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Use AI without losing
control of your data.
Two halves, one engagement. We find and govern the AI tools your staff already use, and we test and secure the AI you build, against the frameworks your auditors recognize.
Your staff adopted AI before your policy did.
Employees paste source code into chatbots, upload spreadsheets to analysis tools and install AI browser extensions nobody reviewed. That data may be stored, retained or used to train a provider's model. At the same time, the AI your own teams build, the assistant with access to internal documents or the customer-facing chatbot, is a new attack surface that a traditional penetration test does not cover.
This assessment covers both. On the shadow-AI side we discover every AI tool in use, map what data flows into each, and put monitoring, IP protection and endpoint policy in place. On the build side we test your models and integrations for prompt injection, data leakage and abuse, design the data boundaries, and leave you with governance an auditor accepts. The aim is not to stop AI adoption; it is to make it safe.
- An inventory of every AI tool in use, sanctioned or not, with a risk rating for each
- A data-flow map: what is sent to each tool, how the provider stores and retains it, and whether it trains on it
- Prompt-injection and data-leakage findings for the AI you build, with proof of concept and reproduction steps
- Data-boundary design for AI systems that touch internal or customer data
- An AI acceptable-use policy, approved-tool list and endpoint controls you can enforce
- An executive summary for leadership and a technical appendix for engineering
Find it, test it, govern it.
Discover the AI in use
We find AI tools through network and SaaS discovery, SSO and OAuth grants, browser extension inventory, developer tooling and short staff interviews.
Map the data
For each tool we map what data goes in, how it is processed and retained, and classify the exposure against your data classification policy.
Test what you build
Direct and indirect prompt injection, system-prompt extraction, tool and function-calling abuse, output-filter bypass and training-data leakage, mapped to OWASP and MITRE ATLAS.
Set the controls
Policy, approved tools, monitoring and endpoint enforcement, sized to keep the productivity and remove the leak.
Govern it
Risk classification, approval workflows, model documentation and AI-specific incident procedures, aligned to the NIST AI Risk Management Framework.
Two halves ofthe same problem.
Shadow-AI control
Visibility and policy for the tools already in the business.
- Tool risk analysis
- Continuous usage monitoring
- IP protection
- Endpoint policy enforcement
Securing the AI you build
Testing and design for models, assistants and integrations you own.
- Model and prompt-injection review
- Training-data leakage and model extraction testing
- Data-boundary design
- Governance an auditor accepts
Ready for the rulesas they arrive.
- Mapping of your AI systems to EU AI Act risk categories and obligations
- Algorithmic impact assessments aligned to ISO/IEC 42001 and the NIST AI RMF
- UAE PDPL, Saudi PDPL and Singapore PDPA implications of AI-processed personal data
- Bias and fairness testing for models that make decisions about people
- Model governance: version control, access, deployment pipelines and drift monitoring
- Incident response procedures for AI-specific failures
Who it is for
Any organization whose staff have started using AI assistants, which is nearly all of them, and any organization building AI into its own products or internal tools.
- HLÍN, enforce the policy at the prompt
- SÝN ADR, monitor the AI agents your developers run
- vCISO, own AI governance at leadership level
- Gap assessment, see AI risk alongside the rest of your programme
What buyers ask.
We blocked AI tools.
Blocking moves the use to personal devices, where you see nothing. Visibility and policy work better than a ban, and they let you keep the productivity.
Isn't this just a penetration test?
A penetration test looks at your applications and network. AI systems fail in different ways: prompt injection, data leakage through outputs, tool abuse. We test for those specifically, mapped to OWASP and MITRE ATLAS.
We only use approved tools like Copilot.
Approved is a good start. The assessment confirms what data reaches those tools, how it is retained, and whether anything unapproved is running alongside them.
Will this slow down our AI projects?
No. The goal is to let you deploy with confidence. Findings come with remediation your data science and engineering teams can act on.
Know where your AI sends your data.
Put an engineer on a call to scope an AI security assessment. No charge, no commitment.