“He tingut una experiència excel·lent amb aquesta empresa. Des del primer moment m’han ofert un tracte molt proper i professional, escoltant el que necessitava i buscant sempre la millor solució. Es nota que cuiden els d…”
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CYBERSECURITY
LLM and AI application security
We secure your AI applications and systems: adversarial testing (red teaming), prompt injection mitigation and hardening of the model supply chain.
LLM and AI application security
LLM security matters when you need to protect applications that use language models from specific attacks, not a traditional network audit. Before selecting tools, we define the journeys, data and decisions that must remain under your company's control.
We work from Barcelona and Madrid and remotely with teams across Spain, Europe, LATAM and the United States. The process combines decision sessions, verifiable releases and documentation so progress stays visible.
We hand over code, repositories, configurations and documentation in accounts owned by your company. You can therefore audit, evolve or change supplier without relying on private access or licences.
We secure your AI applications and systems: adversarial testing (red teaming), prompt injection mitigation and hardening of the model supply chain. That is why we prioritise a maintainable, measurable solution ready to evolve without rebuilding its foundations.
THE CHALLENGE
The challenges around LLM security
The value of LLM security depends on a design that serves operations, not only a demonstration.
Solutions that do not fit
When language models open new attack paths, a template tool forces the process to bend to the product. The result is generic answers and manual work that does not disappear.
Fragmented context
Without ordered access to your data and systems, the solution responds incompletely. Every decision depends on information scattered across tools and emails.
No control or measurement
A project with no owners, limits or metrics soon stalls. We leave traceability and clear criteria so every result is assessed with evidence.
APPROACH
From need to protecting AI applications in production
We sequence technical and operational decisions so the first release is useful and maintainable.
Context map
We document data, systems and use cases to define how LLM security must fit daily work, before proposing architecture.
Data and security
We define access, sensitive data and controls aligned with ENS practice when required. Privacy is designed from the first sprint.
Incremental releases
We prioritise a usable, measurable use case; the rest is planned against value. Every release leaves something running for protecting AI applications in production.
Handover and ownership
Code, data and documentation stay under your company's control. Your team can maintain and evolve the solution without depending on us.
DELIVERABLES
What LLM security leaves ready
We deliver an operational foundation your team can govern, measure and continue to evolve.
Working solution
LLM security ready for the agreed journeys and reviewed before wider adoption.
Documented integrations
Connections, systems of record, permissions and limits recorded so the solution can be maintained without relying on individual memory.
Code and configuration
Repositories, workflows and configuration under your company's control, transferable to another team when needed.
Evolution plan
Metrics, accountable owners and a prioritised backlog to decide what improves after the first release.
TRUST
Technology applied to real operations
We apply AI security criteria aligned with the OWASP Top 10 for LLM and ENS where required.
- Scope, assumptions and risks visible before the project is committed.
- Integrations, permissions and data designed for real operations.
- Reviewable releases before the solution reaches the whole organisation.
- Code, configuration and documentation under your company's control.
FAQ
Questions about LLM security for business
Have a project in mind?
Tell us what you need around LLM security for business. We will help you turn it into a clear, viable delivery plan.
