So, does that mean IT teams will become redundant soon, as AI-based security tools can do it all? Simply put, no. But for a more in-depth answer, we’ll need to first understand what machine learning in cybersecurity is and what this technology holds for businesses in the future.

What is machine learning?

Machine learning refers to the ability of algorithms to learn patterns from existing data and use this knowledge to predict outcomes on new, previously unknown data without explicitly being programmed. The more information you feed to the machine learning engine, the more data it can analyze and, consequently, become more accurate.

But what does it mean to say that a machine is learning from the existing data? While traditional programming performs simple and predictable tasks by strictly following detailed instructions, machine learning allows the computer to teach itself through experience. In other words, it mimics human behavior in how to solve problems.

However, the fact that machine learning can improve itself isn’t the only reason why it’s so easy to find its models in the online wilderness. The sheer amount of information that businesses in different industries currently have to manage has become too vast for humans to tackle alone. As a result, companies rely on machine learning to process that data and quickly generate actionable insights.

For instance, an ML technique called a decision tree solves classification dilemmas and uses certain conditions or rules in the decision-making process. This particular technique is widely used in fintech (for loan approval and credit scoring) and marketing.

Machine learning solutions are also helpful for businesses in harvesting, organizing, and analyzing large volumes of customer data. This can include purchasing history or individual customer’s typical behavior, such as online browsing habits. With such analyzed data, companies can then recommend relevant products tailored to their customers’ preferences. Think Netflix: With an ML-driven model, it examines its users’ histories on the platform to compile appropriate content recommendations for them to choose from. This increases the time users spend watching Netflix content and their overall satisfaction. Similarly, ML models pick up information relevant to the unique user on the Facebook feed and even moderate content on Instagram.

In addition, in most customer support self-service tools, users usually interact with a machine rather than a fellow human being. Such chatbots can answer basic questions and guide a person to relevant content on the website.

Lastly, even in the medical field, machine learning plays a huge role. These models can be trained to examine medical images or other information and then search for illness characteristics.

Four types of machine learning

Machine learning traditionally has four broad subcategories that are defined by how the machine learns:

• Supervised machine learning models rely heavily on “teachers”, meaning models that are trained with labeled data sets, which allow them to learn and become more accurate over time. For instance, if you want to teach the algorithm to identify cats, you’ll have to feed it with pictures of cats and other things, all labeled by humans.

• Unsupervised machine learning looks for patterns and common elements in data. In turn, such machine learning can find similarities and trends that humans aren’t explicitly looking for.

• Semi-supervised machine learning falls somewhere between supervised and unsupervised learning. In this case, the model is trained on a small amount of labeled data and lots of unlabeled data. Such a way of learning is beneficial when there’s a lot of unlabeled data, and it’s too difficult (or expensive) to label it all.

• Reinforcement machine learning is where an algorithm learns new tasks by interacting with a dynamic environment. Here, it is rewarded for correct actions, which it strives to maximize, and punished for incorrect ones. Such machine learning is widely used in cybersecurity, as it enables a broader range of cyber attack detection.

Machine learning use cases in cybersecurity

As cybersecurity is a truly fast-paced environment where threats, technologies, and regulations constantly evolve, it’s the agility of machine learning that comes in handy.

ML-powered models can process massive amounts of data and, therefore, rapidly detect critical incidents. This means that machine learning enables organizations to detect various types of threats like malware, policy violations, or insider threats by constantly monitoring the network for anomalies. It is so because ML-driven algorithms learn to identify, for instance, new malicious files or activity based on the attributes and behaviors of previously detected malware.

In addition, using machine learning proves to be a good method for filtering your company’s inbox from unsolicited, unwanted, and virus-infected spam emails, which may contain pernicious attachments such as malware or ransomware. For instance, the machine learning model used by Gmail not only sifts through spam but also generates new rules based on what it has learned in the past. ML methods, coupled with natural language processing techniques, can also detect phishing domains by picking on phishing domain characteristics and features that distinguish legitimate domains.

Last but not least, machine learning can significantly support online fraud detection and prevention. By using ML algorithms, companies can identify suspicious activities in transactional data. These algorithms are trained to recognize normal payment processes and flag suspicious ones. Also, ML-driven engines can be trained to spot when cybercriminals change their tactics as they automatically will retrain themselves to recognize a new fraud pattern.

These examples illustrate just a few use cases of machine learning in cybersecurity. But there are many others, such as vulnerability management, that can greatly impact business cybersecurity.

So, is it AI, machine learning, or deep learning?

Oftentimes, these terms – artificial intelligence, machine learning, and deep learning (DP) – are used interchangeably. We already defined machine learning, so now, let’s see how it relates to artificial intelligence and deep learning.

Artificial intelligence, in the broadest sense, is a set of technologies that enable computers to perform various advanced tasks in a way similar to how humans solve problems. This makes machine learning a subfield of artificial intelligence.

In turn, deep learning is a subset of machine learning. It mimics the structure and functions of the human brain. Such systems use artificial neural networks that function like neurons in the brain. These neurons, also referred to as nodes, are used in chatbots or autonomous vehicles.

Difference between machine learning, artificial intelligence, deep learning, and cybersecurity

Even though machine learning brings some challenges when applied to cybersecurity (for instance, the difficulty in collecting large amounts of certain malware samples for the ML machine to learn from), it remains the most common approach and term used to describe AI applications in this industry.

In cases where shallow (or traditional machine learning) falls short, deep learning should be used. For example, when dealing with highly complex data such as images and unstructured text or when temporal dependencies have to be taken into account.

The future of machine learning in cybersecurity

In the current AI tool-filled climate, it’s easy to see how this technology can become better at specific tasks than we humans are. Luckily (or not), machine learning is not a panacea to all things cybersecurity. However, it provides and will continue to provide a great deal of support to cybersecurity or IT teams by reducing the load off of their shoulders.

Since many devices (like phones and laptops) connect to the company’s networks daily, it is almost impossible for IT teams to monitor every single gadget. With AI-powered device profiling, you can improve the fingerprinting of endpoint devices and better understand the type and quantity of endpoints connecting to your network. This will help you create effective segmentation rules and stop unwanted devices (potentially including bad actors) from connecting.

Also, employing machine learning can improve your cybersecurity game by helping your IT team develop policy recommendations for security devices such as firewalls. In this case, machine learning learns what devices are connected to the network and what constitutes normal device behavior. In turn, ML-powered systems can make specific suggestions automatically — instead of your team manually navigating different conflicting access control lists for each device and network segment.

So, embracing more ML-driven cybersecurity practices into your daily company’s processes seems vital if you want to improve your cybersecurity in the future. With more devices and threats coming online daily, the human resources available to tackle them are becoming scarce. In such an environment, machine learning can step in by helping sort out various complicated cybersecurity situations and scenarios at scale while maintaining constant surveillance 24/7.

How does NordPass use machine learning?

Machine learning offers a wide range of applications for businesses, from applying it to cybersecurity to simply enhancing customer satisfaction. With artificial intelligence still making headlines, we’ll likely see even more use cases in the future that will benefit the company’s cybersecurity as well.

NordPass is one of the companies that use machine learning. We do so to offer more accuracy and convenience for our customers. Our autofill engine relies heavily on machine learning to accurately categorize the field that it needs to fill in on a website or app – no matter if it is a sign-up, credit card, or personal information form. Remember those artificial neural networks? It has been trained using exactly those!

If you’re interested in improving your employees’ online experience and enhancing overall company security, explore what enterprise password management can offer for your company.

When we discuss data security, we mainly talk about security practices within an organizational setting. The concept covers every aspect of information security, such as hardware, software, access controls, and organizational security policies. A sound and thoughtful data security strategy can make a difference in a business environment because it helps organizations protect one of their most valuable assets — data — against cyberattacks. 

Why is enterprise data security important?

In the digital age, data reigns supreme. These days, all businesses deal with data in one way or another. Whether it’s a financial institution handling sensitive customer data or an individual operation collecting the contact information of its clientele, data is a significant part of all enterprises, regardless of their size or industry. Data informs decision-making, improves efficiency, enables better customer service, and plays a major role in marketing.

With growing public awareness about the importance of data security and more data-related laws and regulations coming into play, companies face challenges in creating secure infrastructures and processes to handle enormous amounts of data.

Data security management strategies are simple to establish – for instance, employee digital safety training or centralized password policies. Setting up an enterprise password manager can be a massive upgrade to an organization’s security practices. Although high-end software can greatly improve an organization’s security strategy, employee awareness is often what makes or breaks its effectiveness.

Failure to establish a secure perimeter frequently results in a data breach, leading to substantial regulatory fines and reputational damage. According to IBM’s Cost of Data Breach Report 2023, the global average data breach cost is estimated at $4.45 million. It’s not hard to imagine that a data breach could spell the end of a company.

As data breaches and cybercrime continue to rise and become more sophisticated, companies of all sizes and industries look for ways to ensure the security of their data. And the first step in doing so is understanding the threats you’re facing.

What threats to data security do companies face?

Cyber threats related to data security come in various shapes. Here are some of the most common data security risks that every organization has to deal with.

• Phishing attacks

Phishing attacks are designed to acquire sensitive information from unsuspecting users. Hackers achieve their goal by crafting email messages that appear to be from a reputable source. In those messages, you are usually urged to download a malicious attachment or click on a dodgy link. If you follow through, the attackers can access your device and get their hands on your sensitive data.

• Accidental data exposure

Not all data breaches are caused by cyberattacks. Sometimes, they’re byproducts of human error or lack of awareness. In day-to-day office life, employees will inevitably share data and exchange access credentials. Unfortunately, security might not be at the top of their priority list, and accidents can happen: data can end up on an unsecured server, and passwords can be stored in a publicly accessible sheet. That’s why cybersecurity training sessions are critical. Once employees grasp what’s at stake and what to pay attention to, the risk of accidental data exposure can be drastically minimized.

• Malware

Malware is usually spread via email. In most instances, hackers will launch a phishing campaign to trick users into downloading and installing a piece of malicious software. Once malware is on a corporate network, hackers can do pretty much anything, from tracking network activity to downloading enormous amounts of data without authorization.

• Ransomware

Ransomware is a type of malware that is designed to encrypt data on the affected machine. If a ransomware attack is successful, bad actors will demand a ransom in return for decryption services.

• Insider threats

Insider threats might be the hardest to anticipate. As you can guess, insider threats are employees who intentionally harm an organization’s security perimeter. They might share sensitive data such as passwords with dubious third parties or steal business data and sell it on the black market.

What types of data security are we talking about here?

As already discussed, data security protection strategies comprise many different tools and practices. Typically, the most effective way to ensure data security is to use a combination of security practices to limit the potential surface area of an attack.

Data encryption

Data encryption is one of the easiest ways to ensure the security of sensitive information. Fancy terminology aside, data encryption converts readable data into an unreadable encoded format. Think of it this way: even if a hacker were to get their hands on the encrypted data in your servers, they couldn’t do anything unless they managed to decrypt it. Fortunately, contemporary encryption is unbelievably hard to crack without a decryption key.

Data erasure

Data, as with anything in life, can become irrelevant. Data can clog your servers like clutter in your attic. Security-wise, irrelevant data is rarely considered a priority – and sometimes, it’s best just to get rid of it for good. Data erasure is an effective data management and security method because it shrinks the potential attack surface and liability in case of a data breach.

Data masking

Data masking is a data security technique during which a data set is duplicated, but its sensitive data is obfuscated. The benign copy is usually used for testing and training for cybersecurity purposes. Masked data is useless for a hacker because it is essentially incoherent unless the hacker knows how that data has been obfuscated.

Data resiliency

Data backups are among the easiest steps an organization can take to mitigate the potential dangers of data loss in a cyber event. Backups ensure that even if data is compromised or stolen, it can be recovered to its previous state rather than entirely disappear.

Data security vs. data privacy: What’s the difference?

Today, the terms “data security” and “data privacy” are used a lot. At times, they might seem interchangeable. While in a sense that can be true, the two terms are technically distinct concepts.

Data security is a broad term that encompasses data privacy. However, when we talk about data security, we mainly refer to cybersecurity practices that are aimed at protecting data from unauthorized access or corruption.

Data privacy, on the other hand, is a concept that aims to ensure that the way businesses collect, store, and use data is compliant with legal regulations.

How about data security vs. cybersecurity?

Similarly, you might have some questions about the difference between the terms “data security” and “cybersecurity.” The difference here is the scope of what each security type covers.

Broadly speaking, cybersecurity concerns things on the macro – protecting servers and networks from cyber attacks as the first line of defense. Data security, on the other hand, protects the micro – the actual data stored within the networks. If cybersecurity measures fail, data security aims to keep valuable information unaffected by means of encryption and other measures we’ve discussed.

How does data security compliance work?

Today, most countries have laws and regulations that govern the way organizations should collect, store, and use data. Regulatory compliance can be a challenge for companies of all sizes and industries. Still, they’re vital in ensuring that your data will not be abused and remain secure at all times. Here are some of the most important regulations that relate to data security.

General Data Protection Regulation (GDPR)

The GDPR is the European Union’s primary data protection and privacy legislation. Passed in 2016 and implemented in 2018, the GDPR ensures that organizations handle consumer data responsibly and securely. The GDPR was one of the first legislative efforts requiring companies to ask for user consent to collect their data.

The GDPR is an expanded legislation that can impose penalties going up to €20 million or 4% of a company’s annual global turnover. Thus, choosing reliable tools that help work toward compliance with GDPR, such as NordPass, to manage and secure customer data is crucial for ensuring overall enterprise security.

California Consumer Privacy Act (CCPA)

The CCPA went into effect on January 1, 2020. It provides consumers in California with additional rights and protections regarding how businesses use their personal information. The CCPA is very similar to the GDPR and imposes many of the same obligations on businesses that the GDPR does, except for the implementation of robust security measures to protect customers’ personal information from unauthorized access, destruction, modification, or disclosure.

Health Insurance Portability and Accountability Act (HIPAA)

HIPAA is the United States data protection and security legislation that regulates electronically protected health information (ePHI). It is aimed mainly at healthcare providers and partnering institutions that deal with such data. HIPAA lays out requirements for the security of ePHI, which involves specific physical, technological, and administrative safeguards. To stay compliant with HIPAA regulations, medical companies should implement some security measures: safe traffic encryption with a VPN, secure messaging apps, encrypted email services, and reliable business password management.

Sarbanes-Oxley (SOX) Act

The SOX Act was passed in 2002 to protect shareholders and the general public from fraudulent corporate practices and improve corporate disclosures’ accuracy. Even though the act does not specify how an organization should store records, it does define which documents should be stored and for how long. The SOX Act primarily applies to public corporations.

Payment Card Industry Data Security Standard (PCI DSS)

The PCI DSS is a set of regulations geared toward organizations that process, store, and transmit credit card data. It lays out requirements to ensure that all credit card-related data is handled securely.

International Standards Organization (ISO) 27001

ISO/IEC 27001 is an Information security management standard that outlines how business entities should manage risk related to cybersecurity threats. Defined within the ISO 27001 standard are data security guidelines and requirements intended to protect an organization’s data assets from unauthorized access or loss. The ISO/IEC 27001 is not a piece of legislation in the sense that the GDPR is. It is rather a standard that helps businesses comply with regulations such as the GDPR cost-effectively.

Data security best practices

Data security is a complex concept that includes a variety of practices and processes working together like a well-oiled machine. The data security strategy within the organization depends on its size, IT infrastructure, resources, and several other variables. However, a few data security solutions can be applied in any organization.

Access management and controls

Access management and controls help organizations set rules for who has access to networks, systems, files, and various accounts within the digital ecosystem. Proper access management and control integration can significantly shrink the potential attack surface area.

Employee education

One of the leading causes of data breaches is human error. The obvious counter is education. For an organization that wishes to be successful security-wise, a team that is aware of the risks that might be faced and how they would be handled is crucial.

Password management

Weak, reused, or old passwords also play a significant role in data breaches. It’s understandable because today, an average person needs about 100 passwords – leading to reliance on the same easy-to-remember passwords for multiple accounts. Ensuring that each one is unique and complex is impossible without help from technology. Password managers are tools designed to help individuals and organizations create strong passwords, securely store them, and access them whenever there’s a need. Today’s business password managers improve organizational security as a whole and spur productivity with handy features such as autofill and autosave.

Cloud data security

Many organizations rely on cloud technologies to carry out daily operations. While cloud technology offers significant benefits, it simultaneously opens up additional security risks. Misconfigured cloud technology services can lead to data leaks and breaches. Therefore, you must take action to ensure that any cloud apps you use are properly configured to limit potential risks and prepare a robust cloud security strategy for your company.

Data encryption

As discussed earlier, data encryption is a way to secure information within databases and servers by making it unreadable without the decryption key. Encryption is essential to overall data security and should always be employed.

Data loss prevention and backups

These days, most business-related information is stored in databases. The data they contain may be customer records, credit card details, or internal company documents. Backing up data protects the organization from accidental data loss or corruption. Regularly scheduled backups can also help in the case of a ransomware attack because the backups could be used to restore the affected data.

Incident response and disaster recovery plans

An incident response plan is an organization’s systemic approach to managing a security-related event. Usually, such plans are purpose-built to address malware attacks, data breaches, unauthorized network intrusions, and other cybersecurity-related events. With a comprehensive incident response plan, the organization has a clear pathway to mitigating a cyber attack in a swift and coordinated manner.

A disaster recovery plan (DRP) is focused on broader business continuity and recovery efforts in the face of major disasters: natural catastrophes, power outages, or system failures. DRP encompasses a more extensive range of scenarios than IRP, often including data backup and redundancy, a proactive cybersecurity approach, alternate work locations, and comprehensive recovery procedures.

Multi-factor authentication (MFA)

Multi-factor authentication is a method that requires two or more authentication factors such as additional passwords, PINs, passphrases, tokens, geographical locations, or biometric data. In the business world, multi-factor authentication provides the highest level of security required by GDPR or HIPAA regulations. MFA works like a safety net and can save an organization a lot of trouble and money if login credentials to corporate accounts are breached. In most cases, cyber crooks are not able to obtain extra authentication factors.

It seems only reasonable to ask for extra proof of identity online. However, many individuals and companies rely solely on one layer of security. The reason may be a common misconception that MFA is difficult to adopt, especially in a corporate environment where it has to be incorporated into existing IT infrastructure. In reality, advanced password management tools like NordPass can smooth the whole process and make the adoption of multi-layered security easy like a piece of cake.

Email security

For many, emails are their main work tool. No wonder so many corporate secrets get into the wrong hands through carefully crafted phishing emails. Cybercrooks bend over backward to make their fraudulent attempts look legit. Luckily, some measures enhance company email security.

First of all, well-trained employees who are aware of various types of cybercrime are less likely to risk the company’s safety by clicking random links or acting in haste. Second, corporate-wide solutions like multi-factor authentication, encrypted VPN, or email masking create further layers of security, contributing to the overall safety of an organization. Finally, the random and complex passwords stored in an encrypted vault are the solid foundation of email security and should never be underestimated.

How NordPass Business can help

As mentioned, weak, old, or reused passwords are often the cause of a data breach. Password fatigue is a major factor that leads people to use weak and easy-to-remember passwords across multiple accounts. However, password fatigue can be mitigated with the help of a corporate password manager.

NordPass Business is purpose-built to improve organizational security and take a load off employees when creating and remembering passwords. Keep all your business passwords, credit cards, and other sensitive information in a single encrypted vault and securely access it whenever you need. Thanks to company-wide settings present in NordPass Business, you can set password policies across your organization. And with the help of the Admin Panel, access management is easier than ever.

NordPass Business is ISO/IEC 27001:2017 certified and has received the SOC 2 Type 2 attestation, making it a critical security tool for companies striving to meet GDPR and HIPAA compliance standards.

Try NordPass Business with the 14-day free trial and enjoy improved productivity and security within your organization.