A Practical Governance Question

If an AI assistant generates a materially adverse or misleading representation of your institution today…

Could you reconstruct it six months from now?

Precisely.

• Exact prompt
• Exact model and version
• Exact output
• Exact timestamp
• Demonstrable testing conditions

Or would you rely on screenshots and internal recollection?

In controlled testing, we consistently observe:

1. Institutional ordering variance across models under identical high-intent prompts
2. Inclusion frequency swings between runs
3. Subtle model updates that alter recommendation structure without notice

This is not misconduct.
It is structural instability in external AI reasoning layers.

Yet these systems now influence:

• Procurement shortlists
• Institutional comparisons
• Diligence framing
• Therapy or vendor recommendations
• Risk perception

Boards are accountable for material risk exposure.

So here is the real question:

At what point does external AI representation become a control obligation rather than a marketing concern?

Before litigation?
Before regulatory inquiry?
Before a procurement loss?
Or only after?

AIVO Evidentia™ was developed precisely for this gap.

It provides time-indexed, multi-model, reconstruction-grade evidence of how institutions are represented inside AI systems under controlled prompt conditions.

Not monitoring.

Evidence.

We are interested in serious answers from governance, legal, and risk leaders:

When does absence of reconstructability become unacceptable?