Deterministic Runtime Governance: Moving from Paper Compliance to Machine Reality (BIGF)

A developer has published the Business Impact Governance Framework (BIGF), an approach that translates statutory mandates such as DORA, NIS2 and the EU AI Act, along with internal Business Impact Analysis baselines, into machine-executable runtime policy parameters. The framework places an enforcement gate directly in the execution path, evaluating each request or autonomous agent call against loss, telemetry and context boundaries and hard-halting workloads when a computed authority function reaches zero, while minting a Canonical Decision Record as audit evidence. The reference architecture and proofs are released under CC BY-NC 4.0, with commercial deployment requiring formal verification and licensing.

In traditional enterprise IT, governance follows a broken rhythm: developers push code, and compliance teams check Word documents once a year during an audit. When things go wrong in production, organizations scramble to schedule an emergency meeting while systems degrade. Implementing the Business Impact Governance Framework BIGF replaces subjective human triage with deterministic runtime enforcement across four core operational phases. Statutory mandates such as DORA, NIS2, or the EU AI Act and internal Business Impact Analysis BIA baselines are translated directly into machine-executable parameters. This is not a policy manual for a committee. It is a configuration parsed at runtime: policy id: POL-DORA-089 target: payment-gateway-agent actuation boundary: B max loss per min eur: 50000 S telemetry constraints: database latency ms: max 150 error rate percent: max 1.5 active replica count: min 2 T context: peak hours utc: 06:00 to 22:00 allow degradation outside peak: false on boundary breach: 403 HARD HALT Every incoming request or autonomous agent call is evaluated against these explicit boundaries. The engine sits directly in the execution path. Instead of guessing risk levels, it calculates a deterministic function: Authority = f B, S, T The moment Authority = 0 , the runtime enforcement gate drops: The workload or autonomous AI agent is immediately locked out before it can corrupt downstream databases or execute invalid transactions. Concurrent with the lockout, the system mints a Canonical Decision Record CDR : When auditors or regulatory authorities inspect the platform, the organization presents mathematical proof of runtime compliance rather than human retrospectives. Traditional IT operates on hope: deploy the service, assume policies are followed, and clean up after the blast radius expands. Deterministic runtime governance flips the paradigm: a workload or agent is permitted to execute ONLY while real-time telemetry mathematically proves that the system remains inside its safe operational envelope. The underlying BIGF architectural patterns, mathematical proofs, and reference models are licensed under Creative Commons Attribution-NonCommercial 4.0 International CC BY-NC 4.0 . You are free to review, benchmark, and evaluate the logic academically. However, commercial exploitation, productization, or deploying these reference models directly into mission-critical production environments requires formal verification and licensing. Do not copy reference architecture blindly into your production cluster expecting magic without the underlying state engine. just google it: big framework erkki laine