TAG ARCHIVE
compliance
5 MARIA OS blog articles tagged compliance. Safety, accountability, fail-closed gates, auditability, and human-in-the-loop control for AI agents. This canonical topic archive supports search engines and LLM retrieval.
Judgment OS / Decision Intelligence OS
Core MARIA OS research on turning organizational judgment into executable decision systems.
Agentic Company Architecture
Research on human-agent organizations, delegation boundaries, role topology, and governed autonomy.
Responsibility Gates and AI Governance
Safety, accountability, fail-closed gates, auditability, and human-in-the-loop control for AI agents.
Multi-Agent Mathematics
Formal models for convergence, stability, game theory, graph dynamics, and multi-agent evaluation.
Evidence, RAG, and Knowledge Governance
Evidence bundles, retrieval architecture, Graph RAG, knowledge trust, and auditable reasoning pipelines.
Agentic R&D and Judgment Science
Research operations, simulation labs, judgment science, recursive improvement, and experimental AI governance.
Audit Universe Runtime: Agent Design for Executing Audit Procedures as Runtime Operations
Transforming ISA/JICPA standards into executable agent specifications — from sampling strategies to substantive testing, within a MARIA OS governance architecture
Traditional audit procedures are encoded in prose-based standards that resist automation. This paper presents the Audit Universe Runtime — a multi-agent execution environment within MARIA OS that compiles audit standards (ISA, JICPA) into executable agent task specifications. We formalize audit procedures as state machines, design sampling strategy agents with statistical rigor, implement real-time anomaly detection during substantive testing, and prove audit completeness through a formal coverage model. The architecture maps MARIA coordinates to engagement structures, enabling continuous auditing with immutable audit trails and human-agent collaboration gates at every materiality threshold.
Audit Universe Runtime：監査手続をランタイム・オペレーションとして実行するAgentアーキテクチャ
ISA/JICPA基準をエージェント実行仕様に変換する — サンプリング戦略から実証的テストまで、MARIA OSガバナンスアーキテクチャの中で
従来の監査手続は、自動化に抵抗する散文ベースの基準書に記述されている。本論文では、MARIA OS内のマルチエージェント実行環境であるAudit Universe Runtimeを提示する。ISAおよびJICPA基準を実行可能なエージェントタスク仕様にコンパイルし、サンプリング戦略エージェントを統計的厳密さで設計し、実証的テスト中のリアルタイム異常検知を実装し、形式的なカバレッジモデルを通じて監査の完全性を証明する。このアーキテクチャはMARIA座標をエンゲージメント構造にマッピングし、すべての重要性閾値における人間-エージェント協働ゲートと不変の監査証跡による継続的監査を可能にする。
Dynamic Regulatory Synchronization: Formal Models for Real-Time Policy Update Propagation
Ingesting regulatory amendments as Policy Set deltas and verifying gate rule consistency through automated compliance checking
Regulatory environments can change faster than manual compliance workflows can absorb updates. This article models policy updates as algebraic deltas and focuses on internal rule-verification mechanics, not on turnkey legal automation or real-time compliance certification. The benchmark figures are best read as replay-style engineering measurements on a curated corpus.
AML Detection Gate Optimization: Constrained Loss Minimization for Anti-Money Laundering
Formalizing gate strength as a continuous control variable to minimize the combined cost of false positives, missed detections, and investigation delay in AML compliance pipelines
AML programs face a costly tradeoff between false positives, missed detections, and investigation delay. This paper formalizes AML detection as constrained loss minimization over gate strength `g` and treats the benchmark numbers as synthetic scenario outputs, not as universal regulatory thresholds or turnkey compliance claims. The practical value of the article is in the control framework, escalation logic, and risk-based calibration structure.
Auditable Financial Decision Traceability: Evidence Graph Models for Regulatory Compliance
Formal evidence graph construction and matrix-algebraic traceability for reconstructing every financial decision under SOX, Basel III, and MiFID II
Regulatory reconstruction of AI-driven financial decisions is difficult when logs are fragmented, timestamps drift, or causal links are missing. This paper introduces a formal evidence-graph model where each decision is an immutable node in a directed acyclic graph, linked by typed causal edges with cryptographic evidence bundles. We define `TraceCompleteness` as `TC = |reproducible decisions| / |total decisions|` and report `TC >= 0.997` across evaluated SOX, Basel III, and MiFID II audit scenarios.