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AI Assurance Framework

An AI Assurance Framework is a structured set of governance processes, controls, and assessments used to evaluate whether AI systems are trustworthy, secure, compliant, and operating responsibly.

AI Assurance Framework

Overview

As organizations adopt more artificial intelligence systems, they need a structured approach to evaluating whether those systems are trustworthy, secure, and operating responsibly.

This structured approach is known as an AI Assurance Framework.

An AI Assurance Framework is a structured set of processes, controls, assessments, and governance activities used to evaluate and provide confidence in AI systems throughout their lifecycle.

Rather than relying on a single review, an assurance framework brings together multiple governance activities into one consistent approach.

These activities often include:

  • AI risk assessments
  • AI audits
  • Security testing
  • Model validation
  • Continuous monitoring
  • Documentation reviews
  • Governance approvals

A helpful way to think about an AI assurance framework is a building inspection process.

Architects, engineers, electricians, and inspectors each evaluate different parts of a building before it is considered safe.

An AI assurance framework works in a similar way by evaluating multiple aspects of an AI system before and after deployment.

AI assurance frameworks work closely with AI Governance, AI Assurance, and AI Risk Management.

Why It Matters

An AI assurance framework provides organizations with a consistent way to evaluate AI systems, reduce organizational risk, and build trust with customers, regulators, and stakeholders.

Real-World Example

Before deploying an AI-powered medical diagnosis system, a hospital follows its AI assurance framework by completing security assessments, fairness testing, governance approvals, documentation reviews, and ongoing monitoring requirements.

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**Short Definition:**
An AI Assurance Framework is a structured set of governance processes, controls, and assessments used to evaluate whether AI systems are trustworthy, secure, and operating responsibly.