Our guarantee that makes the Group’s solutions ready for real-world adoption in companies
Protection and anonymisation of sensitive data to reduce the risk of exposure to external models and services, preserving the confidentiality and integrity of information.
Controls, documentation and traceability to support compliance with applicable AI regulations. Monitoring of regulatory developments and updating of safeguards accompany the solution over time, according to the context of use and the level of risk.
Management of access and permissions, monitoring of usage, costs and performance, and routing of tasks to the models best suited to them, balancing quality of results, time and consumption.
Model robustness and explainability of the algorithmic basis and outputs, together with performance checks, error handling and human oversight at critical steps.
A look at some of the system components that strengthen the solutions they are integrated into
Secure-by-design architectures and data protection.
Anonymisation, pseudonymisation and filtering of sensitive data before it is sent to models.
Encryption of data in transit and at rest, credential vaults and secure API gateways.
Federated learning frameworks for distributed processing without centralising data.
Security testing, prompt-injection protection and incident monitoring.
Regulatory requirements translated into verifiable controls.
Mapping of applicable requirements and risk assessment by use case.
Data lineage and audit trails to trace data origin, processing and usage.
Versioning of models, configurations and technical documentation.
Data retention and deletion policies, with evidence to support audits.
Centralised AI orchestration and control.
Identity & Access Management with roles, permissions and access segregation.
Centralised dashboard for usage, consumption, costs and performance.
Multi-model routing based on task, quality, latency and cost.
Budgets, usage quotas, alert thresholds and approval workflows.
Model validation and operational continuity.
Benchmarks and robustness tests on data representative of the context of use.
Explainability tools and output consistency checks.
Monitoring of performance degradation and data changes (drift).
Fallback and recovery mechanisms and human oversight at critical steps.
