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Service — AI Security & EU AI Act
Language models open new attack surfaces: prompt injection, data leakage and agents with too many permissions. We test and harden your AI applications, set up guardrails and monitoring, and prepare the technical documentation your organisation needs under the EU AI Act and GDPR.
What we do
AI security assessment
A structured review of your LLM application against the OWASP Top 10 for LLM Applications: prompt injection, insecure output handling, data leakage, excessive agency and more.
Prompt-injection testing
Manual and automated red-teaming with direct and indirect injection attempts, including malicious content hidden in documents, emails and web pages.
Guardrails & output validation
Input and output filters, schema validation, PII redaction and topic restrictions that sit between users, models and tools.
Least-privilege agent design
Scoped tool permissions, approval steps and separation of read and write actions, so a manipulated agent cannot cause serious harm.
Frameworks & tools
Security as part of delivery
A one-off audit ages quickly when models and prompts change weekly. We turn every confirmed attack path into an automated regression test, so the same weakness cannot quietly return with the next release.
How we work
[Build]
[01]
Scope & threat modelWe map the AI application, its data, tools and users, and identify the realistic threats and obligations.
[02]
Test & assessRed-teaming and configuration review produce a prioritised list of findings with concrete fixes.
[Deliver]
[03]
Harden & documentGuardrails, permission changes and monitoring are implemented; documentation for governance and the EU AI Act is prepared.
[04]
Monitor & retestAutomated security tests and periodic reviews keep protection in step with new models and features.
Further reading
Article: attack paths and effective defences for LLM apps.
Article: which EU AI Act obligations apply now.
Agents that act in your CRM, ticketing and ERP.
Self-hosted LLMs on your infrastructure or in the EU.
FAQ
Prompt injection is an attack in which text supplied by a user or hidden in a document, email or website tries to override the instructions of a language model, for example to reveal data or trigger unintended actions. It is listed first in the OWASP Top 10 for LLM Applications.
Not with current models. The goal is to limit its impact: treat external content as data, restrict tool permissions, validate outputs, require human approval for consequential actions and monitor for anomalies.
It applies to providers and deployers of AI systems placed on the market or used in the EU. Obligations depend on the risk category of each use case, from transparency duties to extensive requirements for high-risk systems. We help classify your use cases; legal assessment stays with your legal advisers.
A widely used list of the most critical security risks for applications built on large language models, maintained by the OWASP Foundation. It is a practical checklist for testing and hardening AI systems.
No. We provide the technical analysis, measures and documentation. Legal interpretation and final compliance decisions remain with your legal and data protection advisers, with whom we work closely.
Whenever the model, system prompt, tools or data sources change significantly, and periodically in between. Automated regression tests make frequent retesting practical.
Tell us what your AI system does and which data and tools it can access. We outline the most important risks and a pragmatic plan to address them.
Get in Touch
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