The past few years have collectively seen the rapid evolution of machine learning (ML) technology. This sophisticated asset has opened new doors for technology-based companies, presented fresh options on pre-existing concepts, and ultimately changed the face of what is possible in terms of autonomous human perception — and this notion is especially prevalent within modern cybersecurity. Constantly learning technology has eliminated the need for certain human involvement in the security process, all while supplementing the process as a data-driven deterrent.
However, as we move into another year of potential growth in this vital technological sector, some commentators have predicted a wave of equally sophisticated threats from the cybercrime community. Just like the gatekeeping automatons they hope to outsmart, these individuals are finding ways to learn and adapt, changing their approach in order to thrive within an ever-changing landscape.
Though unfortunate, it is undeniable that hackers and cyber terrorists have become increasingly clever in their recent operations. Now, these criminals are finding ways to exploit the very characteristic setting machine learning at the forefront of technological possibility: self-reliant activity. By infiltrating this aspect of ML-ready units, attackers could, in turn, enable digital security systems to corrupt themselves, opening windows of opportunity to harvest valuable data. To make matters worse, these situations could potentially allow criminals to achieve their goals much quicker than previously observed.
Furthermore, many criminals are developing nefarious new ways to potentially vehicalize ML technology — namely AI-enabled bots originally designed to streamline various processes. For instance, chatbots, which have already been developed to oversee financial transactions and various concierge-based services, are predicted to be a major cybercrime target in this regard; they could theoretically be used to not only crash utilities and hack protected domains, but also to potentially influence human interaction through the use of false prompts.
What can be done?
While this outlook is grim at a glance, the good news is there are ways to avoid these threats. There is arguably one broad lesson encapsulating the future of ML-based cybersecurity: certain pre-existing paradigms must shift to answer a continually evolving wave of criminal activity. This notion has already been suggested as we begin to look at longstanding aspects of the cybersecurity landscape, such as password-protected domains. While alternatives to such protections are still the topic of their own widespread debate, the reality is that passwords (and similar authentication methods) are proving to be insufficient as a combative tool towards advanced hacking attempts. Perhaps this conversation is a microcosm of the entire scenario as we currently know it; these are the topics must continue to discuss and question — our own process of changing, adapting, and, most importantly, learning from past follies.
Cybersecurity is an important variable in the professional world. The industry has seen a hiring upswing in recent years, and the need for new sharp minds only continues to increase as the cyber landscape changes.
Jeremy Robertson, founder of Lockwood Executive Search, is an experienced professional in the recruiting industry. For more information, click here.