KoraSafe

Fleet-wide governance
Command center

Assess every AI system across seven governance dimensions. Track maturity, assign accountability, and generate audit-ready evidence automatically.

Governance dimensions

Every AI system is assessed across these dimensions - tracked as In Place, Working On It, or Not Yet - giving leadership an at-a-glance view of governance readiness.

Human Oversight

Review, interpret, override

Mechanisms for human review and intervention in AI decisions. Global AI regulations including the EU AI Act require human oversight for high-risk systems - operators must be able to monitor, interpret, and override.

  • Documented escalation procedures with trigger thresholds
  • Override and reversal logs with timestamps
  • Training records for all human operators
  • Real-time monitoring dashboard with intervention controls
HUMAN OVERSIGHT DASHBOARD Review Rate Coverage Overrides AI System review override Active Alert Block
Decision Logging

Every decision, fully traceable

Every AI decision must be traceable - inputs, model version, confidence, alternatives, and output. Regulatory frameworks require automatic logging for high-risk systems with immutable audit logs.

  • Input capture and model version for every event
  • Tamper-evident append-only storage
  • Retention aligned with regulatory periods
  • End-to-end decision reconstruction
Decision #4821 input: loan_app_293 | model: v2.4.1 | conf: 0.94 | output: APPROVED 0.94 Decision #4822 input: loan_app_294 | model: v2.4.1 | conf: 0.67 | output: REVIEW 0.67 Decision #4823 input: loan_app_295 | model: v2.4.1 | conf: 0.31 | output: DENIED 0.31 Decision #4824 input: loan_app_296 | model: v2.4.1 | conf: 0.88 | output: APPROVED 0.88 SHA-256 chain a3f8...2c1d
Data Governance & Bias Testing

Fairness by design

Fairness testing across demographic groups. Includes disparate impact analysis, the 4/5 rule, and ongoing production monitoring for bias drift.

  • Data provenance and lineage documentation
  • Statistical bias testing across protected groups
  • Disparate impact analysis (4/5 rule)
  • Production bias drift monitoring and alerting
Raw Data Sources Quality Completeness ! Bias 4/5 Rule Test Clean Dataset DISPARATE IMPACT ANALYSIS Group A 92% Group B 87% Group C 75% Group D 68% 80% threshold PASS PASS WARN FAIL
Risk Management

Structured risk lifecycle

Structured identification, assessment, and mitigation of AI risks. Regulations require a risk management system throughout the lifecycle covering accuracy, security, bias, safety, and fundamental rights.

  • Risk register with likelihood and severity scoring
  • Documented mitigation controls with metrics
  • Residual risk acceptance with executive sign-off
  • Periodic reassessment schedule
RISK MATRIX LIKELIHOOD → SEVERITY → R1 R2 R3 R4 R5 Critical High Medium Low Negl.
Technical Documentation

Audit-Ready specifications

System specifications as required by regulatory frameworks like the EU AI Act. Model architecture, training data, performance metrics, and risk assessments - versioned and maintained throughout the lifecycle.

  • System description with purpose and boundaries
  • Model architecture and design rationale
  • Performance benchmarks and test results
  • Version-controlled documentation with change history
Risk Assessment Report v3.1 | Updated 2026-03-15 Training Data Governance v2.8 | Updated 2026-03-20 Model Architecture Spec v4.2 | Updated 2026-04-01 ANNEX IV COVERAGE System Description Model Architecture Training Data Performance Metrics
User Transparency

Clear AI disclosure

Clear disclosure when users interact with AI. Regulatory transparency requirements apply for chatbots, deepfakes, and emotion recognition. Users must know they are interacting with AI and have a path to contest decisions.

  • AI disclosure notifications at point of interaction
  • Plain-language decision explanations
  • Contest and complaint procedures with SLAs
  • Accessible documentation (WCAG compliant)
AI This response was generated by AI Model: GPT-4 | Confidence: 0.91 | Learn more Your loan application has been reviewed... DECISION EXPLANATION Key factors: income stability (high), credit history (medium), employment tenure (high) Contest this decision Request Review Feedback
Accuracy & Monitoring

Continuous performance tracking

Continuous performance tracking of deployed systems. Regulatory standards require accuracy, robustness, and cybersecurity for high-risk AI. Detect drift, alert on anomalies, and trigger automated rollbacks.

  • Performance baselines at deployment
  • Drift detection with configurable thresholds
  • Anomaly alerting with escalation paths
  • Automated rollback when metrics breach
MONITORING DASHBOARD 94.2% Accuracy 0.91 F1 Score 142ms P95 Latency 0.3% Error Rate ACCURACY OVER TIME baseline 95% alert 90% drift detected alert! Jan Feb Mar Apr May Jun Performance drift detected - rollback recommended

Governance maturity scoring

Track your organization's governance posture across key pillars. The radar chart visualizes all pillars at once, highlighting where to focus.

Initial - Ad-hoc, reactive
Developing - Some processes, inconsistent
Defined - Standardized, minimum viable
Managed - Metrics-driven, evidence-based
Optimized - Continuous, automated
AI Strategy Risk Mgmt Data Gov Model Lifecycle Ethics AI Ops
Compliance Tracking

Know exactly where you stand - across every governance pillar

The built-in compliance checklist covers accountability, policies, risk management, data governance, development standards, and deployment monitoring. Every item is tracked as complete, in-progress, or not started - so you always know what's done and what's left.

Progress is visualized per pillar and overall, making board-ready reporting a one-click export instead of a week-long spreadsheet exercise.

Accountability Framework

Clear ownership prevents governance gaps

Every governance activity - risk assessment, bias testing, deployment approval, incident response, monitoring, regulatory reporting, and user transparency - has explicit role assignments: who does the work, who owns the outcome, who provides input, and who stays informed.

Fully customizable per organization. No more "I thought someone else was handling that."