A Framework for Responsible, Defensible, and Auditable AI in Compliance

The promise of AI in compliance is enormous, but so are the risks.
Most so-called “AI-powered” solutions fail to meet enterprise standards for model risk management, auditability, and transparency. They look impressive in a demo but often crumble under real scrutiny from internal audit, model validation, or regulators.

Sigma360’s AI Evaluation Framework changes that.
Built for regulated institutions, it provides a structured, repeatable approach for evaluating and governing Generative AI solutions. Every model can be explainable, secure, and aligned with your risk appetite.

👉 Complete the form to download the full framework.
Learn how to operationalize AI safely and confidently inside your compliance program.

Why Download This Framework

This is not a white paper. It is a blueprint.

The Sigma360 GRACE Framework (Governance, Robustness, Accuracy, Compliance, and Evolution) helps financial institutions move beyond hype and build AI that withstands regulatory and internal scrutiny.

Inside the guide, you will learn how to:

• Evaluate whether an AI system can be trusted, documented, and audited.
• Apply model governance principles from the start of development or procurement.
• Establish a human-in-the-loop framework that reinforces reliability and transparency.
• Monitor performance drift and maintain ongoing oversight.
• Enforce data privacy, zero-retention policies, and regulatory compliance in AI workflows.

The framework also includes a vendor evaluation checklist used by compliance teams to identify credible AI partners.

What’s Inside the Sigma360 AI Evaluation Framework

1. Governance and Documentation
Define ownership, purpose, and audit trails for every model decision. If it cannot be documented, it cannot be trusted.

2. Robustness and Reliability
Ensure models remain accurate under stress and data volatility. Understand how they perform against noise and edge cases.

3. Accuracy and Relevance
Measure performance with task-specific metrics that reflect real business outcomes such as materiality and reputational risk.

4. Compliance and Security
Guarantee strict controls for data privacy and model security. Support zero-data-retention by default.

5. Evolving Monitoring and Maintenance
AI is not static. Build live feedback loops and retraining protocols to maintain model integrity over time.