MGLA is a stack — seven layers of data, representation, augmentation, simulation, legal reasoning, governance and feedback. Each layer has a specific responsibility. They are not interchangeable, and they are not optional.
Ingest from internal documents, system telemetry, satellite and sensor data, system APIs, legal databases, and supplier metadata. Normalised, time-stamped, version-controlled.
A knowledge graph and vector embeddings of every entity in scope — systems, processes, rules, contracts, owners. Time-series for telemetry.
LLM agents, fine-tuned on internal data and legal corpus, propose structured improvements — t1. Output is typed change proposals, not free-form prose.
A what-if engine combining rule/consequence modelling, economic models, and risk models. Monte-Carlo and scenario-sampling for uncertainty. Reinforcement learning for policy markers.
Machine-readable rule library, mapping between action and the articles, recitals and case law that govern it. DPIA generation triggers on material gap change.
Escalation thresholds, human oversight, policy sandbox for safe simulation. Tamper-evident audit logs; optional blockchain anchoring.
Visualisation, dashboards, and APIs into existing decision support. The loop closes by returning to the systems it observed.
Agent proposals run in an isolated environment before any change can reach a production system.
Every recommendation carries the data, the chain of inference, and the rule version that produced it.
Material gap changes auto-generate a DPIA draft. The legal artefact is part of the loop, not after it.
Auto-apply, human-approve, and block are policy thresholds, not defaults. Each is a deliberate choice.
A live regulatory analysis framework, developed and maintained by IT Law 2035.