EU AI Act β’ NIST AI RMF β’ ISO 42001
Expert-led assessments available now
β οΈ EU AI Act deadline: Only 8 months away (August 2, 2026)
β No obligation β Expert guidance β Get your readiness score
Professional compliance assessments available now β’ Automated platform coming Q2 2026
Expert-led compliance readiness assessments available today
Investment: $50K-$120K per assessment
Timeline: Start as soon as next week
EU AI Act deadline: 8 months away (Aug 2026)
SaaS platform for continuous compliance monitoring (in development)
Pricing: $25K-$100K/year (projected)
Availability: Q2-Q3 2026
Be first to access when we launch
Our Approach: Professional services revenue funds platform development. Early assessment customers get lifetime platform access when we launch.
Make AI compliance accessible to every organization, enabling safe and responsible AI deployment at scale.
Bridge AI ethics to practical compliance. Today through expert assessments, tomorrow through automated platform technology.
Book a free 15-minute consultation to discuss your AI systems and compliance needs.
β 15 minutes β No obligation β Expert guidance β Get your readiness score
Enterprises face a perfect storm of regulatory pressure
3-6 months per audit, 1000+ hours of manual work
August 2026 high-risk AI deadline, up to β¬35M fines for non-compliance
Enterprises run 100+ AI models with no visibility
Automated AI compliance in days, not months
Weeks instead of months - automated scanning and reporting
Reduce audit costs from $500K to $150K annually
Real-time compliance monitoring and instant audit trails
Integrate with MLflow, SageMaker, Azure ML, Databricks - 5 minutes setup
Auto-discover all AI models across your infrastructure
Run 47+ compliance checks against EU AI Act, NIST, ISO standards
Generate audit-ready reports in PDF, Excel, JSON formats
Get actionable recommendations to fix compliance gaps
Continuous monitoring with alerts for compliance drift
Master AI audit trails, bias detection, and regulatory compliance
Comprehensive guide to AI audit trails in 2025. Learn what to log, why it matters, and how to implement compliant decision tracking.
Practical guide to detecting AI bias. Learn the 5 types of bias, key fairness metrics, and step-by-step testing methodology.
What US companies need to know. Timeline, requirements, penalties, and 12-month roadmap for high-risk AI systems.
Why plain-English explanations reduce complaints by 80% and increase re-applications by 30%. Turn compliance into revenue.
How automated bias remediation works. Detect and fix bias in 4 hours instead of 6-8 weeks, preventing 99% of biased decisions.
Comprehensive AI compliance validation across all major frameworks
22 Checks
Risk classification, documentation, human oversight, transparency requirements
15 Checks
Governance, mapping, measuring, managing AI risks
10 Checks
AI management system requirements, lifecycle controls
8 checks
Model Card Completeness
Validates presence of model purpose, architecture, performance metrics
Training Data Documentation
Verifies data sources, collection methods, preprocessing steps
Version Control & Lineage
Tracks model versions, dependencies, reproducibility
Intended Use & Limitations
Documents use cases, edge cases, known limitations
Performance Metrics Disclosure
Accuracy, precision, recall across demographics
Explainability Requirements
SHAP/LIME values, feature importance, decision rationale
Model Owner & Accountability
Responsible parties, contact information, escalation paths
Update & Maintenance Logs
Change history, retraining schedules, deprecation plans
10 checks
Demographic Parity
Equal positive prediction rates across protected groups
Equal Opportunity
Equal true positive rates across demographics
Disparate Impact Analysis
4/5ths rule validation for adverse impact
Equalized Odds
Balanced error rates across protected classes
Calibration Fairness
Predicted probabilities match outcomes by group
Proxy Feature Detection
Identifies features correlated with protected attributes
Training Data Bias
Representation balance, sampling bias, label bias
Intersectional Fairness
Multi-dimensional protected class analysis
Fairness Metrics Reporting
Statistical parity, predictive equality documentation
Bias Mitigation Evidence
Pre-processing, in-processing, post-processing techniques
7 checks
EU AI Act Risk Level
Unacceptable, High, Limited, Minimal risk classification
High-Risk Use Case Detection
Credit scoring, hiring, critical infrastructure, law enforcement
Impact Assessment
Likelihood Γ severity analysis for identified risks
Fundamental Rights Impact
Privacy, non-discrimination, freedom of expression
Risk Mitigation Controls
Human oversight, monitoring, safeguards documentation
Prohibited Practices Check
Social scoring, subliminal manipulation, real-time biometrics
Residual Risk Assessment
Post-mitigation risk evaluation and acceptance criteria
8 checks
Data Quality Metrics
Completeness, accuracy, consistency, timeliness
Data Provenance
Source tracking, collection date, update frequency
Privacy Compliance
GDPR Article 22, data minimization, purpose limitation
Consent & Legal Basis
Lawful processing, explicit consent for sensitive data
Data Retention Policies
Storage limits, deletion procedures, archival rules
Data Security Controls
Encryption, access controls, audit logging
Third-Party Data Agreements
Vendor contracts, data processing agreements
Data Drift Monitoring
Distribution shifts, concept drift detection
6 checks
Human-in-the-Loop
Required for high-risk decisions (hiring, credit, healthcare)
Override Mechanisms
Ability to override automated decisions with justification
Appeal Process
Right to contest automated decisions, recourse mechanisms
User Notification
Disclosure when interacting with AI systems
Competent Personnel
Training, qualifications, authority of human reviewers
Emergency Stop Capability
Kill switch for critical systems, incident response
8 checks
Model Accuracy Thresholds
Minimum acceptable performance by use case
Performance Degradation Alerts
Automatic detection when metrics drop below threshold
Continuous Monitoring
Real-time tracking of prediction quality, latency, errors
Adversarial Robustness
Resistance to adversarial attacks, input manipulation
Edge Case Handling
Out-of-distribution detection, uncertainty quantification
Retraining Triggers
Automated retraining schedules based on drift/performance
Audit Trail Completeness
Logs of all predictions, decisions, interventions
Post-Market Monitoring
Production performance tracking, incident reporting
47+ automated checks across 6 critical categories ensure your AI systems meet global regulatory standards
Banks, insurance, fintech - high-risk AI models requiring strict compliance
Hospitals, pharma, medtech - patient safety and HIPAA compliance
Telecom operators - customer-facing AI at scale
Public sector - transparency and accountability requirements
Industrial AI - safety-critical systems
E-commerce, brick-and-mortar - customer experience AI
The world's first comprehensive AI regulation, with high-risk AI systems facing an August 2026 compliance deadline
Biden's AI Executive Order creates new compliance requirements for federal contractors
Enterprise AI deployment growing 200% year-over-year
Traditional compliance tools weren't built for AI
See why leading enterprises choose TrustRAIL over alternatives
| Feature |
π‘οΈ
TrustRAIL
|
π’
Traditional GRC Platforms
|
π€
ML Platforms (MLflow, SageMaker)
|
π₯
Consulting Firms
|
|---|---|---|---|---|
| AI-Specific Compliance Checks | β | β | Partial | Manual |
| Automated Model Discovery & Scanning | β | β | β | β |
| EU AI Act Compliance (Aug 2026) | β | β | β | Manual |
| Continuous Real-Time Monitoring | β | β | Limited | β |
| Automated Remediation Recommendations | β | β | β | Manual |
| Multi-Platform Integration |
β
MLflow, SageMaker, Azure ML, Databricks |
Generic |
β
Single platform |
Custom |
| Time to Deploy | 5 minutes | 2-3 months | N/A | 3-6 months |
| Annual Cost (typical enterprise) | $150K | $200K-$400K |
$50K-$100K
(no compliance) |
$500K+
per audit |
| Audit-Ready Reports |
β
PDF, Excel, JSON |
Generic | β |
β
Expensive |
| Scalability (100+ models) | β | Possible | Limited | Prohibitive Cost |
Deploy in 5 minutes vs. 3-6 months. Get audit-ready reports in days, not months.
$150K/year vs. $500K+ for consultants. Fixed cost, unlimited scans.
Built specifically for AI compliance. EU AI Act ready. 47+ AI-specific checks.
See how much you'll save with TrustRAIL
$50K
per year
$150K
per year
Custom
contact us
Enterprise-grade APIs for seamless integration
Simple, standards-based REST API with JSON responses. Get started in minutes with our comprehensive docs.
Native SDKs for popular programming languages. Install via npm, pip, or Maven.
Pre-built connectors for popular ML platforms. Auto-discover and scan models.
Get started in under 5 minutes
from trustrail import TrustRAIL
# Initialize client
client = TrustRAIL(api_key="your_api_key")
# Log a decision
decision = client.log_decision(
model_id="credit_model_v1",
decision="APPROVED",
confidence=0.87,
input_features={
"credit_score": 720,
"income": 85000,
"debt_ratio": 0.32
},
user_id="user_12345"
)
print(f"Decision logged: {decision.id}")const TrustRAIL = require('@trustrail/sdk');
// Initialize client
const client = new TrustRail({
apiKey: 'your_api_key'
});
// Log a decision
const decision = await client.logDecision({
modelId: 'credit_model_v1',
decision: 'APPROVED',
confidence: 0.87,
inputFeatures: {
creditScore: 720,
income: 85000,
debtRatio: 0.32
},
userId: 'user_12345'
});
console.log('Decision logged:', decision.id);/v1/decisions
Log an AI decision - Records decision with full audit trail
Required: model_id, decision, confidence, input_features, user_id
/v1/compliance/check
Run compliance checks - Validates model against 47+ checks
Required: model_id, framework (EU_AI_ACT, NIST_RMF, ISO_42001)
/v1/audit-trail/{decision_id}
Retrieve audit trail - Get complete decision record
Returns: decision details, input data, model version, compliance status
/v1/bias/detect
Detect bias - Analyze decisions for demographic parity, equal opportunity
Required: model_id, protected_attribute (gender, race, age)
/v1/explain/{decision_id}
Generate explanation - Plain-English consumer explanation
Returns: decision factors, "what if" scenarios, actionable next steps
Connect your ML platform and TrustRAIL automatically discovers all models, scans for compliance issues, and monitors ongoing.
Get notified instantly when compliance issues are detected or when bias thresholds are exceeded.
Start with our free sandbox environment. No credit card required.
Need help? Email [email protected] or schedule a technical consultation
Everything you need to know about TrustRAIL
TrustRail can be deployed in as little as 5 minutes. Simply connect your ML platforms (MLflow, SageMaker, Azure ML, Databricks) via API keys, and TrustRail will automatically discover and scan your AI models. Most customers are fully operational within the same day.
Yes! TrustRail is specifically built to help organizations comply with the EU AI Act. High-risk AI systems must comply by August 2026. Our platform includes 47+ compliance checks covering EU AI Act requirements, NIST AI Risk Management Framework, and ISO/IEC 42001 standards.
TrustRail integrates with all major ML platforms including MLflow, AWS SageMaker, Azure ML, and Databricks. We support both cloud and on-premise deployments. Enterprise customers can also request custom integrations for proprietary platforms.
Traditional GRC (Governance, Risk, Compliance) platforms are built for general IT compliance and lack AI-specific checks. TrustRail is purpose-built for AI compliance with automated model discovery, bias detection, fairness assessments, and explainability analysisβfeatures that generic GRC tools don't offer.
TrustRAIL takes security seriously. We are SOC 2 Type II compliant, support SSO/SAML, and offer role-based access control (RBAC). For enterprise customers, we provide on-premise deployment options to keep all data within your infrastructure. We never access your training dataβonly model metadata and performance metrics.
Absolutely! TrustRail is built for enterprise scale. Our Growth plan supports up to 200 models, and our Enterprise plan supports unlimited models with continuous real-time monitoring. Fortune 500 companies use TrustRail to manage 500+ AI models across multiple business units.
Yes! We offer personalized demos where you can see TrustRAIL in action with your own use cases. We also provide a 30-day pilot program for qualified enterprises to test TrustRail with up to 20 models at no cost. Contact our sales team to get started.
Our Starter plan includes email support with 24-hour response time. Growth plan customers get priority 24/7 support via email, chat, and phone. Enterprise customers receive a dedicated Customer Success Manager, white-glove onboarding, and SLA guarantees with 99.9% uptime.
Traditional consulting firms charge $500K-$1M+ per audit and take 3-6 months to complete. TrustRail starts at $50K/year for up to 50 models with unlimited scans. Most customers save 70%+ on compliance costs while reducing audit time by 80%. Use our ROI Calculator to see your specific savings.
TrustRAIL not only identifies compliance gaps but also provides automated remediation recommendations. For each issue found, you'll receive specific, actionable steps to fix itβincluding code snippets, configuration changes, and documentation templates. Our platform helps you go from "problem identified" to "problem fixed" in days, not months.
Our team is here to help. Schedule a personalized demo or contact us directly.
An AI audit trail is a comprehensive, tamper-proof record of every decision, data point, and model change in your AI system. As AI regulations tighten globally (EU AI Act, US Executive Order 14110, state-level laws), organizations using AI for high-stakes decisions need audit trails to prove compliance, explain decisions to regulators and customers, and detect bias before it causes harm.
Key Takeaway: If your AI system makes decisions that affect people's livesβloans, jobs, healthcare, housingβyou need an audit trail. Not having one in 2025 is a regulatory and legal liability.
What decision was made, when, which model version, input data used, and output generated.
Where data came from, how it was transformed, data quality checks, and data freshness.
Training data, parameters, performance metrics, deployment details, and A/B testing results.
Inventory AI systems, map regulatory requirements, design log schema
Set up infrastructure, instrument code, validate log quality
Add explanations, configure bias checks, track data lineage
Build dashboards, create APIs, roll out to all models
Try our platform free for 14 days. No credit card required.
For the complete article with code examples and detailed implementation guidance, download the full PDF guide.
AI bias isn't just unethicalβit's expensive. The average discrimination lawsuit costs $50K-$500K, class actions reach $10M-$100M+, and regulatory fines can hit 7% of global revenue under the EU AI Act.
Training data doesn't represent real-world population. Medical AI trained on 85% white patients performs poorly on Black patients.
Past discrimination baked into training data. Hiring AI learns from company that historically hired 90% men for leadership.
Different data quality for different groups. Missing income data forces model to rely on zip code (proxy for race).
AI penalized resumes with "women's" terms. Trained on 10 years of male-dominated hiring data.
Result: Project discontinued after bias discovery
See automated bias detection and remediation in action
The EU AI Act (in effect since August 2024) is the world's first comprehensive AI regulation. If your AI system is used by EU citizensβeven if your company is US-basedβyou must comply. High-risk AI systems face an August 2026 compliance deadline. Fines reach β¬35M or 7% of global revenue.
You're in scope if:
Continuous process to identify and mitigate risks
High-quality, representative, unbiased training data
Tamper-proof logs retained 6 months to 10 years
Users must know they're interacting with AI
β¬35M or 7% of global revenue (whichever is higher) for non-compliant high-risk AI
Schedule a free assessment of your EU AI Act readiness
When AI rejects someone's loan application, how you explain the decision determines whether they complain, sue, or quietly improve and re-apply. Industry data shows plain-English explanations reduce complaints by 80% and increase re-applications by 30%.
80%
Saves $173K/year (1,000 rejections/month)
+30%
Adds $420K/year in new revenue
Total Benefit: $593K/year
vs. $3,600-$9,600/year platform cost = 60-165x ROI
"Credit Score: 650 (needed 680+)" not "credit_score_standardized: 0.342"
"Debt-to-income ratio: 48% (needed under 40%)" with exact calculation
"Pay down $400/month in debt to reach 40%" instead of "improve your finances"
Interactive simulator showing: "If you increased income to $6,000/month β APPROVED β"
See how much you could save with Consumer Explainer
Traditional bias remediation takes 6-8 weeks: detect β analyze β retrain β validate β deploy. By then, you've made thousands of biased decisions. Auto Bias Fix detects and remediates bias in 4 hours, automatically adjusting decision thresholds to achieve fairness.
6-8 weeks
24,000-32,000 biased decisions made
$21,900 per incident (engineer time)
4 hours
Only 170 biased decisions made
$799/month (unlimited incidents)
99% Fewer Biased Decisions
Every decision logged with protected attributes (stored separately)
Daily automated checks for demographic parity, equal opportunity, disparate impact
Automated checks for feature drift, proxy features, threshold issues
Threshold adjustment, feature reweighting, or retraining with fairness constraints
A/B test on 10% traffic, verify fairness improved without accuracy drop
Automatic rollout to 100% traffic, audit log updated, compliance team notified
Watch how automated bias remediation works in real-time
15-minute assessment call β’ Get your compliance readiness score
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