It’s tempting to aim for the minimum viable solution—a framework just good enough to satisfy auditors. When looking at the kind of reporting I can create overlaying regulatory frameworks to our data we gather through our cloud providers and security systems, you feel like in the next level bakery filled with pie-charts. Stunning stuff at your fingertips, many pages you can dazzle boards and auditors with.
On the one hand, simplicity saves time and money. Keeping controls lean means fewer resources wasted on unnecessary processes. It allows companies to focus on the core risks, avoiding a web of overcomplicated systems that can slow things down.
But the minimum has its risks. Regulations are a moving target, especially in areas like cybersecurity. Meeting today’s standards might leave you exposed tomorrow. And let’s not forget the optics. When incidents happen, regulators and boards rarely care that you ticked all the boxes; they’ll wonder why you didn’t prepare better.
So, is there a middle ground? Maybe it’s not about the “minimum viable” at all but finding a balance between efficiency and resilience. Enough controls to meet evolving threats without overengineering. Are we designing systems for the next audit or the next challenge?
When we discuss AI, conversations typically focus on how it can improve efficiency, reduce costs, and enhance existing processes. But is this narrow view limiting the true possibilities of AI?
The time where industrialized society's primary goal was gathering information has ended. Information is now abundant and widely accessible, presenting the new challenge of effectively combining and applying this information to drive innovation. We must evolve from passive recipients of data to active creators, capable of synthesizing ideas and applying them in practical, innovative ways.
