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.
Compliance Is No Longer Enough: How HAIGS Builds Trust in Healthcare AI
Compliance alone won’t earn patient trust in healthcare AI. Passing audits is not enough—outcomes, fairness, and transparency matter most. This blog with IAIGH CEO Josh Baker explores how the HAIGS framework helps providers move from box-ticking compliance to demonstrable trust.
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.
3‑Step Roadmap for Your Responsible AI Journey
Discover a clear, standards‑aligned path to Responsible AI. Learn how CognitiveView’s 3‑step Governance, Risk, and Compliance (GRC) journey helps you assess, monitor, and comply with frameworks such as NIST, the EU AI Act, and ISO 42001—while growing at your own pace.
Shipping Safe LLMs
Use Deepeval to build a continuous quality gate for LLMs that blocks hallucinations, bias, and drift. This guide shows how to integrate it with GitHub Actions and your risk framework—aligning AI deployments with NIST and ISO standards using open-source tools.
From Theory to Practice: From AI Fear to AI Fluency—Building the Future Hybrid Workforce
AI isn’t replacing your workforce—it’s becoming part of it. In this recap of my conversation with Dr. Ranjan Data, we break down how to move from AI fear to fluency with practical steps for reskilling, governance, and designing a truly hybrid workforce.
Startups, Want Faster Sales? Launch a TrustCenter to Close the Trust Gap
In regulated industries, AI governance isn’t optional—it’s foundational. Discover how launching a TrustCenter can accelerate your sales by proactively addressing buyer concerns around risk, compliance, and governance.
Go From Zero to AI Trust in Under 2 Hours
AI startups struggle when missing AI documentation stalls deals and slows procurement. In this guide, learn how to self-assess, leverage our AI Policy Assistant, and publish a shareable Trust Center—in under two hours—so you can build trust and move forward without a legal team.