Patterns
Implementation control library
Find the implementation control behind the gap.
138 implementation controls mapped to architecture, obligations and evidence. Search the problem; open implementation depth only when you need it.
AI Data Mesh Integration
Enterprises adopting Data Mesh architectures face a structural challenge when integrating AI: the mesh's domain-orient…
AI Training Data Governance
AI training data governance is the foundation of responsible AI. A model is only as trustworthy as the data it was tra…
Data Lineage for AI
Regulators, auditors, and risk committees increasingly demand that organisations explain not just what an AI model pre…
Data Quality for AI
AI systems are uniquely sensitive to data quality failures in ways that traditional BI systems are not: a 3% missingne…
Federated Learning Pattern
Federated learning enables multiple organisations or devices to collaboratively train a shared AI model without any pa…
Privacy by Design for AI Data
Privacy-by-design mandates that privacy controls are embedded into AI data pipelines at every stage — not bolted on re…
Real-Time Feature Engineering
AI models that make real-time decisions — fraud detection, personalised recommendations, credit pre-screening — requir…
Synthetic Data Generation
Many of the most valuable enterprise AI use cases — fraud detection, clinical risk prediction, credit underwriting — r…