Security and trust from the first diagnostic
At Humano we design AI solutions considering security, privacy and governance from day one. Before automating or connecting models to your processes, we evaluate what data is used, who can access it, what risks exist and how to ensure the solution works securely.
Humano designs every artificial intelligence solution with privacy, risk assessment, secure architecture, traceability and human oversight from the first assessment. The goal is for AI to operate with clear limits, named owners and verifiable results, never as a black box.
Privacy and data handling
We define which data is processed, for what purpose, for how long and under whose responsibility.
AI risk assessment
We analyze privacy risks, bias, errors, operational dependency, security and exposure of sensitive information.
Secure architecture
We design solutions with access control, client separation, encryption, logging, monitoring and trusted cloud providers.
Governance and traceability
We document models, data sources, prompts, integrations, known limits, metrics and responsible parties.
Human oversight
We define when AI can automate, when it should assist and when a person must review before making decisions.
Compliance and best practices
We align designs with information security principles, data protection and modern standards such as ISO 27001, ISO 27701, SOC 2 and ISO 42001, where applicable.
Documentation for clients
For each project we can prepare technical and contractual documentation that facilitates internal security, legal and technology reviews.
Glossary
The terms we use in every conversation, defined without hedging.
- AI agent
- A system that performs tasks with access to real information and tools rather than only chatting. It looks up the data, applies a rule and leaves a record of what it did and which information it used.
- RAG
- Retrieval augmented generation. Before answering, the system retrieves the relevant fragments of your documents and answers citing that source, instead of relying only on what the model memorized during training.
- Document normalization
- Turning heterogeneous documents into structured, consistent data. A scan, a PDF and a form all end up delivering the same set of validated fields, regardless of the original format they arrived in.
- Process automation
- Running a complete workflow without human involvement in the normal cases. Exceptions are routed to a person with the context needed to decide quickly, without re-reviewing what already worked.
- Traceability
- Being able to answer where each piece of data came from and which model version produced it. It is the condition for fixing an error and reprocessing only the affected cases instead of repeating the whole batch.
- Human oversight
- Explicitly defining which decisions AI makes alone, which require approval and which always stay with a person. It is agreed per process and revisited whenever the underlying conditions change.
Trust is not promised. It is designed.
Every AI solution must have clear boundaries, defined responsibilities, controlled data and verifiable results. Our job is to help you implement artificial intelligence without turning it into a black box inside your operations.
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