Security, privacy, responsible AI and accountability should be visible before procurement — not discovered after deployment.
Use the Trust Centre to understand the operating principles and evidence structures DataLgorithmics applies to AI and data work.
Security Principles
How DataLgorithmics approaches identity, access, secure engineering, logging, resilience and operational security.
Open control area →Privacy & Data Handling
A practical framework for data minimisation, purpose limitation, access, retention, transfer and client control.
Open control area →Responsible AI
Principles and operating controls for accountable, transparent and proportionate AI use.
Open control area →AI Quality Framework
A measurable quality framework covering task success, grounding, safety, escalation, latency, cost and user experience.
Open control area →Human Accountability Standard
Assign accountable business, technical and governance ownership to every production AI capability.
Open control area →Vendor & Model Governance
Evaluate and monitor external AI providers, models and critical vendors across capability, security, privacy and commercial risk.
Open control area →AI Incident & Escalation
Define what constitutes an AI incident, how it is triaged, contained, investigated and learned from.
Open control area →Subprocessor Register Framework
A transparent framework for documenting third parties that may process customer data when a live engagement requires them.
Open control area →Current certification position
Procurement-ready pathways
Control questions and evidence.
Data handling and contractual requirements.
Inventory, ownership, evaluation and review.
SLA, support and incident routes.
Validated claims and documentation.