The Credit Score for AI: Introducing the AURA Framework

By Ajay Arora

“Agentic AI (AI agents)” – Marketing Words of the Year. Everyone’s got “great AI” powered by agents. Not agentic? You’re not in the room in 2026. Or even the building. 

Agentic is AI with hands. Tell an agent you need to visit a customer in New York next Tuesday. It checks your calendar for conflicts, searches flights matching your airline preferences, books the ticket, reserves a hotel near the meeting address, adds everything to your calendar, and sends the itinerary to your expense system. 

By now, you’ve probably heard this quick take: Your AI agent is like a super-smart, super-eager-to-please intern. 

Would you let interns operate completely unsupervised from day one?

Of course not.

You would review their work.

You would check for consistency.

You would ask them to explain how they arrived at conclusions.

You would monitor whether they follow policy.

You might even check if they were confidently making things up. 

And most importantly — you would measure whether they are actually delivering business value (not just coffee). 

That’s what the AURA framework from SAFE does for AI.

AURA — AI Usage, Reliability & Assurance

SAFE developed this framework as the governance layer that makes agentic AI measurable, defensible and trustworthy. 

Boards, regulators, and your customers won’t take your word for it any more. They want evidence, not adjectives, that your AI-powered process or service meets objective performance metrics. And won’t hallucinate its way to a major lawsuit. 

At Safe Security, before the AURA initiative, validating AI required heavy manual testing. Teams had to periodically review prompts, check responses, test edge cases, and document findings. It was slow. It was inconsistent. It didn’t scale as AI agents multiplied across the enterprise. And it carried a nagging irony: You deploy AI to automate your work but have to deploy teams of humans to babysit the robots.

AURA replaces that manual oversight with continuous, automated evaluation following a consistent framework. 

Instead of guessing whether an AI system is “good enough,” AURA assigns every AI agent a quantified AI Trust Score (0–100) — a hard-number indicator of how reliable and production-ready that agent truly is.

That score isn’t arbitrary or black box. It’s built across multiple dimensions — think of it as a credit score (or credibility score) for AI performance. Like a credit score, it is a weighted composite index derived by some complicated math under the hood. The AURA factors, however, are easy to understand by non-technical stakeholders.  

• Accuracy & Consistency – Does the AI produce correct outputs? Does it remain stable across repeated queries?

• Explainability – Can it justify its reasoning in a way humans can understand and audit?

• Safety & Security – Is it protecting sensitive data, resisting prompt injection, and avoiding hallucinations?

• Reliability & Resilience – Does it behave predictably under stress and edge cases?

• Latency – Is it fast enough for real operational use? Or does it think about it for so long you’ve already done it yourself?

• Human Override Signals – How often do humans need to intervene or correct it?

• Business Impact Alignment – Is it delivering measurable operational or financial value?

AURA then translates all of this into something leaders can act on:

Live dashboards.

Continuous AI Trust Reports.

Simple Green / Amber / Red indicators for instant executive visibility.

No guesswork. No blind trust. No marketing claims.

The core idea is simple but powerful:

Trust should not be assumed.

Trust should be measured.

AURA makes AI trust quantifiable — giving enterprises proof that their AI systems are not only innovative, but safe, reliable, and operationally sound in the real world.

And in an era where AI is everywhere, measurable trust becomes the real differentiator. 

Tell your intern you need those receipts!