Artificial intelligence (AI) and machine learning are becoming embedded in businesses across the globe, and cybersecurity is quickly emerging as a key area of focus for enterprises striving to enhance the security of sensitive data.
Despite this growing adoption, however, many are still struggling with misconceptions and confusions surrounding the different types of solutions available on the market today.
To set these misconceptions to rest once and for all, one must recognize certain key considerations around AI and understand how it is disrupting the information and network security realm.
It’s equally important to recognize the difference between traditional automation and intelligent automation and its impact on cybersecurity. With this knowledge in hand, business leaders can then begin to capitalize on the opportunities and long-term potential of AI and automation in the intelligent enterprise.
The role of AI in cybersecurity
Perhaps the ultimate turning point in terms of organizations recognizing the critical importance of adequate network security was the Target breach of 2013. The utter magnitude of that breach opened the eyes of many and placed the topic of cybersecurity front and center on the list of business priorities.
Since that time, there has been a steady influx of attacks that have evolved and increased in both complexity and frequency, subsequently increasing the need for fast, accurate incident response and remediation.
The challenge many organizations face, however, is how? Hiring additional staff isn’t always feasible, whether it’s due to budgetary restraints or simply a lack of qualified personnel.
Additionally, given the sophistication and relentlessness of today’s cyber-attacks, many organizations are finding that human ability is no match. That’s where automation and orchestration technology has become a true game changer.
Combined with artificial intelligence and machine learning capability, automated cybersecurity is meeting attackers head on and essentially fighting fire with fire.
In the context of cybersecurity, AI is able to perceive its own environment well enough that it can independently identify threats and take the appropriate action, all without the need for human intervention. AI is particularly powerful from an incident response perspective because it is adept at recognizing patterns and anomalies far better than any human agent ever could.
Essentially, it’s like having an army of intelligent robots standing at the ready, 24/7/365 to detect and respond to threats. Few, if any, human workforces can accomplish such a feat, especially with such tremendous accuracy.
Machine learning is bringing that power to the next level because it can “learn” and improve on its own, based on factors such as the outcome of previous actions taken. Together with artificial intelligence, machine learning can effectively be used to predict future outcomes based on past events. This can help humans make more data-driven and therefore more accurate business decisions. And when the monumental task of incident management can be shifted from human to machine, businesses are better able to allocate resources toward the most valuable human-led tasks.
Simply put, as the amount of data continues to grow and the global threat landscape continues to advance, both in number and sophistication of attackers, organizations can no longer rely on antiquated tools and manual activities.
Automated cybersecurity incident response powered by AI and machine learning will enable business leaders to stay a step ahead of the threats.
Traditional vs. intelligent automation
IT automation is certainly not a new concept, but the technologies behind it have progressed significantly in recent years. As a result of these advancements, businesses are benefiting in a number of tangible ways, including that of enhanced network security.
But what’s the difference between the traditional automation tools of the past and today’s sophisticated platforms that are powered by intelligent technology?
While both technologies function with similar end-goals in mind—that is, streamlining and automating manual tasks and workflows—intelligent automation is designed to take things a step further by augmenting human intelligence. Not only is this a more cost-effective and scalable approach, but it can be implemented without having to sacrifice process quality and reliability.
Ultimately, the key differentiator between traditional and intelligent automation is the ability to make decisions.
Basic automation tools are capable of gathering and organizing data into reports that human agents can then use to forecast and plan. With machine learning, that data can be analyzed by artificial intelligence at a rate of speed and accuracy far greater than humans are capable of. The result is more valuable information that can facilitate improved business decisions.
The future of AI in cybersecurity
The opportunities that AI-powered automation presents to the enterprise are many, particularly in terms of enhanced network security.
For instance, intelligent automation is capable of quickly detecting and identifying not only known but also entirely new classes of threats. Over time, these agentless systems will continue to learn, adapt and improve on their own, becoming even more effective at managing incidents and analyzing the changing behaviors of attackers.
Additionally, deep learning algorithms will be able to sift through enormous amounts of data in real-time to uncover valuable insights into the growing threat landscape, enabling rapid and effective improvements to existing incident remediation processes.
The long-term goal of automation powered by AI is to achieve an even greater level of flexibility along with enhanced thinking capability that matches the human mind as closely as possible. The result will be a genius-level platform that is faster, more accurate, more consistent and far more effective at achieving maximum cybersecurity than any human team could ever accomplish.
Such a system, just like the human cognition it’s designed to simulate, will be capable of learning new processes, adapting according to its changing environment, arriving at its own conclusions and making its own intelligent decisions.
Perhaps the most interesting fact of all is that this type of system is not some far off distant vision of the future, but a present reality and one that is already driving the intelligent enterprise of today with the promise of keeping it a step ahead of the threat landscape of tomorrow.
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