Scientific Modeling & DefensibilityMethodology
Unlike checklist compliance or static training quizzes, CyberPosture's methodology rejects subjective guesswork. Our architecture is engineered on empirical behavioral psychology, aviation human reliability engineering, actuarial loss mathematics, and high-assurance data integrity standards.
Every employee risk signal, 0–1,000 resilience index calculation, and automated audit workpaper adheres to validated, peer-reviewed scientific frameworks—ensuring complete mathematical defensibility for individuals, CISOs, and independent audit teams.
psychology_altCognitive Exploit Modeling
Behavioral Psychology
Cialdini’s 6 Principles of Influence
Robert Cialdini's seminal persuasion framework models how social engineering actors weaponize human cognitive heuristics: Authority (executive spoofing), Scarcity & Urgency (panic triggers), Consistency (commitment traps), Reciprocity (quid pro quo), Social Proof (herd compliance), and Liking (affinity pretexting). HVI measures resistance against these exact behavioral levers.
Human Vulnerability Index (HVI)Scientific Model →
warningSystemic Failure Precursors
Human Reliability Engineering
The Dirty Dozen Human Factors
Derived from high-reliability aviation safety, the Dirty Dozen isolates the 12 psychological and environmental conditions—including Pressure, Urgency, Fatigue, Complacency, Distraction, and Norms—that precede 80%+ of security breakdowns. CyberPosture models situational context rather than assigning punitive blame.
Human Factor AnalysisScientific Model →
verifiedAudit-Grade Verifiability
High-Assurance Evidence Governance
ALCOA / ALCOA+ Data Integrity
Originating in regulated life sciences and pharmaceutical compliance, ALCOA+ mandates that every risk signal and RAMP audit workpaper is Attributable, Legible, Contemporaneous, Original, and Accurate (+ Complete, Consistent, Enduring, Available), providing audit-ready cryptographic defensibility.
Audit Integrity StandardScientific Model →
paymentsDollar-Value Pricing
Actuarial Financial Loss Modeling
FAIR™ Quantitative Risk ($VaR)
Factor Analysis of Information Risk (Open FAIR™) replaces subjective 'High/Medium/Low' heatmaps with probabilistic Value-at-Risk ($VaR) loss distributions. It prices risk exposure in concrete dollars using value at risk across productivity loss, incident response, regulatory fines, and reputation impact.
VALORA AI & RAMPScientific Model →
smart_toyAlgorithmic Exposure Guardrails
AI Governance & Algorithmic Safety
NIST AI RMF 1.0 & ISO/IEC 42001
The premier global benchmarks for Artificial Intelligence Management Systems (AIMS). The VALORA engine benchmarks autonomous LLMs, agentic workflows, and training data pipelines—uncovering surprising algorithmic exposure and pricing it into concrete dollar figures using value at risk.
VALORA AI EngineScientific Model →
fact_checkSOC 2 · GDPR · HIPAA · PCI DSS
Day 1 Audit Readiness
Multi-Regulation Baseline & Test of Design (ToD)
Tech-stack tailored control cross-mapping that verifies control design integrity in 30 seconds across SOC 2 Type II, EU GDPR, HIPAA Security Rule, and PCI DSS v4.0, packaging signed, immutable workpapers ready for export to day-to-day CCM tools.
RAMP ArchitectureScientific Model →