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All solutions ↗AI system protection
AI system protection means controlling which data reaches the model, which instructions are trusted and which actions the application may execute. We implement these boundaries around the model so security does not depend on the prompt alone.
01 / Who it is for
You may already have an assistant, RAG workflow, agent or AI feature reading documents and communicating with company applications. As more context and tools are connected, the boundary between what the model proposes and what the system actually permits becomes critical. For a new product, we design these controls from the start. For an existing one, we review data paths, permissions, model integration and actions that can create real consequences.
02 / What the project can include
We define which data may reach the model and what must be filtered, masked, anonymised or replaced before a request. Necessary context is separated from sensitive information, with rules based on user, purpose and source.
Prompts, retrieved documents and external content are treated as inputs that cannot grant themselves additional authority. Tools and actions are authorised separately, and sensitive operations can require human confirmation before execution.
We validate output structure and content before it is executed or passed to another system. We retain the information needed to understand which rule applied, which action was permitted or blocked and the reason.
03 / Illustrative example
An agent receives a free-form request and retrieves customer information from a CRM. One retrieved document also contains an instruction attempting to change the agent's behaviour. The control layer does not treat that instruction as authorisation: it filters permitted data, keeps the agent within its assigned rights and evaluates the proposed action. The agent may prepare an update, while deleting a customer or sending an external document remains blocked or requires approval. The decision and result remain traceable.
04 / How we work
We identify models, data sources, RAG, tools, users and available actions. We distinguish information the model can read from operations that can create effects in other systems.
We establish permitted data, access levels, prohibited actions and mandatory approval points. We also define behaviour for unclear information, hostile instructions and invalid outputs.
We add filtering, validation, authorisation policies, tool-calling restrictions, output checks and relevant audit logging. Critical controls remain in the application rather than existing only in the model prompt.
We exercise normal and abusive cases: prompt injection, hostile instructions in documents, unauthorised access, invalid output and excessive-action attempts. Rules are adjusted before access is expanded.
05 / What you receive
What the project can include
Where we start
Start with the current AI flow: what enters, which model is used, what information it sees and what it can do after a response. From there we identify the attack surface and the first control set.
Assess the security of your AI system
