THE PROBLEM
Three Critical Governance Gaps Current Frameworks Miss
Inadequate Use Case-Level Risk Assessment
Each AI deployment presents unique risk profiles requiring tailored governance. Most frameworks provide one-size-fits-all guidance that fails when deployed systems exhibit bias and high error rates.
Case Study: Humana Healthcare Claims
Principles Without Actionable Controls
Frameworks like ISO 42001 and NIST AI RMF remain at high conceptual levels, leaving practitioners unable to translate governance requirements into specific technical implementations.
No Mechanism for Operationalizing at Scale
Organizations lack systematic approaches to embed trustworthy AI throughout the development lifecycle, measure compliance quantitatively, or provide role-appropriate visibility from boards to data scientists.
THE SOLUTION
AI TIPS 2.0: Trust-Integrated Pillars for Sustainability
A comprehensive operational framework with eight governance pillars, risk-based scoring methodology, and complete regulatory mappings—battle-tested across 1,000+ enterprise AI projects in 120 countries.
Security
Privacy
Ethics
Transparency
Explainability
Regulations
Accountability
Audit
↔ NIST AI RMF ↔ EU AI Act ↔ ISO 42001 ↔ CSA AI Controls Matrix
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Pamela Gupta
Founder, Trusted AI | US Department of Defense AI Advisor
2025 Wasserman Award Recipient
#3 Global Risk Management
#7 Global Cybersecurity
US DoD AI Advisor
1,000+ Enterprise AI Projects
