AI and machine learning: Cybersecurity action and reaction

AI and machine learning are already viable resources in the battle against cybercrime, but are they being under-utilized in the face of growing digital incursions?

Increasing threat levels

The FBI’s Cyber Crimes Division reported four times the amount of cyber attacks than were recorded pre pandemic. That is more than 4,000 instances of hacks, data breaches, and the like every day. 

Perpetrators of cybercrime pose an imminent threat to every industry, every business and every organization—especially those that rely upon data to deliver effective service models and products. Every person who leaves a digital fingerprint is vulnerable to potential exposure and exploitations. 

In 2019, an IBM study estimated the financial consequences of data breach within a company employing 500 employees or fewer was an average of $2.52 million. Many of these businesses bring in less than $50 million in annual revenue. Even for a small business storing and utilizing data at a smaller scale, these losses are staggering, potentially crippling or killing the business outright. 

Malware, ransomware and bots are developing so quickly that building an effective defense is difficult, primarily because cybersecurity professionals largely do not know how new and active threats will function. AI and machine learning are not a fix-all, but these tools and the people who know how to fully harness them are already making a huge impact in securing data stores against digital incursions across every industry.

Maintaining a stalwart digital defense is still a complicated endeavor. While many forms of cybersecurity software are effective, bad actors are continuously making alterations to their code, making it difficult for many programs to identify a piece of code as harmful. AI and machine learning are designed to effectively mitigate this particular issue. 

A database properly outfitted with AI and machine learning capabilities has the advantage of being able to compare new ransomware, malware and bots to every one of those ever detected in its network before. Even with adjustments, if code is acting like a variant similar to it, AI and machine learning can block said attack based on the likelihood of that behavior occurring again. 

Basically, the AI or machine learning software can look at code the way you or I would look at a fish and a bird. If we have seen a bird fly or fish swim, even with many significant variations, we know how those particular organisms will act and react 99 percent of the time. It is a rudimentary way to look at it, but essentially it’s accurate; no matter how effective tools like AI and machine learning may be, you occasionally come across a penguin. 

Current AIs are especially good at monitoring networks for potentially dangerous activities, as malware, ransomware and bots are not the only threats to your digital environments. Credentials can be stolen and bad actors can gain access to sensitive data through means that are legitimate. What AI does is track activity daily and, in that context, pick up on activities that fall too far outside the margins of acceptable variance, resulting in limits to or discontinuation of that user’s access. 

These are just a few ways AI and machine learning can reduce the time it takes to respond to threats and the data exposed to a negative digital presence. At LaunchGig, we want all of our clients and everyone reading this to know that these threats are real, but also that there are tools and—more importantly—the AI and machine learning educated professionals capable of powerfully responding to them. 

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