Trust, Risk, Action, Compliance, and Evidence
HITRUST’s AI Risk Management Assessment shifts healthcare compliance from policy checklists to continuous model monitoring. Learn why metrics like bias, drift, and explainability now matter—and how TRACE helps map these metrics directly to HITRUST controls for real-time, audit-ready evidence.
TRACE is an open assurance framework that turns Responsible AI from intent into evidence. It links model metrics to legal clauses, automates controls, and delivers audit-ready proof—without black-box platforms.
Radiology AI tools are powerful—but are they provably safe? This post explores how TRACE transforms performance metrics into HIPAA-compliant audit logs and factsheets for patients and clinicians alike.
AI metrics are necessary—but not sufficient—for compliance. Learn how TRACE adds purpose, risk, and impact metadata to generate audit-ready evidence that meets EU AI Act and ISO 42001 expectations.
Learn how pairing Deepeval with the TRACE framework turns raw fairness, privacy, and robustness metrics into audit-ready evidence that satisfies EU AI Act, NIST RMF, and ISO 42001 requirements.
AI teams track metrics. Regulators want evidence. TRACE transforms fairness scores, privacy metrics, and model evaluations into audit-ready proof—automatically. Learn how it bridges the Metrics-to-Evidence Gap and helps you comply with EU AI Act, NIST AI RMF, and ISO 42001.
AI governance demands more than metrics—it needs evidence. Learn how CognitiveView’s TRACE Framework bridges the gap between evaluation and audit-ready compliance aligned with EU AI Act, NIST, and ISO 42001.
Gain instant visibility into your AI landscape with one-click auto-discovery from Amazon SageMaker.
MLOps is essential for AI governance, ensuring compliance, security, and fairness in AI systems. Learn how enterprises can leverage MLOps to streamline AI model management, mitigate risks, and align with regulations like NIST AI RMF and the EU AI Act.
AI models require structured governance throughout their lifecycle to ensure compliance, fairness, and security. Learn best practices for AI model development, deployment, monitoring, and decommissioning while aligning with global AI regulations like NIST AI RMF and the EU AI Act.
