There is a big debate currently going on about whether Artificial Intelligence (A.I.) is a good or a bad thing in terms of its effect on humanity’s wellbeing. And as AI is finding more and more applications in the business world, it’s time to analyze the effects it has on security. The term “security” covers such a broad spectrum and contexts at both the industry and government level. Nevertheless, Artificial Intelligence and Machine Learning (M.L.) technologies have found applications across all of them.
While many of these have the potential to greatly benefit society, the evolving application of AI technologies often opens up more questions than answers. This refers, in large part, to the rules, regulations, and moral judgments that apply to them. In many respects, Artificial Intelligence and security are an almost perfect match, and M.L. has come just at the right time to fill in the gaps of the traditional rule-based data security systems. Below, we will be taking a look at the positives and negatives of Machine Learning and AI in terms of cybersecurity as well as how it will affect the workforce.
The Positives of AI in Cybersecurity
Cybersecurity professionals have long been saying that passwords, alone, can be incredibly vulnerable to cyberattacks, resulting in stolen financial and/or personal information. Many companies, big and small, have fallen victim to security breaches as a direct result of compromised passwords. It’s for this reason why biometric logins, in the form of fingerprint, palm print, facial recognition, or human eye scanning, are seeing such a huge rise in use in various places and devices, such as smartphones. This is one of the positives of AI in cybersecurity.
AI will also be used to detect a security threat and various forms of potentially malicious activities. Most traditional systems simply cannot keep up with the overwhelming number of malware that appears every month. This is another area where AI can step in and address the problem. Cybersecurity companies are using AI systems and complex machine learning algorithms to detect malware and viruses by running various pattern recognition software. These systems can be trained to identify all sorts of small behavior in regards to malware and ransomware attacks even before these have the chance to enter the system. AI systems can also use predictive functions that far surpass the speed and accuracy of more traditional approaches.
A.I.-run systems can also be used for natural language processing that collects information automatically from various sources of cybersecurity-related information. This data can be used to give up-to-date insight into the latest cyber attacks, anomalies, and prevention strategies, which can be used by cybersecurity professionals to stay updated on the latest risks and build responsive strategies.
In addition, AI systems can find their uses in terms of multi-factor authentication. Within many organizations, different users have different levels of authentication privileges that are also influenced by the location from which data is accessed. With A.I., the authentication framework can be more dynamic and applied in real-time, rapidly changing the users’ access privileges based on their location. In other words, the multi-factor authentication will collect user data to understand their behavior and determine their access privileges.
The Drawbacks and Limitations of AI in Cybersecurity
While the benefits presented above only scratch the surface of what AI can bring to the field of cybersecurity, it’s also important to keep in mind that there are also some limitations that keep this technology that keeps it from becoming mainstream. For starters, AI systems require large amounts of resources in terms of data, memory, and computing power to build and maintain.
In addition, AI systems are trained through learning data points. This means that cybersecurity firms need to acquire these many different sets of data in terms of anomalies, malware and non-malicious codes. Obtaining these accurate data sets can take a long time and resources; something that many organizations may not be able to afford at the moment.
Another major drawback of AI is that cyber attackers can also use it to test their malware to improve them and even make them A.I.-proof. This type of malware can be extremely destructive as it can learn from existing AI tools and develop more sophisticated attacks that are able to penetrate traditional cyber security programs and protocols with relative ease.
How AI Will Impact Cybersecurity Jobs?
While AI systems do replace humans in some industries, this doesn’t seem to be the trend with it comes to those working in the cybersecurity sector. While automation is great at reducing operational errors and helping scale up tasks, AI is not completely practical and effective at spotting cyberattacks. There have been plenty of cases where false positives or missed attacks have occurred. The reason for this is that AI still lacks the contextual awareness needed to deal with exceptions that fall outside its programmed parameters. In many cases, human experience is needed to analyze and make critical decisions on potential risks. Relying solely on artificial intelligence to manage security can lead to more vulnerabilities as attacks could exploit the machine learning element in automation.
However, when AI comes in support, cybersecurity professionals can achieve more. They will be able to focus on the critical tasks, while AI systems will take care of much of the “legwork” in terms of processing, data analysis, and aiding in decision making. To put it simply, AI systems don’t even come close to eliminating cybersecurity professionals; it will only help them become more efficient. It’s important to keep in mind that the cybersecurity sector has severe skills shortages as it is.
The Evolution of New Cybersecurity Job Roles
While AI will not eliminate cybersecurity roles, it will, nevertheless, change them. As more and more computer systems move to the cloud, cybersecurity professionals will no longer have to go through the hundreds of security tools available on the market, determining which ones will be the most effective for their organization’s needs. Instead, cloud computing providers will offer their built-in security solutions. This will result in companies looking for professionals that are able to understand other security issues like the potential business impact and risk of different projects and architectural decisions. This, in turn, will demand a different set of cybersecurity skills and knowledge, when compared to current roles. Aside from technical knowledge, cybersecurity experts will also need to understand how to integrate with other key business decision-makers.
When it comes to security experts in the Information Technology sector, from a legal compliance perspective, they are best suited when it comes to policy formulation, risk management and analysis, and cyber attack response. In other words, they can help AI systems learn and evolve. This is also the case with the Spain-based antivirus company, Panda Security. When it first started, the company had a number of experts reverse-engineering malicious code and writing signatures. Today, however, the company would need “hundreds of thousands of engineers” to do the same job. With the help of AI, they use a small team of engineers to look at over 200,000 new malware samples on any given day.
The question here is about what happened to those employees that would have, otherwise, performed that job? Were they made obsolete by AI? The answer is No, but they will need to upgrade their skill set as a means of remaining relevant to the job market. AI will create new job positions in cybersecurity that will provide new problems to be analyzed and solved. This is what’s now known as “new collar jobs,” which are also part of IBM’s hiring strategy.
Basically, once graduates enter the IBM workforce, they are aided by AI to give their career a fast start. Even those entering as Junior analysts, they have the ability to investigate new malware infecting mobile devices. The AI system will quickly research the new malware infecting those devices, identifying its characteristics as reported by others, and provide the Junior analyst with the best course of action. This will relieve security employees from doing much of the manual work that implies going through piles of data and code. This will allow them to focus on the actual work of solving the issue.
AI and cybersecurity professionals are not in conflict when it comes to this sector because of the concerns surrounding cyber threats and security risks posed by AI in the wrong hands. Instead, they will complement each other by creating new job opportunities focused on redefining security measures against adversarial attacks.
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