Operationalizing AI Assurance: Turning Evaluation into Evidence
AI metrics alone don’t satisfy regulators. Learn how to operationalize AI assurance by converting evaluations into audit-ready evidence aligned with EU AI Act, NIST RMF, and ISO 42001.
Why HITRUST AI Risk Management Needs Metrics, Not Policies
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.
Why Responsible AI Needs TRACE — Operational Evidence, Not Just Policies
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.
Doctor, This AI Model Is Safe—Here’s the Proof
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.
Metrics Aren't Compliance: How TRACE Adds Context for Auditable AI
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.
TRACE + Deepeval: Making Open-Source Metrics Audit-Ready
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.
Bridging the Metrics-to-Evidence Gap
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.
Turning AI Metrics into Audit‑Ready Evidence with the Responsible AI TRACE Framework
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.
Auto Discovery of AI Assets with Amazon SageMaker: One Click to AI Governance
Gain instant visibility into your AI landscape with one-click auto-discovery from Amazon SageMaker.
The Role of MLOps in AI Governance and Compliance
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 Model Lifecycle Management: From Development to Decommissioning
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.