MGLA does not run analyses on a schedule. It runs a continuous loop. Each iteration captures the present, projects an improved present, and simulates plausible futures — then routes the gap between them into action.
A timestamped, hashed, immutable snapshot of every artefact the framework reads — documents, telemetry, contracts, regimes — at a single moment.
An AI-augmented projection of the same state under recommended remediation — concrete, structured change proposals rather than free-form prose.
A set of plausible forward scenarios — regulatory amendment, system drift, contractual change — explored under Monte-Carlo and scenario-sampling.
A change in the environment — a code deploy, a clause amendment, a regime update — triggers a snapshot.
Every relevant rule fires against the snapshot. Findings emerge, bound to the evidence that produced them.
The agent layer proposes structured remediation — t1 — with confidence scores and risk-reduction estimates.
tF projects the same state forward under scenario-sampling: what would this finding cost in six months, under drift?
The gap is routed to a named owner with policy-driven thresholds — auto-apply, human-approve, or block.
Every action, every actor, every artefact is appended to the chain of evidence. The loop closes; the next begins.
For regulatory analysis, "real time" means the loop fires on every relevant change — not every second. A typical institution sees evaluation cadences of minutes to hours, with critical regimes (incident reporting, breach notification) running on the faster end of that range.
The audit cycle was annual. The replacement is not a stream — it is a continuous, evidence-bound, governable loop.
A live regulatory analysis framework, developed and maintained by IT Law 2035.