Beatrice did not give up immediately.
After her loan application was rejected, she decided to challenge the decision.
There had to be a mistake.
She had a stable income. No debt issues. Nothing that should raise concern.
So she reached out.
The response came quickly.
“Your application was assessed using our automated decision system. Unfortunately, we are unable to provide further details.”
Beatrice read the message again.
No explanation.
No breakdown.
No human review.
Just a decision… with no clear reason behind it.
When There Is No One to Question
In the past, decisions like this involved people.
You could ask:
- Why was I rejected?
- What can I improve?
- Can someone review my case?
There was always someone accountable.
But with AI systems, things are different.
The decision is made instantly.
The process is hidden.
And often, there is no clear path to challenge it.
The Problem With “Black Box” Decisions
Many AI systems operate in what experts call a black box.
That doesn’t mean they are broken.
It means:
The system produces an outcome, but the reasoning behind it isn’t easily understood
Even the organisations using these systems may not fully understand:
- how certain decisions are made
- why one person is approved and another is rejected
- what factors influenced the outcome
So when someone like Beatrice asks for answers…
There may not be a clear one to give.
Why This Becomes a Risk
At first, this might not seem like a cybersecurity issue.
But it is a governance and risk problem.
Because when decisions cannot be explained:
- mistakes can go unnoticed
- bias can remain hidden
- accountability becomes unclear
- trust begins to break down
In Beatrice’s case, the risk wasn’t just the rejection.
It was the lack of transparency behind it.
When AI Gets It Wrong
AI systems are trained on data.
And that data may contain:
- incomplete information
- historical bias
- patterns that don’t reflect real-life situations
This means AI can make decisions that are:
- unfair
- inaccurate
- difficult to justify
And without the ability to challenge those decisions, the impact becomes even more serious.
The Governance Gap
This is where governance becomes critical.
Organisations cannot rely on AI systems without oversight.
They need to ensure:
- decisions can be explained
- outcomes can be reviewed
- accountability is clearly defined
Because if no one can challenge a decision…
Then no one is truly responsible for it.
A Familiar Pattern in a New Form
This problem may feel new, but the underlying issue isn’t.
In many industries, systems have always failed when:
- processes were unclear
- accountability was weak
- decisions were not transparent
AI is simply amplifying that problem.
Faster decisions.
Less visibility.
Higher impact.
The Human Side of the Problem
Beatrice wasn’t trying to break a system.
She was trying to understand it.
She wanted clarity.
A reason.
A chance to respond.
What she faced instead was a system that had already decided and moved on.
On A Final Note
AI is becoming a powerful part of how decisions are made.
But power without transparency creates risk.
Because when people cannot question decisions…
They cannot trust them.
And in cybersecurity, governance, and risk management, trust is everything.
If you are beginning your journey in cybersecurity or GRC, this is something worth thinking about:
It’s not just about building smarter systems
It’s about making sure those systems can be understood, challenged, and trusted
