The Core Elements of a Robust AI Governance Framework
There is a dangerous delta in the enterprise: 93% adoption vs. 7% effective governance. Most companies are operating on "hope-based" policies that fail the moment a model drifts or a vendor changes an API. This framework is for the leaders who need more than a PDF of rules. It is a guide to building a system of record that connects inventory, assessment, and real-time enforcement into a single, robust program.
The AI Governance Paradox: Bridging the Divide Between Adoption and Control
This whitepaper gives risk, compliance, and technology leaders a practical framework across the core dimensions that determine whether your AI governance actually holds:
- Security — prompt injection, model inversion, and the Shadow AI problem
- Safety — enforcing what every AI system can and cannot do
- Reliability — catching concept drift and data drift before they affect decisions
- Accountability — the audit trail regulators and auditors now expect
- Data and Privacy — stopping sensitive data before it reaches any model
- Societal Impact — bias, fairness, and the obligations that are becoming law
What You Will Take Away:
- A framework for governing AI across all the core dimensions
- A regulatory reference: EU AI Act, NIST AI RMF, ISO/IEC 42001, OCC SR 11-7
- Practical guidance on runtime enforcement, redaction, and policy management
- A blueprint for connecting inventory, assessment, and enforcement in one program
Who It Is For: CISOs, Chief Risk Officers, compliance leads, and technology executives building or maturing an enterprise AI governance program.
Want to see how Enlighta GovernAI addresses all the core dimensions in practice? Book a Demo