ML Governance Platform
Enterprise Machine Learning Governance Aligned with NIST AI RMF & EU AI Act
Enterprise Machine Learning Governance Aligned with NIST AI RMF & EU AI Act
GuardAxion's ML Governance Platform provides comprehensive governance capabilities for machine learning and AI systems, ensuring alignment with the NIST AI Risk Management Framework (RMF) and EU AI Act requirements. Our platform addresses the full AI lifecycle from development through deployment and monitoring, enabling organizations to implement responsible AI practices, manage risks, and demonstrate regulatory compliance.
As AI regulations mature and governance expectations increase, organizations need structured approaches to ML governance. Our platform transforms compliance and risk management from reactive checklists into proactive, integrated processes that enhance both safety and business value.
Establish AI governance structures, policies, and accountability with role-based responsibilities, oversight mechanisms, and stakeholder engagement frameworks.
Identify and document AI system context, stakeholders, impacts, and dependencies with comprehensive mapping of business processes and regulatory requirements.
Assess AI system performance, trustworthiness characteristics, and risk metrics with automated measurement, benchmarking, and continuous evaluation.
Implement risk treatment strategies, resource allocation, and continuous improvement processes with prioritization frameworks and decision support tools.
Comprehensive risk assessment methodology covering fairness, bias, security, privacy, transparency, and accountability with quantitative risk scoring.
Evaluate and monitor seven NIST trustworthy AI characteristics: valid/reliable, safe, secure, resilient, accountable, transparent, and explainable.
Automated AI system classification into unacceptable risk, high-risk, limited risk, and minimal risk categories based on EU AI Act definitions.
Identify and prevent prohibited AI practices including social scoring, subliminal manipulation, and exploitation of vulnerabilities.
Comprehensive controls for high-risk AI systems including risk management, data governance, documentation, transparency, human oversight, and accuracy requirements.
Support EU AI Act conformity assessment procedures with evidence collection, documentation management, and third-party assessment coordination.
Ensure AI system transparency including user notification, clear labeling, and disclosure of AI-generated content as required by the EU AI Act.
Continuous monitoring of deployed AI systems with incident reporting, corrective action management, and market surveillance compliance.
Centralized registry of all ML models with version control, metadata management, lineage tracking, and approval workflows.
Structured impact assessment process covering ethical, social, legal, and business implications with stakeholder consultation and documentation.
Automated bias detection across protected attributes with fairness metrics, disparity analysis, and mitigation recommendations.
Model explainability tools including feature importance, SHAP values, LIME, and counterfactual explanations for transparency requirements.
Training data lineage, quality assessment, bias detection, and documentation to ensure responsible data usage in ML systems.
Implement human-in-the-loop controls, decision review workflows, and override mechanisms as required for high-risk AI systems.
Achieve and maintain compliance with NIST AI RMF, EU AI Act, and emerging AI regulations through comprehensive governance capabilities.
Identify and mitigate AI risks including bias, safety hazards, security vulnerabilities, and privacy concerns before deployment.
Build trust with customers, regulators, and stakeholders through transparent, accountable, and ethical AI practices.
Streamline ML operations with standardized processes, automated workflows, and comprehensive visibility across the AI lifecycle.
Establish enterprise-wide AI governance with policies, standards, and oversight mechanisms for all AI initiatives.
Manage high-risk AI systems in employment, law enforcement, critical infrastructure, or education with EU AI Act compliance.
Ensure ML model fairness across demographic groups with automated bias testing and mitigation strategies.
Manage complete ML model lifecycle from development through retirement with version control and approval gates.
Evaluate third-party AI systems and vendors for risk, compliance, and trustworthiness before procurement or integration.
Implement organization-wide responsible AI programs with ethics reviews, impact assessments, and stakeholder engagement.
Centralized platform for implementing and monitoring AI governance frameworks with automated compliance checking and reporting.
Integrated tools for bias testing, explainability, and documentation without disrupting ML development workflows.
Comprehensive risk assessment, regulatory mapping, and audit-ready documentation for AI governance compliance.
Strategic visibility into AI governance posture, risk exposure, and regulatory compliance across all AI initiatives.
Align with NIST AI RMF and EU AI Act requirements while building trustworthy, responsible AI systems.
Request a Demo