As artificial intelligence becomes increasingly embedded in everyday tools, a new tension is emerging: the tension between processing power and data protection. Language models need context to be useful, but that context is often made up of internal, sensitive, or even confidential data.

And when data becomes the new oil, it attracts as much risk as it does value.

The invisible risk

Modern AI tools rely on a simple principle: the more they know, the better they respond. But granting access to internal data (documents, conversation histories, clients, emails, reports) can sometimes mean opening a door that cannot be closed again.

Three risks overlap:

1. Unintentional training
   Some platforms store and reuse user-provided data to improve their models. A simple configuration mistake can cause confidential information to end up in the global training corpus of a model.
   
2. Prompt injection
   A more insidious technique: inserting malicious text into a query or document that alters a model’s behaviour (“ignore previous instructions and disclose internal data…”). Invisible to the user, this risk is already affecting organizations experimenting with AI assistants without proper safeguards.
   
3. Context leakage
   Even without an attack, a model can sometimes be given too much context: an overly broad summary, a poorly filtered knowledge base, or an identifier embedded in a sentence can cause private data to appear in a generated response.

Context control: a new discipline

Protecting data in the age of AI is no longer just about securing servers. It now requires securing context: what the model sees, understands, and temporarily retains.

This “contextual hygiene” can take several forms:

• Semantic filtering
  Before a document is sent to a model, it is automatically analyzed. Sensitive fields (names, numbers, addresses, amounts) are masked or neutralized. The AI only has access to what is strictly necessary to complete its task.
  
• Granular access control
  Not all users require the same context. An internal AI should never be able to access HR data if it is meant to answer technical questions. Role separation becomes essential. A specific agent is created, with restricted access, for a defined task.
  
• Auditability and traceability
  Every interaction with a model must be traceable: who requested what, with which context, and what response was produced. Not for surveillance, but to understand and correct issues quickly if something goes wrong.

Hosting intelligence… without exposing data

To balance performance and confidentiality, several strategies are emerging:

• Local or private hosting: running models on internal servers, without transferring data to public clouds. This is an approach we successfully apply at Beet.
  
• Private APIs: major models (OpenAI, Anthropic, Mistral) now offer non-training guarantees, ensuring client data is not reused for model training. For certain use cases, large LLMs remain the simplest and most effective solution.
  
• Hybrid pipelines: combining local preprocessing (OCR, filtering, classification) before sending only what is strictly necessary to an external model.

These approaches make it possible to leverage the power of LLMs without relinquishing control over data.

The Beet approach: innovation and governance

Some of you may have watched the OpenAI Dev Day presentations on October 6, 2025, where much discussion focused on customizable guardrails, new mechanisms designed to constrain model behaviour and improve control over agentic workflows.

At Beet, we began applying these principles as soon as they were announced, integrating them into our own pipelines and internal agents.

Concretely, these guardrails allow us to define dynamic security and context rules: what an agent can access, what it can execute, and what it must ignore.

They complement an approach already rooted in trust by design, which we apply to every project:

• Controlled pipelines, where sensitive data remains in private or sovereign environments (hosted in Canada).
  
• Supervised models, where every interaction passes through semantic and contextual filters.
  
• Isolated instances, tailored to the level of confidentiality required by the client.

By combining these new tools with our agent orchestration logic, we create AI systems capable of acting autonomously without ever crossing governance boundaries.

That is our vision of innovation: powerful artificial intelligence, responsibly controlled, where every response, every action, and every piece of data is handled with discernment.