Trust, Risk, Action, Compliance, and Evidence
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.
