AI Governance
Govern AI decisions with the rigor they demand
Register, monitor, and govern every AI model in your enterprise. Detect bias and drift, enforce human-in-the-loop policies, and maintain the audit trails regulators require — from EU AI Act to NYC Local Law 144.
Challenges We Solve
Governance gaps that structured controls and audit trails eliminate.
Shadow AI Sprawl
AI models deployed without oversight create compliance and reputational risk. Register, classify, and monitor every model from one governed platform.
Model Bias & Drift
AI decisions quietly degrade over time as data distributions shift. Detect drift, bias, and performance decay before they cause real-world harm.
Missing Accountability
When AI makes a bad decision, who's responsible? Map every model output to a human owner with clear escalation paths and override protocols.
Regulatory Exposure
EU AI Act, NYC LL144, SOX — regulations are multiplying. Maintain compliance documentation, risk assessments, and audit trails automatically.
Use Cases
Real governance scenarios powered by DecisionLedger.
Registers all 47 AI models in the agent registry with risk classifications, then uses shadow mode to validate 3 new models against live data before production deployment.
Zero unregistered AI models in production — full inventory with risk tiers
Generates EU AI Act compliance evidence packages from actual governance activity — bias audits, explainability reports, and override logs — without manual documentation.
Compliance evidence generated automatically from platform activity
Deploys the kill switch after detecting anomalous model outputs, instantly disabling the affected AI agent across all tenants while the team investigates root cause.
Model incident contained in under 60 seconds with full audit trail
Measurable Impact
Based on platform benchmarks across early adopters.
Built-in AI Governance Controls
Not just models about AI risk — actual enforcement tools that register, monitor, and control every AI agent and model in your organization.
Agent Registry
Register every AI agent with per-agent permissions, activity monitoring, and instant suspension. Know exactly what AI is doing across your organization.
Kill Switch
Circuit breaker for any AI model or agent. One-click disable across your entire tenant with automatic cool-down re-enable when you're ready.
Shadow Mode
Test new models in production without affecting live decisions or audit logs. Validate AI outputs side-by-side before going live.
Bias Auditing
Statistical bias detection across protected classes with a dedicated bias dashboard. Surface disparate impact before it becomes a compliance finding.
SHAP Explainability
Every prediction comes with SHAP waterfall plots showing feature importance and input-to-output transparency. No more black-box AI decisions.
Policy Guardrails
Define enforceable constraints with JSONLogic rules. Auto-flag, block, or escalate violations with full override tracking and rationale logging.
Drift Detection
Monitor policy and guardrail effectiveness over time with automated alerts when controls go stale. Detect when regulatory or policy changes invalidate existing controls.
Compliance Badges
Pre-mapped controls for EU AI Act, NYC Local Law 144, SOX 302/404, and DOL Fiduciary Rule. Generate compliance evidence automatically from your governance activity.
Connects With
Featured Models
Pre-built decision models ready to run with your data.
Control Effectiveness Drift
Detects weakening controls before audits fail using time-series anomaly detection with statistical process control (SPC), linear regression trend analysis, moving average deviation, and Western Electric rules. Provides drift classification, time-to-failure prediction, environment health scoring, and prioritized remediation recommendations.
Decision Audit Replay
Reconstructs how a decision was made at a point in time.
Decision Drift Detection
Identifies when decisions diverge from original intent over time.
Decision Traceability
Decision Traceability Model - Links inputs, assumptions, approvals, overrides, and outcomes into a directed traceability graph. Scores completeness of each link, detects broken chains, identifies orphaned decisions, flags stale assumptions, and computes override-to-decision ratios for governance and audit compliance.
Ethical Reputational Risk
Ethical & Reputational Risk Model - Assesses brand and ethical exposure tied to decisions. Scores decisions across ethical dimensions, stakeholder impact, and reputational exposure to surface red-line flags, brand value at risk, and mitigation recommendations for governance boards.
Executive Override Risk
Flags governance risk when leaders override data-backed decisions. Scores each override for governance risk based on justification quality, divergence from data confidence, financial magnitude, and overrider level. Detects serial overriders, domain clustering, and escalating patterns.
Human Override Risk Model
Assesses the likelihood and potential impact of manual overrides that could undermine model intent or governance controls.
Policy Compliance Guardrails Model
Ensures HR decisions align with organizational policies and legal requirements for selected US states. Evaluates termination, hiring, compensation, leave, accommodation, and discipline decisions against federal and state employment laws.
How It Works
Three steps to structured, auditable decisions.
Register & Classify
Catalog every AI model by risk tier, data sensitivity, and regulatory scope. Auto-classify using the EU AI Act framework and built-in compliance controls.
Monitor & Govern
Track model performance, detect drift and bias, enforce human-in-the-loop policies, and log every override with rationale.
Audit & Report
Generate compliance reports, replay decision trails, and demonstrate accountability to regulators, boards, and stakeholders.
How many AI models are running in your organization right now without a risk classification, a bias audit, or a kill switch?
Spreadsheet AI inventories
Static model lists with no live monitoring, drift detection, or enforcement
Manual compliance documentation
Weeks of evidence gathering for every regulatory inquiry
MLOps tools without governance
Model deployment pipelines that track versions but not bias, risk, or approvals
GRC add-on modules
Generic risk tools that don't understand AI-specific risks like drift and explainability