DOSSIER | 04

Institutional-grade forensics spanning liability quantification, causal proof systems, and governance architectures.

Primary Corpus

Research Archive

Source documentation for liability proofs, computational law instrumentation, and autonomous systems governance.

DOSSIER 01

AVT-MRV/01

Q4 2025Forensic Liability Intelligence // Class-L

Deployment Readiness

Executive Ready

The Mens Rea Vector

AI-Driven Epistemic Analysis for Quantifying Executive Liability

Corporate software failures can no longer shield executives behind claims of ignorance. The Mens Rea Vector establishes a mathematically rigorous forensic methodology that reconstructs organizational knowledge states from digital artifacts, proving executive culpability with prima facie certainty. By combining Judea Pearl's causal inference framework with Tree of Thoughts analysis of development artifacts and Graph of Thoughts aggregation of organizational patterns, this methodology transforms git commits, pull requests, and communications into dispositive evidence of fiduciary breach.

Release Window
Q4 2025
Methodology Stamp
AVT-MRV/01
Deployment Readiness
Executive Ready
DOSSIER 02

AVT-BYZ/02

Q4 2025Systemic Risk Doctrine // Class-R

Board Docket

Board Circulation

The Byzantine Calculus

Quantifying Distributed Ledger Security as Enterprise Financial Risk

Distributed ledger technology security must transition from cryptographic theory to quantifiable financial metrics. North Korean state actors have stolen $6 billion since 2017, with $2 billion extracted in 2025 alone, demonstrating that theoretical Byzantine fault tolerance provides insufficient protection against sophisticated adversaries. This framework translates consensus-layer security into board-comprehensible risk metrics, establishes fiduciary duties for oversight, and quantifies systemic contagion across interconnected DLT infrastructure using mathematical models validated in traditional financial networks.

Release Window
Q4 2025
Methodology Stamp
AVT-BYZ/02
Board Docket
Board Circulation
DOSSIER 03

AVT-SNG/03

Q4 2025Causal Governance Protocol // Class-G

Regulatory Briefing

Regulatory Liaison

The Sangedha Framework

A Causal Forensics Protocol for Algorithmic Negligence Attribution

A definitive legal-technical doctrine establishing standards for attributing corporate liability when automated systems cause harm. Corporations deploying algorithmic systems now face unprecedented legal exposure following a convergence of three critical developments: Delaware courts have extended Caremark oversight duties to mission-critical automated systems, federal regulators have secured record enforcement actions exceeding $8 billion in 2024, and technical standards now enable mathematically rigorous causal attribution of algorithmic failures to specific governance breakdowns.

Release Window
Q4 2025
Methodology Stamp
AVT-SNG/03
Regulatory Briefing
Regulatory Liaison