems, or customers.
- A system you are still planning. If your team needs help designing and building it, start with Consulting.
- Network penetration testing or compliance certification. We do not offer either service.
An architecture review and an adversarial test, run as one engagement
We examine what your system can reach, then test what happens when someone tries to redirect it.
Cloud configuration and access
Identity, trust boundaries, keys, storage, network exposure, and logging across the environments your AI touches.
AI behavior under pressure
Hostile prompts and retrieved content, unsafe tool use, and information leaving through answers or actions.
Permissions across the whole system
Tiered test accounts to check that each user can reach only the data and actions they are entitled to.
Evidence and a fix
Confirmed findings with severity, reproduction steps, business impact, and a remediation roadmap. Relevant cloud and OWASP references included.
We review the system, not just the prompt box
A manipulated agent and an over-broad cloud role can be parts of the same failure. We review the connection between them.
A security practitioner works alongside an engineer who builds these systems, so the finding comes with a practical path to fixing it.
Peace of Mind Afterwards
You know what it can reach
Permissions and trust boundaries have been tested rather than assumed to hold.
Your team knows what to fix first
A technical findings report sequences the work by risk and effort. An executive summary explains the impact in plain language.
You can keep checking after we leave
Repeatable AI tests help your engineers check material changes to models, prompts, data, and integrations.
Four weeks, two deliverables
- 01
Kickoff and access
Agree the scope, walk through the system, and provision read access and tiered test accounts.
- 02
Cloud review
Review configuration, identity, and trust boundaries across the environments in scope.
- 03
AI testing
Test the paths from user input and retrieved content to tools, data, and external responses.
- 04
Report and review
Review the technical findings report and executive summary with your team, then finalize the remediation roadmap.
Four weeks is the standard scope. Your team contributes roughly a half-day across the walkthrough, access, and draft review. Critical findings are escalated within 24 hours.
How engagements are structured
Scope and price follow the system: what it connects to, what it can do, and the environments to review. Agreed before work begins.
Material changes to models, prompts, data, or integrations can change the result. Retesting follows those changes.
Questions we get
How does this fit with our existing security program?
What access do you need?
Do you test against production?
What if you find something serious?
Tell us what it can reach
Describe what you have deployed and what it is connected to. We will tell you whether an assessment is worth doing and what it would cover.