AI and Machine Learning in Cybersecurity, How They Will Shape the Future

AI cybersecurity, with the support of machine learning, is set to be a powerful tool in the looming future. As with other industries, human interaction has long been essential and irreplaceable in security. While cybersecurity currently relies heavily on human input, we are gradually seeing technology become better at specific tasks than we are.

Every technology improvement brings us slightly closer to supplementing human roles more effectively. Among these developments, a few areas of research are at the core of it all:

Artificial intelligence (AI) is designed to give computers the full responsive ability of the human mind. This is the umbrella discipline under which many others fall, including machine learning and deep learning.

Machine learning (ML) uses existing behavior patterns, forming decision-making based on past data and conclusions. Human intervention is still needed for some changes. Machine learning is likely the most relevant AI cybersecurity discipline to date.

Deep learning (DL) works similarly to machine learning by making decisions from past patterns but makes adjustments on its own. Deep learning in cybersecurity currently falls within the scope of machine learning, so we’ll focus mostly on ML here.

What AI and machine learning can do for cybersecurity

AI and cybersecurity have been touted as revolutionary and much closer than we might think. However, this is only a partial truth that must be approached with reserved expectations. The reality is that we may be faced with relatively gradual improvements for the future to come. In perspective, what may seem gradual when compared to a fully autonomous future is actually still leaps beyond what we’ve been capable of in the past.

In the business environment, AI is currently being used to analyse large amounts of data and to help streamline processes, it is also increasingly being used as a method of cybersecurity protection by alerting organisations of unusual activity. AI and machine learning tools help reduce cybercrime in a variety of ways, from automatic network security monitoring to behavioural analytics, vulnerability management or Phishing detection.

Behavioural analytics AI tracks a user’s patterns, for example, what time you generally log in and log off, what IP addresses you tend to use and then algorithms notice unusual activity and flag it for further investigation. AI-based systems proactively look for potential vulnerabilities in organisational information systems to determine when and how an organisation might be

attacked. With Phishing detection AI can detect and track more than 10,000 active phishing sources reacting much faster than a human could, AI can also differentiate between a fake website and a legitimate one quickly. As we explore the possible implications with security in machine learning and AI, it’s important to frame the current pain points in cybersecurity. There are many processes and aspects we’ve long accepted as normal that can be treated under the umbrella of AI technologies.

How AI Improves Cybersecurity

Threat hunting – Traditional security techniques use signatures or indicators of compromise to identify threats. This technique might work well for previously encountered threats, but they are not effective for threats that have not been discovered yet. Signature-based techniques can detect about 90% of threats. Replacing traditional techniques with AI can increase the detection rates up to 95%, but you will get an explosion of false positives. The best solution would be to combine both traditional methods and AI. This can result in 100% detection rate and minimize false positives.

Companies can also use AI to enhance the threat hunting process by integrating behavioral analysis. For example, you can leverage AI models to develop profiles of every application within an organization’s network by processing high volumes of endpoint data.

Vulnerability management – 20,362 new vulnerabilities were reported in 2019, up 17.8% compared to 2018. Organizations are struggling to prioritize and manage the large amount of new vulnerabilities they encounter on a daily basis. Traditional vulnerability management methods tend to wait for hackers to exploit high-risk vulnerabilities before neutralizing them.

While traditional vulnerability databases are critical to manage and contain known vulnerabilities, AI and machine learning techniques like User and Event Behavioral Analytics (UEBA) can analyze baseline behavior of user accounts, endpoint and servers, and identify anomalous behavior that might signal a zero-day unknown attack. This can help protect organizations even before vulnerabilities are officially reported and patched.

Data centers – AI can optimize and monitor many essential data center processes like backup power, cooling filters, power consumption, internal temperatures, and bandwidth usage. The calculative powers and continuous monitoring capabilities of AI provide insights into what values would improve the effectiveness and security of hardware and infrastructure.

In addition, AI can reduce the cost of hardware maintenance by alerting you when you have to fix the equipment. These alerts enable you to repair your equipment before it breaks in a more severe manner. In fact, Google reported a 40 percent reduction in cooling costs at their facility and a 15 percent reduction in power consumption after implementing AI technology within data centers in 2016

Network security – Traditional network security has two time-intensive aspects, creating security policies and understanding the network topography of an organization.

Policies —security policies identify which network connections are legitimate and which you should further inspect for malicious behavior. You can use these policies to effectively enforce a zero-trust model. The real challenge lies in creating and maintaining the policies given the large amount of networks.

Topography—most organizations don’t have the exact naming conventions for applications and workloads. As a result, security teams have to spend a lot of time determining what set of workloads belong to a given application.

Companies can leverage AI to improve network security by learning network traffic patterns and recommending both functional grouping of workloads and security policy.

AI and Machine Learning in Cybersecurity How They Will Shape the Future

How machine learning is used in cybersecurity

Machine learning security solutions are different from what people envision to be of the artificial intelligence family. That said, they are easily the strongest cybersecurity AI tools we have to-date. In the scope of this technology, data patterns are used to reveal the likelihood that an event will occur — or not.

ML is somewhat opposite to that of true AI in some respects. Machine learning is particularly “accuracy” driven, but not as focused on “success.” What this means is that ML proceeds intending to learn from a task-focused dataset. It concludes by finding the most optimal performance of the given task. It will pursue the only possible solution based on the given data, even if it’s not the ideal one. With ML, there is no true interpretation of the data, which means this responsibility still falls on human task forces.

Machine learning excels at tedious tasks like data pattern identification and adaptation. Humans are not well suited to these types of tasks due to task fatigue and a generally low tolerance for monotony. So, while the interpretation of data analysis is still in human hands, machine learning can assist in framing the data in a readable, dissection-ready presentation. Machine learning cybersecurity comes in a few different forms, each with its own unique benefits:

Data classifying – works by using preset rules to assign categories to data points. Labeling these points is an important part of building a profile on attacks, vulnerabilities, and other aspects of proactive security. This is fundamental to the intersection of machine learning and cyber security.

Data clustering takes the outliers of classifying preset rules, placing them into “clustered” collections of data with shared traits or odd features. For example, this can be used when analyzing attack data that a system is not already trained for. These clusters can help determine how an attack happened, as well as, what was exploited and exposed.

Recommended courses of action – elevate the proactive measures of an ML security system. These are advisories based around behavior patterns and former decisions, providing naturally suggested courses of action. It is important to restate here that this is not intelligent decision making via true autonomous AI. Rather, it’s an adaptive conclusion framework that can reach through pre-existing data points to conclude logical relationships. Responses to threats and mitigating risks can be assisted immensely by this type of tool.

Possibility synthesis – allows for the synthesizing of brand-new possibilities based on lessons from previous data and new unfamiliar datasets. This is a bit different from recommendations, as it is concentrating more on the chances that an action or the state of a system falls in line with similar past situations. For example, this synthesis can be used for a preemptive probing of weak points in an organization’s systems.

Predictive forecasting – is the most forward-thinking of the ML component processes. This benefit is achieved by predicting potential outcomes by evaluating existing datasets. This can be used primarily for building threat models, outlining fraud prevention, data breach protection, and is a staple of many predictive endpoint solutions.