Trust, Quantified
Real-time AI trust scoring across 5 pillars. A single, composable metric that tells you exactly how secure, compliant, and resilient your AI infrastructure is.
The 5 Pillars of AI Trust
Each pillar is independently scored and weighted to produce a composite trust metric that reflects your organization's true AI security posture.
Evaluates your AI infrastructure security posture including endpoint protection, encryption standards, access controls, and vulnerability exposure across all connected AI systems.
Measures adherence to regulatory frameworks like EU AI Act, NIST AI RMF, SOC 2, and ISO 42001. Tracks control implementation, evidence collection, and audit readiness.
Quantifies organizational AI risk through Monte Carlo simulations, FAIR analysis, threat modeling, and continuous attack surface assessment with VaR/CVaR metrics.
Assesses your ability to withstand and recover from AI failures through RTO/RPO measurement, chaos engineering scenarios, and blast radius analysis.
Evaluates AI agent management maturity including guardrail enforcement, model oversight, prompt injection defenses, MCP firewall coverage, and shadow AI detection.
Composite Score
All 5 pillars are fetched concurrently via asyncio.gather() and combined using weighted aggregation to produce a single trust metric in under 200ms.
Grading System
Instantly understand your AI trust posture with letter-grade clarity.
How It Works
From integration to actionable score in four steps.
Connect
Integrate your AI providers, infrastructure, and governance tools through our universal adapter layer. Supports 33+ AI providers and 38 enterprise integrations.
Discover
Automatically inventory all AI assets, agents, models, and data flows across your organization. Detect shadow AI and unsanctioned deployments.
Assess
Continuously evaluate security, compliance, risk, resilience, and governance posture using real-time data from 6 ACL sources across 12 platform modules.
Score
Generate a weighted composite trust score with per-pillar breakdowns, historical trends, and actionable remediation recommendations.
Real-Time Recalculation
Trust scores aren't static snapshots. Every security event, compliance change, or risk update triggers an automatic recalculation through our event-driven pipeline.
30-Second Debounce
Batches rapid-fire events to prevent score thrashing while ensuring timely updates. A security incident at 10:00:01 and a compliance change at 10:00:15 are processed together at 10:00:31.
CloudEvents v1.0
All 137 domain events across 12 modules flow through Redis Streams using CloudEvents v1.0 envelope format with consumer groups and XAUTOCLAIM for fault tolerance.
Score Decay
Trust scores naturally decay over time when assessments become stale. Four decay models ensure scores reflect current reality, not historical snapshots.
Score Decay Models
Linear Decay
Score decreases at a constant rate over time when assessments become stale.
S(t) = S0 - k * tExponential Decay
Rapid initial decrease that slows over time, modeling trust erosion in dynamic environments.
S(t) = S0 * e^(-lambda * t)Step Decay
Score drops at defined intervals, reflecting policy-driven reassessment windows.
S(t) = S0 - floor(t/p) * dSigmoid Decay
Slow initial decline with an accelerating drop, then leveling off at a floor value.
S(t) = floor + (S0 - floor) / (1 + e^(k*(t-t0)))Know Your AI Trust Score
Stop guessing about your AI security posture. Get a quantified, real-time trust score backed by data from every layer of your AI infrastructure.