With the exponential rise in the adoption of digital tools and technologies (the majority in a non-optimal, non-strategic way) comes the risk of opening more channels of cyber threat. For anyone claiming to have a genuine interest in technology, it is imperative to have a decent understanding of cybersecurity.

I recently started my turbocharged deep dive into cybersecurity to become certified in cybersecurity. I do not intend to become a cybersecurity professional. Still, anyone who aims to understand the imperatives faced by the world of technology must know the role of cybersecurity embedded within those imperatives.

Risk management was one of the initial foundational topics I was listening to in a lesson. Within risk management, the topic of risk treatment discussed the four categories of risk treatment. These four categories were:

• Risk acceptance
• Risk avoidance
• Risk mitigation
• Risk transference

One thought that emerged during this lesson was how AI can now play a key role in moving risks from one category to another. So essentially, if a risk was inevitable before, and you HAD TO accept it and then plan around that acceptance, can AI move that risk to the risk mitigation category? Similarly, can a risk in the mitigation category be avoided entirely by leveraging AI? Let us discuss an example.

The first example is the one that made me think about the recategorization. While listening to tutorial videos on my porch, I saw a flock of sparrows working furiously to empty seeds from my bird feeder. Reminded me of my visits to my ancestral village as a child. We would bring freshly ground flour to our city home every visit. So when we got to the village, helpers who helped us in our fields would wash the grain and then dry it before it went to the mill to get ground into flour. While the grain dried on mats after washing, flocks and flocks of sparrows would arrive, using this opportunity to consume as much as possible. A person was assigned to keep scaring them away, but they still did a decent amount of damage.

This wastage due to the sparrow attack memory diverted my thoughts in a different direction. In countries like India, the government buys a significant amount of essential grains stored in granaries across the country. A decent percentage goes to waste due to pests. In fact, World Economic Forum estimates that 40% of world’s crop production goes waste due to pests and the problem is worsening.

In some areas, where rodents are not a significant problem, the wastage risk is primarily due to stored grain getting infested with insects. And I recalled a research paper I read this past weekend titled “Application of Machine Learning for Insect Monitoring in Grain Facilities.” There are certain caveats in the approach in the paper in my opinion but with certain modifications, and then with extrapolations, approaches like these can be leveraged to address a variety of pest problems.

This risk of wastage due to insect infestation is widely accepted, and the government buys more to account for that. The result is that a significant portion of these grains sometimes go bad while being stored. This is an example of accepted risk. As you can see, AI can convert this to avoidance or mitigation, depending on what type of pest challenges you are facing and of what magnitude.

Since this thought crossed my mind while listening to the lessons, I could not stop my mind from thinking of plenty of others. It is incredible how AI is changing the paradigms and theories of other disciplines, risk management being the example in this case. However, the examples of AI being able to transform risk management go beyond this grain example and deeply into many risks that enterprises face these days. Exciting times!