Artificial Intelligence & Cybersecurity
Artificial Intelligence (AI) is progressively being ingrained in the fabric of business and is being broadly applied across a variety of application use cases. However, not all industries are at the same level of AI adoption: the information technology and telecommunications industry are the most advanced, with the automobile sector trailing closely behind.
Did you know that a majority of the world’s CEOs consider cybersecurity the biggest threat to the world’s economy over the next decade? It’s true, and the cybersecurity statistics to back up these beliefs are absolutely staggering. On average, the global economy loses nearly one trillion dollars a year due to cyber-attacks!
Unfortunately, everyday cybercriminals are figuring out new unique ways to breach organizations’ IT infrastructures and gain access to sensitive data that will later be sold on the dark web. Whether it’s through sophisticated ransomware attacks, malware attacks, phishing attacks, or other strategies to foster cyber threats, cybercriminals are causing data breaches at unprecedented rates, posing extreme financial repercussions for the targeted organizations.
To combat this ever-evolving and growing challenge, organizations are now leveraging cutting-edge research in the advancement of artificial intelligence to detect cyber threats in new unique ways, and the results are astounding. By implementing artificial intelligence-enabled cybersecurity systems designed to mimic human intelligence and human intervention, organizations are gaining an unprecedented edge in detecting imposters and limiting the overall cost of data loss.
As noted, cybersecurity is going through a massive evolution with the enablement of AI systems. These artificial intelligence-enabled detection mechanisms are born out of research in big data, deep learning, and machine learning technology to detect cyber threats using near-human reasoning and decision-making.
Researchers in this field believe that this near-human decision-making capability will have incredible effects in the world of cybersecurity. As we look to the future to assess how artificial intelligence will impact cybersecurity, we can posit that environments will no longer need to act reactively to these intrusive threats but proactively. This subtle difference will create massive waves in how incident response plans are developed and how cybersecurity will look forever.
AI may be both a benefit and a problem for cybersecurity since it is a general-purpose, dual-use technology. This is supported by the fact that AI is employed both as a sword (i.e. to enable malicious assaults) and as a shield (i.e. to defend against them) (to counter cybersecurity risks).
With an additional complication: While the use of AI for defensive purposes faces a number of constraints, particularly as governments (and the European Union) move to regulate high-risk applications and promote the responsible use of AI, the most pernicious uses are multiplying, the cost of developing applications is plummeting, and the ‘attack surface’ is becoming denser every day.
As these artificial intelligence systems progress and become more finely tuned, we’re finding it’s creating an entirely new specialty within information security. Today, there are dedicated cybersecurity teams, cybersecurity professionals, cybersecurity solutions, and security operations built around developing, implementing, and managing artificial intelligence-enabled cybersecurity solutions in the modern IT environment. This emerging information security niche takes the traditional security systems, threat detection strategies, and threat intelligence paradigm and reimagines it in a fresh, more threat-resilient way.
As we discuss the importance of artificial intelligence to enhance an existing cybersecurity posture, it’s also important to discuss how machine learning fits into the picture. Machine learning is a branch of artificial intelligence dedicated to the advancement of enabling systems to gain the ability to learn from past experiences. As AI systems are designed to mimic human intelligence, it’s machine learning that enables artificial intelligent systems to “learn” from previous events to make better decisions in the future.
Machine-learning and deep-learning techniques will make complex cyber-attacks easier to carry out, allowing for more focused, quicker, and damaging strikes. The influence of AI on cybersecurity is expected to broaden the dangerous environment, bring new risks, and change the nature of existing threats. Apart from offering new and stronger channels for carrying out attacks, AI systems will also become more vulnerable to manipulation.
To address this need for better artificial decision-making, advanced artificial intelligence algorithms are trained to detect cybersecurity threats by analyzing massive data sets, including thousands and thousands of examples of cyberthreats. These massive datasets are so incredibly large that humans could in no way analyze them at the same speed or efficiency that artificially intelligent systems can. This incredible ability to “learn” from previous events through machine learning coupled with the ability to make real-time decisions through artificial intelligence makes these new cybersecurity solutions so incredibly powerful.
Furthermore, a great benefit to organizations is the autonomous manner in which these artificial intelligence systems behave. The intention of these systems is to analyze events and make decisions enabled by automation. As these systems become more complex and advanced, the targeted end state is to automate many of the manual analysis and decision-making processes humans are currently tasked with.
The intended outcome is that artificial intelligence-enabled detection systems can catch malicious cyber-attacks early. It accomplishes this autonomously, based on unique key signatures of the cyber attack the AI system has been trained to detect.
Backup Of Sensitive Data
This paradigm shift in the way we design systems to protect organizations is not limited to cybersecurity. Data protection and backup are also looking to benefit from the advancements within artificial intelligence, machine learning algorithms, and advanced neural networks. Backup strategies have always grappled with one major challenge – backing up infected systems. If the malware can effectively sit undetected in a system long enough, the malware can not only infect the production environment but also the backup production data.
In this scenario, organizations face serious challenges ensuring they can remove and recover from the harm imposed by a major malware attack. Here, data protection solutions are looking to implement artificial intelligence and machine learning to catch these modernized threats before they become a real challenge.
As we’ve discussed in this investigation of modern threats thus far, cyberattacks are, of course, the prominent challenge facing an organization’s network security today. The great promise of AI technology is not only effectively detecting malicious attacks but also removing false positives. False positives are one of the largest challenges in data detection and security solution decision-making today.
Often, security systems today incorrectly flag normal behavior within an IT network as malicious behavior. We’ve designed highly advanced firewalls to detect malicious activity entering an internal network from the outside world; however, threats still make their way through. The challenge here is often even the most advanced firewalls, and other network-connected security devices can often unintentionally allow malicious activity over the network or accidentally flag non-malicious behavior as malicious behavior.
As we look forward to assessing the landscape of cyberattacks, it’s clear that we will enter a time where detection systems are more human-like each and every day, and in parallel, these cyberattacks will attempt to mimic the same technology to spoof security systems. We are truly headed for a fascinating time in cybersecurity through these incredible advancements in AI and machine learning.