Governance documentation for AI systems, cloud infrastructure, and ML operations that regulators, auditors, and boards can evaluate.
Artificial intelligence and cloud systems create new documentation challenges that traditional GRC frameworks were not designed to address. AI models produce outputs that cannot be fully explained by their inputs. Cloud infrastructure operates at a scale and velocity that conventional change control processes cannot track manually. MLOps pipelines connect data sources, training environments, model registries, and production deployments in ways that require purpose-built documentation architectures.
ELDR's AI, cloud, and MLOps documentation system is built on practitioner experience at TransUnion, SAP, and Capgemini — covering AI governance documentation, LLM deployment records, cloud security architecture documentation (AWS, Azure, GCP), Kubernetes and container documentation, and MLOps pipeline governance. The system addresses the full AI and cloud documentation lifecycle — from system design through regulatory compliance and ongoing monitoring.
NIST AI RMF-aligned organizational profile, AI risk register, and GOVERN/MAP/MEASURE/MANAGE implementation documentation for enterprise AI deployments
Article 11-compliant technical documentation for high-risk AI systems — system description, performance metrics, risk management, data governance, and conformity assessment
Structured model documentation covering model description, intended use, performance metrics, evaluation results, biases, limitations, and ethical considerations
Responsible AI policy, AI ethics documentation, acceptable use policy, AI risk management procedure, and human oversight governance documentation
Cloud security architecture documentation for AWS, Azure, and GCP environments — aligned with CSA CCM, FedRAMP, and enterprise cloud security standards
Kubernetes cluster architecture documentation, container security documentation, pod security standards, and Kubernetes configuration management records
ML system architecture documentation, data lineage documentation, model training and validation records, model registry governance, and deployment pipeline documentation
LLM system documentation covering model selection rationale, prompt engineering standards, output evaluation documentation, safety testing records, and guardrail implementation
ELDR Advisory and ELDR Consulting deliver documentation engagements across all six documentation systems. Request a consultation to discuss your documentation requirements.