AI Governance & Responsible AI
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AI Agents Are the New Employees — But Who Manages Them?
Responsible AI

AI Agents Are the New Employees — But Who Manages Them?

A new class of digital workers is entering the enterprise. Governance — not hype — will determine who succeeds. On a recent call with a CIO at a large healthcare network, she described a moment that caught her entire leadership team off guard. Their AI agent, designed to help with documentation and

by Dilip Mohapatra
Operationalizing AI Assurance: Turning Evaluation into Evidence
TRACE Responsible AI

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.

by Dilip Mohapatra
Compliance Is No Longer Enough: How HAIGS Builds Trust in Healthcare AI
Responsible AI AI Compliance

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.

by Dilip Mohapatra
Why HITRUST AI Risk Management Needs Metrics, Not Policies
TRACE

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.

by Dilip Mohapatra
Why Responsible AI Needs TRACE — Operational Evidence, Not Just Policies
TRACE Responsible AI

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.

by Dilip Mohapatra
Doctor, This AI Model Is Safe—Here’s the Proof
TRACE Responsible AI

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.

by Dilip Mohapatra
Metrics Aren't Compliance: How TRACE Adds Context for Auditable AI
Responsible AI TRACE

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.

by Dilip Mohapatra
TRACE + Deepeval: Making Open-Source Metrics Audit-Ready
TRACE Responsible AI

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.

by Dilip Mohapatra
Bridging the Metrics-to-Evidence Gap
Responsible AI TRACE

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.

by Dilip Mohapatra
Turning AI Metrics into Audit‑Ready Evidence with the Responsible AI TRACE Framework
TRACE

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.

by Dilip Mohapatra
Patient-Facing AI Factsheets: From Theory to Practice
AI Compliance Industry Insights

Patient-Facing AI Factsheets: From Theory to Practice

In 2025, transparency is a must. Learn how healthcare startups can publish a patient-facing AI factsheet in 30 days to meet EU AI Act, NIST, and ISO 42001 standards—while building trust with patients and payers. Includes examples, KPIs, and a free template.

by Dilip Mohapatra
3‑Step Roadmap for Your Responsible AI Journey
Responsible AI

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.

by Dilip Mohapatra
AI Governance & Responsible AI

AI governance, risk, and compliance made simple.

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Featured Posts

From Principles to Proof: Why Healthcare AI Needs More Than Guidelines — and How CognitiveView is Operationalizing CHAI

From Principles to Proof: Why Healthcare AI Needs More Than Guidelines — and How CognitiveView is Operationalizing CHAI

by Dilip Mohapatra
AI Human Impact Signals (AI-Human)

AI Human Impact Signals (AI-Human)

by Dilip Mohapatra
Why Responsible AI Needs TRACE — Operational Evidence, Not Just Policies

Why Responsible AI Needs TRACE — Operational Evidence, Not Just Policies

by Dilip Mohapatra
Bridging the Metrics-to-Evidence Gap

Bridging the Metrics-to-Evidence Gap

by Dilip Mohapatra
3‑Step Roadmap for Your Responsible AI Journey

3‑Step Roadmap for Your Responsible AI Journey

by Dilip Mohapatra
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