GDPR
Privacy-conscious architecture and data-handling controls designed to support GDPR-compliant deployments.
Official regulationESD Resources
Enterprise-grade AI. Built for responsible deployment.
Responsible AI is not a badge added at the end of a project. It begins with the purpose of the system, the data it uses, the decisions it can make and the points where people remain in control.
ESD’s architecture and deployment methodology are designed to support organisations working across leading international AI and data-protection frameworks.
01 International frameworks
Each framework has a different legal or governance role. Our language reflects that distinction: laws may impose obligations, voluntary frameworks guide risk management, and certification applies only to the organisation and scope shown on a valid certificate.
Privacy-conscious architecture and data-handling controls designed to support GDPR-compliant deployments.
Official regulationDesigned to support organisations in meeting applicable AI governance, transparency, oversight and risk-management requirements.
European Commission overviewAI governance practices aligned with internationally recognised principles for trustworthy and responsible AI risk management.
Official NIST frameworkArchitecture designed to support deployments subject to applicable Canadian privacy and data-protection requirements, including PIPEDA where applicable.
Privacy guidance for businessesOur underlying AI technology provider is currently progressing toward ISO/IEC 42001:2023 certification for its Artificial Intelligence Management System.
Official ISO standard overviewOne Digital Employee. Enterprise-ready governance across multiple jurisdictions.
02 From principles to deployment
A responsible deployment is shaped around the actual process, risk level, data and systems involved. The appropriate controls are defined before implementation and reviewed as the role evolves.
A defined business purpose, role, authorised actions and clear limits.
Access controls, data minimisation and handling rules appropriate to the deployment.
Approval points, exception routes and a named person responsible for the process.
Logging, monitoring and review requirements agreed for the work being delegated.
Assessment of material risks, testing and review when the role, systems or data change.
Relevant system, data-flow, oversight and governance information for client review.
03 Clear language, accurate claims
AI compliance depends on the specific deployment and on both supplier and client responsibilities. We therefore avoid language that could imply an automatic legal guarantee or a certification that has not yet been awarded.
The architecture and deployment controls are intended to help an organisation meet relevant requirements. The final compliance position depends on the use case, configuration, data, contracts, jurisdiction and how the organisation operates the system.
The governance approach draws on the principles and practices of the named framework. For NIST AI RMF, this refers to a voluntary risk-management framework rather than a certification.
The certification process is underway, but certification has not yet been awarded. Once completed, any claim must identify the certified legal entity and remain within the scope shown on the certificate.
04 Responsible deployment
The same Digital Employee can carry very different risks depending on what it does, whose data it handles and whether it can act independently.
Before implementation, ESD works with the client to define the role, permissions, approval points, exception handling and practical governance requirements appropriate to the proposed deployment.