Machine learning and cybersecurity: AI as a digital bodyguard


Most business now takes place online, and when working in-house, even small businesses rely on complex networks to carry out daily projects. However, as technology advances and convenience grows, so do the risks of losing data to malicious attacks.

Cyberattacks put clients’ information at risk, and the reputation of the business owner is also under threat. Without an efficient, responsive, and highly specialized cybersecurity plan, the entire operation is on the line.

There is an unspoken agreement between the business and clients that the information they provide is safe and to be used only by the business for the business. Should it fall into the wrong hands from a cyberattack, the business would break that contract with the clients, thus entailing further issues. 

Investing in cybersecurity is the best way to protect the business, the network, and the clients. However, as technology marches forward, older cybersecurity standards soon become outdated.

That’s where machine learning (ML) and artificial intelligence (AI) can fill in the gaps. But how can the average business owner use AI and machine learning to help tighten up their network and data security? This article will closely examine what machine learning offers in practice and how it could apply to cybersecurity planning.

What is machine learning? 

ML is regarded as a subdivision of AI. It refers specifically to computer systems that can learn and adapt using algorithms without human intervention or instructions. 

ML allows computer systems to make predictions based on new data. ML and AI are synonymous terms, as they are often used to explore the same phenomenon – that is, autonomous work that doesn’t need human control. 

However, AI is a technology created to simulate human intelligence, hence the name, while ML refers only to systems that use current information to make predictions. 

Hence, ML is a subset of AI. 

There are currently three main types of machine learning that apply: supervised learning, unsupervised learning, and reinforcement learning. 

Supervised learning refers to computer systems trained and adjusted to perform certain tasks when given new data. When it comes to cybersecurity, supervised learning is a great way to use neutral or malicious data samples to teach the system to predict whether other new samples are indeed harmful. 

Unsupervised learning refers to training a computer system on unlabeled data, making the system find various structures, similarities, and patterns. The goal, in this case, is to make systems classify data without needing human guidance or instructions. When it comes to cybersecurity, unsupervised learning is a great way to find new attack patterns and detect anomalies in pools of data. 

Finally, reinforcement learning refers to a system learning through trial and error. The system receives rewards when it succeeds and aims to make as many correct decisions as possible. Reinforcement learning is used in cybersecurity to enable systems to detect wide ranges of anomalies and cyberattacks. 

All three learning staples support cybersecurity plans the world over – though there are still some challenges to expect.

So, can ML help to support cybersecurity demands? What do businesses need to do to make room for AI?

How machine learning can support cybersecurity 

For businesses, ML can aid cybersecurity through many different avenues.

Question generation

One of the most positive aspects of ML is that it can help humans learn, too. A business owner’s duty is to ensure that systems are always secure, to ensure their own privacy, that of the business, and that of customers and clients. 

ML will help them ask the right questions. It will help them decide if they have the right equipment and data to deal with a cyberattack, whether the team can use the data effectively, and if they have systems in place to detect and/or respond to cyberattacks. 

Machines with the capability to learn can assess and audit existing security standards and measure them against industry or recommended standards. Therefore, they help business owners prioritize the questions that need asking, rather than risk wasting their time on arbitrary changes.

Of course, hiring an auditing expert with an Online Masters in Cybersecurity from a reputable university, such as St. Bonaventure University, is a great first step before deploying AI. St. Bonaventure’s programs ensure graduates can help puzzled business owners determine which areas of their cybersecurity plan need the most attention.

Risk scoring

ML can generate risk scores that apply to different sections of networks as part of the security auditing process. This scoring is frequently based around precedents and standards set by the wider cybersecurity industry.

Intelligent machines can grade areas of security as low or high based on current measures in place. For example, AI can grade the entropy of passwords a business uses, whether they have enough firewall protection, and if there are any urgent software patches they might have missed.

Risk scoring helps the average business owner to find weaker areas in their cybersecurity setup that need the most attention, rather than spending time making guesses.

Data set scanning

Big data is frequently difficult to handle, analyze, and secure manually – even with some of the most efficient tools on the market. However, systems using ML are more than capable of processing, analyzing, and informing on large amounts of data at incredible speed. Such support not only saves personnel time and effort, but also ensures that businesses carefully sift their data to ensure it is as secure as possible without escalating expense.

Threat detection 

One of the most popular and enduring uses of ML in cybersecurity is for autonomous threat detection. When the threat of a cyberattack emerges, responding quickly is vital. Threats move fast to the point where, by the time a normal human detects a problem, it will likely be too late – and a lot of damage might already be underway.

However, systems adopting ML can detect malicious data and potential attacks at the point of emergence, meaning there is no need for human support to manually eradicate such problems.

This asset is especially helpful considering that cyber threats are always evolving. Autonomous threat detection further removes the need for human personnel to keep up to speed with the latest potential issues. That said, learning about the latest hacking threats and techniques is always beneficial, regardless of whether a business is using ML to support security.

Lightning-fast threat detection enables business owners and security specialists to take their hands off the controls to some extent. This asset means they can better apply their knowledge and time to developing security plans and protecting customer information elsewhere.

Threat detection using ML works similarly to firewall support on a standard PC, however, with ML, there is a more efficient, tailored security system. Given that time is in short supply in all walks of business operation, saving even a little by delegating security guarding to AI is hugely welcome.

Threat classification

ML isn’t only adept at detecting threats but also at classifying them – meaning that businesses will always know the risks involved with specific security choices, and which threats are likely to do the most harm. Naturally, all potential threats to a business are unwanted. However, threat classifications can help security experts make the right decisions regarding quarantining intruding threats and patching up their software.

ML doesn’t just classify immediate threats, either. Using precedents based on industry expertise and those set by the security team, a business can request AI to scan and monitor all data their networks handle. If a potential threat lurks within data pools, AI will spot it and classify it without the business needing to take any manual action.

ML will classify all data based on the accuracy of the threat (i.e., whether true or false) and on the output, which is either positive or negative. If something receives a positive classification, then the data scanned was malicious.

ML threat classification removes the risk of false positives breaking through the net. False positives occur when security systems (or personnel) spot a potential threat only to find they pose no risks whatsoever. These cases waste time and effort. Therefore, using ML, businesses can train AI to understand what a genuine threat looks like and to classify problems beyond all reasonable doubt.

Vulnerability management 

One of the most time-consuming tasks for cybersecurity teams is dealing with vulnerability management. This is finding areas within networks that have previously proven hazardous, and manually auditing network strength to ensure there are no gaps or entry points for malicious code.

When deployed efficiently, ML can help scan the entirety of complex networks and datasets in real time. Rather than scanning networks manually and setting scheduled dates and times to measure and upgrade their systems, experts can simply request AI oversees the whole operation and report back if there is a problem.

Sifting through every connection and every piece of hardware in a network takes incredible time and aptitude. When trained properly, machines can use preceding data and user parameters to report back when something appears out of sync or out of the ordinary.

Of course, there may be some challenges when using ML in this way. Tweaking systems so they do not overreact and present false positives can often require some trial and error. That said, AI and ML are evolving so that people feel more confident than ever leaving big decisions up to their machines.

Repetitive task reduction

Repetitive, time-consuming tasks are, regrettably, rife across cybersecurity. From malware analysis to vulnerability management, security management is vital yet painstaking. 

While human engineers and security experts are more than capable of spotting errors and making fixes where appropriate, time and effort could be put to better use elsewhere. This is a common point of contention for managers and business owners alike who look to harness productivity and efficiency.

ML is customizable to a business’ specific needs. Users can easily request program automation to learn about the checks security teams undertake, what counts as a positive or negative result, and which actions are worth taking.

Repetitive task reduction with ML benefits everyone working in security planning. For business owners, it is money saved on having to hire specialists purely to manage mundane tasks. Administrators and in-house staff employed to tackle such problems use AI to free up their time to handle more pressing concerns. Ultimately, it means businesses can take on more projects and potentially more clients and revenue too.

For those who would normally undertake such tasks, it is incredibly freeing. Security experts and network engineers have more opportunities to complete projects and make headway elsewhere at work, reducing frustration and lowering tedium.

Customers, clients, and the business’ reputation also benefit. Reducing repetitive tasks typically undertaken by human personnel also reduces the risk of missed issues and human errors putting data at risk.

The challenges in using machine learning in cybersecurity

As beneficial ML and cybersecurity are, there will always be challenges for engineers and business owners when first managing such technology.

One of the biggest concerns, for example, is that the data a machine is learning from is incomplete, not ‘clean’, or not complete enough to perform tasks effectively. For example, a machine learning from an outdated data pool cannot be reliable in the face of ever-evolving security threats.

Therefore, people handling data and managing smart machines must audit their information carefully before deploying ML across a network. This issue is painstaking and will need experts to regularly run checks and measurements. While the idea of a machine purely taking hold of all manual security checks is a dream come true, there will always be some human intervention.

For many business owners, this is not always a bad thing. Some business owners may be hesitant to take up ML to completely automate their cybersecurity planning and threat detection purely because they don’t trust leaving such important checks to chance. 

There is also the further risk of machines making decisions based on biases and failing to understand certain contexts. For example, a machine that is learning from a specific dataset might assume that one group of customers is always likely to pose security threats or potentially lock out specific users and customers without intent.

These are challenges that are easily manageable through human intervention. This negates the idea that businesses should rely on machines to handle cybersecurity period, but without occasional auditing, there will always be a risk of AI making the wrong decisions.

Beyond cybersecurity, machines are already making mistakes during testing. Generative AI (such as machines used to create text and graphics) has evolved hugely after making waves of mistakes during its initial deployment. Chatbots have required intensive testing after appearing abusive or discriminatory too.

What does the future look like for machine learning and cybersecurity?

Ultimately, nothing is preventing ML from revolutionizing the way businesses secure and audit networks and data in the decades to come. This is an exciting time where many AI and machine learning tools are at business’ disposal. AI is affecting niche industries to the extent where businesses must pivot to keep ahead of machines’ efficiency and capabilities.

However, not everything can be currently handed over to automation. Like cyber threats, automation and machines with learning capabilities are always evolving. For some years ahead, they will still need support to understand contexts and avoid making biased, potentially harmful decisions.

Cybersecurity is never going to lose importance or prominence. Given that most big businesses rely on networking and data storage, it is highly likely that most will adopt ML in the long run to cut costs, boost efficiency, and improve output quality. 

Therefore, businesses should keep an open mind about ML and dig deeper into what systems could do for them and their security demands in the years to come. Already, these tools can spot threats faster and protect large pools of data without having to meticulously work through every single file.

Businesses shouldn’t hand the keys over to AI just yet but those responsible for developing malicious code and security threats will also be working on more advanced ways to intercept preventative measures. AI and ML are invaluable tools that shouldn’t be overlooked.

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