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Virtual private cloud vs. private cloud: What’s the difference?

Summary: Virtual private clouds and private clouds differ in cost, flexibility, and security, helping you choose the best option for your organization.

Private or public? Virtual or local? Cloud deployments come in many varieties. Choosing the right model is critical to performance, ease of use, cost, and security.

This article discusses the two main private cloud solutions: virtual private cloud and private cloud models. Each deployment type has strengths and potential drawbacks. Choosing the right type influences security, cost, and performance. It’s an important decision.

What are the two types of private cloud, and which one should you choose? This article will explain everything you need to know.

What is a virtual private cloud?

A virtual private cloud (VPC) is a virtualized multi-tenant cloud deployment hosted on public cloud infrastructure.

A cloud provider sells public cloud space, and users apply logical segmentation to create a virtual network. This separates the VPC from other resources without needing extra hardware or separate server space.

After that, the VPC functions like a private cloud domain. Users can install applications, create data storage containers, and manage cloud computing as needed.

Virtual private cloud users determine internal routing via IP address subnetting and network access control lists (NACLs). Network gateways enable secure connections from external resources. Users can also connect many VPCs via VPC peering.

Unlike private clouds, VPCs require a direct connection to the public cloud. This potentially makes it accessible to other public cloud users. However, subnetting IP addresses reduces this access risk.

Under the VPC model, users and cloud vendors share responsibility for security. Cloud vendors operate and secure the underlying infrastructure. VPC users must regulate access to resources via tools like security groups, access control lists, subnets, firewalls, and identity and access management (IAM).

Advantages of virtual private cloud architecture include:

Flexibility. VPCs can scale rapidly as companies grow or contract.
Cost-effectiveness. VPCs are cheap to set up and deploy because the cloud provider handles infrastructure.
Low maintenance overheads. Companies can run cloud deployments without large IT teams.
Sophisticated internal security. VPC users can segment deployments. It’s easy to separate financial data, sales platforms, and DevOps environments.

Virtual private clouds also have negative aspects. Most importantly, VPCs can experience outages and downtime. While VPCs are flexible, users of private cloud systems may have more customization options.

Security is another issue. VPC users must connect to gateways before accessing cloud resources, and this connection can raise security risks. Reliable access controls and multi-factor authentication (MFA) mitigate these risks. Virtual private network (VPN) protection also helps secure the VPC perimeter.

Note: Many users confuse VPC and VPN technology. The key difference is that VPNs encrypt data flows over the public internet. VPCs are virtualized cloud deployments. They complement each other, enhancing overall security.

What is a private cloud?

A private cloud is a standalone cloud solution with a single tenant. Under the private cloud model, users own and manage their cloud computing infrastructure, including data storage and networking solutions. Control is centralized, and users take responsibility for cloud security.

Typically, private clouds reside in data centers managed by the user organization. On-site hardware creates a physical network perimeter. Endpoints on the private cloud perimeter enable access control. Managers can filter inbound and outbound traffic, ensuring a high level of security.

Private clouds have many benefits:

Support for legacy applications. Ensuring access to legacy applications that do not function well on the public cloud (if at all).
Enhanced integration management. Managing integrations to ensure operability and maintain security.
Granular visibility of network access and user behavior.
Resource segregation and control. The ability to segregate resources and have full control over the underlying infrastructure.
Robust privacy protection for sensitive information via tight access controls.
Complete customization. Users have total freedom to design private cloud architecture.

There are also downsides. Private clouds are complex and expensive to implement and maintain. They scale poorly compared with VPCs. Users require extensive expertise and may see IT costs spiral.

Differences between virtual private clouds and private clouds

VPCs

VPCs tend to be more affordable. Users can also purchase the capacity needed, keeping costs as low as possible.

Availability

Private cloud

Private clouds are generally very reliable and deliver high levels of availability.

VPCs

VPCs rely on cloud providers to keep systems operational and available. Users can leverage redundancy to hedge against downtime or cyberattacks.

Security

Private cloud

The private cloud model is extremely secure. Organizations can limit external access across the network perimeter and deploy internal segmentation to regulate lateral movement.

VPCs

VPCs are more secure than public cloud solutions but less secure than private clouds. Network access controls and segmentation protect critical data. However, unsecured access points can expose data to the public cloud.

Virtual private cloud vs. private cloud vs. hybrid cloud

Before we discuss how to choose cloud solutions, we need to talk about another issue: hybrid cloud deployments.

Hybrid cloud solutions mix different technologies. The most common type combines public clouds and private cloud services.

This type of hybrid cloud suits businesses that need to cut costs, host large amounts of non-critical data, or regularly experience traffic spikes. However, hybrid cloud security is a critical factor to consider, as securing data and workloads across diverse environments requires careful planning.

For instance, space on public clouds is usually cheaper than private alternatives. You might secure confidential data in VPC containers while keeping low-risk assets public.

Another form of hybrid cloud combines private clouds and VPCs. In this scenario, users might reserve sensitive data in a private cloud service. VPCs can handle other workloads. This suits remote workforces and reduces cloud computing costs.

Choosing the right cloud for your business

Let’s return to the main question: should you choose a private cloud or a VPC-based solution? Here are some factors that influence the decision to choose private cloud vs public cloud technologies:

Complete data protection

In the comparison between private cloud vs. public cloud security, VPCs, and private clouds easily beat shared public cloud solutions.

Private clouds are slightly more secure than VPCs, as users have more control over how and where their data is stored. This makes them a better choice for organizations like healthcare bodies or financial data processors.

In general, organizations in highly regulated sectors should consider a private cloud model. They might also segregate sensitive data within private clouds and use public or VPC solutions for other assets.

Simplicity and ease of use

Virtual private cloud solutions suit smaller companies without dedicated cloud maintenance teams. Private clouds require extensive maintenance and are relatively hard to scale.

A VPC solution lets small businesses benefit from cloud computing, secure data, and adapt their deployment as their needs change. Setting up a VPC is also much easier than a private cloud.

Keeping costs low

Think about the cost of your cloud hosting solution. Private clouds have high upfront costs, while VPCs are very affordable. They lock down confidential data or workloads without needing huge capital investment.

Private clouds may have long-term advantages as the operational costs fall over time, especially for larger organizations.

Flexibility

VPCs are more flexible than private clouds. You can spin up virtual servers and storage capacity as needed. For example, you may need a temporary DevOps environment to test code before using it elsewhere.

VPCs can also reside closer to customer communities. If you serve clients on other continents, regional VPCs cut latency and may aid compliance by separating customer data sets.

Private clouds are easier to customize but less flexible. Scaling is complex, making VPCs a better option if your computing or storage needs are uncertain.

Availability

Companies using the cloud to host websites or customer data need high availability. Downtime, which disables web services and workloads, costs money.

VPCs solve the availability issue via redundancy. You can use peering or availability zones to keep systems running, even if part of your deployment fails.

Private clouds are generally reliable but present a single point of failure. Using multiple virtual servers may be a safer option.

Performance

Properly designed private cloud systems perform well because they dedicate resources to essential tasks such as processing AI data sets or video rendering.

VPCs share space with cloud provider customers, leading to variable latencies. Virtual private cloud data centers could also be distant, causing speed issues.

Virtual vs. private cloud: Securing access to both

Whether you choose a virtual private cloud or private cloud solution, security is a top priority. VPC best practices like encrypting data and applying security groups help but are not comprehensive solutions.

Secure cloud access controls are critical to minimize data breach risks. Malicious actors pounce on vulnerable devices and endpoints. There is no room for complacency, no matter what assurances your cloud provider offers.

NordLayer is compatible with the most popular VPC solutions. It can enhance your security by protecting who can access the data stored in the cloud. To secure your VPC, consider these steps:

Secure Remote Access: Use NordLayer’s Site-to-Site VPN to create an encrypted tunnel, allowing safe access to the VPC without exposing data to public internet risks.
Prevent unauthorized access: NordLayer’s Cloud Firewall helps you control who can access the VPC. You can limit access to authorized users, reduce the chance of data leaks, and use extra security layers like SSO and MFA to double-check identities before granting access.
Device Posture Security: NordLayer’s Device Posture Security ensures that only approved devices that meet company security standards can connect to the VPC. It helps prevent compromised or non-compliant devices from accessing sensitive data.

To find out more, contact the NordLayer sales team and discuss your cloud security needs.

If you serve security-conscious clients, why not take a look at our MSP partner program as well? As a cybersecurity partner, you can earn revenue and secure your cloud assets with support from our experts.

About NordLayer
NordLayer is an adaptive network access security solution for modern businesses – from the world’s most trusted cybersecurity brand, Nord Security.

The web has become a chaotic space where safety and trust have been compromised by cybercrime and data protection issues. Therefore, our team has a global mission to shape a more trusted and peaceful online future for people everywhere.

About Version 2 Digital

Version 2 Digital is one of the most dynamic IT companies in Asia. The company distributes a wide range of IT products across various areas including cyber security, cloud, data protection, end points, infrastructures, system monitoring, storage, networking, business productivity and communication products.

Through an extensive network of channels, point of sales, resellers, and partnership companies, Version 2 offers quality products and services which are highly acclaimed in the market. Its customers cover a wide spectrum which include Global 1000 enterprises, regional listed companies, different vertical industries, public utilities, Government, a vast number of successful SMEs, and consumers in various Asian cities.

What is an insider threat?

Today, we’re taking an in-depth look at insider threats, offering you an overview of identifying and preventing these risks to keep your organization secure.

What’s defined as an insider threat?

The concept is fairly simple—an insider threat is a risk posed by someone within the company, like an employee, contractor, or partner, who has access to the company’s sensitive data, networks, and systems. This risk arises when that person, whether on purpose or by accident, misuses their access, putting the company’s digital resources at risk.

So, why do insider threats happen? There are a lot of reasons, and it really depends on whether the person meant to cause harm. Some insiders might act maliciously, wanting to hurt the company for personal gain or out of resentment. On the other hand, some are just negligent, causing harm unintentionally, simply because they’re careless or don’t fully understand cybersecurity. Whatever the reason, intentional or not, insider threats can cause significant damage to a company, both financially and to its reputation.

For many, this idea can be hard to accept because we naturally want to trust our team members and find it difficult to believe they’d harm the company. As a result, many organizations focus on external threats, overlooking the fact that insiders—armed with a deep understanding of systems, processes, and policies—can exploit vulnerabilities from within. What makes this even trickier is that sometimes, the actions of insiders are so subtle it’s tough to tell what’s normal and what’s actually harmful. That’s why cyber insider threats are often more difficult to detect than external ones.

Types of Insider Threats

It’s important to understand that insider threats are not monolithic—as briefly stated above, they fall into two main categories: malicious and negligent. This distinction is crucial for developing targeted strategies to effectively mitigate each type of risk.

Let’s first talk about malicious insider attacks—these are caused by individuals within the organization who intentionally seek to cause harm. Their motives could be personal gain, revenge, or even espionage. Malicious insider threats might involve stealing sensitive data to sell to competitors, sabotaging systems, or committing fraud. In short, these actions are deliberate and meant to hurt the organization, whether through financial loss or reputational damage.

On the other hand, negligent insider threats are caused by individuals who don’t intend to cause harm but still put the organization at risk due to carelessness or lack of awareness. Negligence often stems from failing to follow security protocols or making poor decisions, like using weak passwords to protect business accounts or falling for phishing scams and creating openings in the company’s protective layer. While these individuals aren’t trying to harm the organization, their lack of attention or poor judgment creates vulnerabilities.

There are also a couple subtypes of insider threats worth mentioning. One is the accidental threat, which is caused by human error. These are typically rare but can still cause significant damage, such as when an employee forgets to log out of a system or uses unauthorized software by mistake (also known as shadow IT).

And then we have the so-called third-party internal threats, the name of which sounds a bit contradictory. But that’s because it describes threats caused by external entities, like contractors, partners, or service providers, who aren’t full-time employees but still have access to the organization’s resources. Therefore, their actions—whether malicious or accidental—can also pose significant risks to the company.

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Insights from the frontlines: Insider threat examples

Moving from the theoretical to the tangible, let’s anchor our understanding of insider threats in the reality of actual incidents. These examples serve as critical lessons in the multifaceted nature of insider threats. Each incident sheds light on different aspects of insider actions, whether driven by malicious intent or accidental negligence, which can lead to significant security breaches.

The Morrisons data leak

Back in 2014, in an alarming display of malicious intent, a disgruntled employee at Morrisons supermarket exploited his access to confidential employee data. He leaked personal information, including bank details and salaries, of nearly 100,000 employees to the internet and newspapers. This breach not only exposed employees to potential financial fraud but also proved the critical need for stringent internal access controls and the ability to quickly respond to insider threats.

Anthem data breach

Anthem’s data breach is a stark reminder of the consequences of negligent insider actions. Attackers used a clever phishing scheme to get hold of the credentials of several key employees, which eventually led to unauthorized access to the personal information of 78.8 million individuals. This incident highlights how important is employee training on cybersecurity best practices and the implementation of robust security tools.

Edward Snowden NSA leak

Edward Snowden’s disclosure of classified NSA documents to the public is perhaps the most infamous and controversial example of an insider threat. The incident highlighted the profound implications that insider threats can have on national security. Snowden’s actions, driven by a belief in the public’s right to know about government surveillance programs, illustrated the potential for significant ideological motivations behind insider threats and the necessity for comprehensive vetting within organizations that have implications nationally and even globally.

These real-world examples emphasize that insider threats are not a monolithic problem but rather a spectrum of risks that require a nuanced approach to mitigation. They illustrate the necessity for organizations to develop insider threat programs that address both intentional and unintentional risks.

Insider Threat Prevention and Detection: Fortifying Against the Invisible Enemy

As organizations increasingly recognize insider threats as potentially organization-ending incidents, the imperative shifts to understanding these risks and actively implementing strategies to prevent and detect them.

Insider threats, by their very nature, require a nuanced approach. Here, we look at the cornerstone practices for bolstering your defenses.

Insider Threat Prevention

Prevention is the cornerstone of a robust security posture. Effective prevention combines early intervention with a comprehensive strategy, focusing on:

Access control and management: Employing strict access controls and regular reviews to make sure that employees only have the necessary privileges to perform their duties, thus minimizing potential abuse.

Security awareness and training: Developing an ongoing education and awareness program that highlights the importance of following the organization’s security policies, helping to prevent negligent behavior by making employees aware of the risks and how they should act in the face of those risks.

Regular audits and compliance checks: Conduct periodic audits of systems and practices to ensure compliance with security policies and identify potential vulnerabilities.

Reporting mechanisms: Creating reporting systems and fostering an environment where employees feel safe to report suspicious activity without fear of reprisal is critical for the early detection of potential threats.

Insider Threat Detection

Detection strategies are critical for identifying threats that prevention measures may not have fully mitigated. Effective detection is predicated on the ability to identify anomalies and act swiftly, involving:

Behavioral analytics: Implementing user and entity behavior analytics (UEBA) to monitor for unusual activity patterns that may indicate malicious or negligent insider actions.

Incident response and management: Developing a clear, efficient incident response plan that enables quick action to mitigate the impact of detected threats.

Technology and system monitoring: Utilizing advanced monitoring tools to continuously observe system and user activities for signs of insider threat, including unauthorized data access.

Feedback loops for continuous improvement: Creating mechanisms for feedback on the effectiveness of detection strategies, allowing for continuous refinement and improvement of security measures.

Harnessing password managers to combat insider threats

Among the tools available to protect organizations against insider threats, password managers emerge as a utility for convenience as well as a critical line of defense. Let’s explore how enterprise-grade password managers, such as NordPass Enterprise, can bolster an organization’s security posture against insider threats.

Centralized control over access

Password managers offer centralized control mechanisms that significantly streamline the management of user access to sensitive systems and information. By centralizing password storage, organizations can enforce company-wide password policies, ensure the use of strong, unique passwords across all accounts, and rapidly revoke access when a user’s relationship with the company changes or suspicious activity is detected.

Enhanced security features

Enterprise password managers come equipped with advanced security features such as multi-factor authentication (MFA), biometric access controls, and secure password and item sharing. These features add layers of security that make it significantly more challenging for malicious insiders to gain unauthorized access to critical systems. MFA, in particular, is a powerful deterrent against unauthorized access attempts, ensuring that even if a password is compromised, the additional authentication layer provides a formidable barrier.

Audit trails and monitoring

One of the key advantages of using an enterprise password manager is the ability to generate comprehensive audit trails and engage in proactive monitoring. Enterprise-grade password managers, such as NordPass, log user interactions with the stored credentials, providing security teams with valuable insights into access patterns and behaviors that may indicate a potential insider threat.

Educating and Empowering Employees

Beyond the technical benefits, password managers play a crucial role in fostering a culture of security awareness within an organization. They relieve employees of the burden of remembering complex passwords for every account and reduce the temptation to reuse passwords or resort to easily guessable ones. This, in turn, empowers employees to embrace security best practices without compromising productivity or ease of use.

A foundation for secure collaboration

In today’s collaborative work environments, such as IT security departments, the secure sharing of access credentials is critical but poses significant security challenges. Fortunately, tools like NordPass, a password manager for IT teams, address this challenge by enabling the secure, controlled sharing of credentials and access rights. This ensures that sensitive information remains protected, even when access is extended across teams or departments, mitigating the risk of insider threats related to shared credentials.

By integrating a robust password management solution into their cybersecurity strategy, organizations can significantly enhance their defenses against insider threats. Password managers provide a comprehensive suite of tools designed not only to secure passwords but also to enforce access policies, monitor user behavior, and promote a culture of security awareness.

About NordPass
NordPass is developed by Nord Security, a company leading the global market of cybersecurity products.

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The darkest season: the peak time of cyber threats

Summary: Dark web forums peak in activity during winter months. Holiday scams surge, boredom rises, and AI makes cyber-attacks easier.

The dark web is a key enabler for cybercrime. It allows bad actors to share tools, knowledge, and services secretly.

Anyone wanting to buy illegal items—like cyber-attack tools or drugs—can find them on dark web marketplaces. These markets appear and disappear quickly as they get blocked. They are usually advertised on dark web forums, and some even have mirror sites on the clear web.

Researching the dark web is hard because marketplaces have short lifespans. They come and go quickly. That’s why NordLayer and NordStellar decided to analyze dark web forums instead.

Forums are more stable over time. This stability makes it possible to see trends in discussions. These forums mix legal topics like news, politics, and content sharing with illegal activities.

However, legal activities like whistleblowing make up less than 1% of the content. Illegal activities are the largest part. By studying these forums, we wanted to uncover new trends in illicit activities.

Our research shows that illicit posts peak in November, December, and January. The darkest months of the year also see the most activity in the web’s shadowy corners.

Why is winter the peak season for illicit posts?

We studied posts from June 2023 to October 2024. We categorized posts by topics and focused on illicit ones. Here’s how those posts were distributed:

These numbers reflect posts on the dark web, not actual attacks. However, research by BitNinja Security, Cloud Security Alliance, and Mimecast shows that Q4 is also when most cyber-attacks take place. This suggests a link between increased dark web activity and real-world cybercrime during this period.

Why are threat actors more active in dark months, both discussing illicit topics and committing crimes?

Carlos Salas (Sr. R&D Engineer at NordLayer): “In most industries, November to January is the busiest time, mainly because of the high amount of transactions from Thanksgiving, Black Friday, and Christmas. Criminals exploit this, knowing people are more likely to click on a phishing link while going through thousands of email orders and offers, compromising their network security.”

It’s a known issue. Black Friday is already called Black Fraud Day. In the UK only, more than 16,000 reports of online shopping fraud were recorded between November 2023 and January 2024, with each victim losing £695 on average.

Andrius Buinovskis (Head of Product at NordLayer): “Everyone is looking for gifts and the best prices, and fake ads try to hook you into deals. Bad actors exploit this season, using urgency tactics boosted by AI to spread threats. People are more relaxed and less cautious, paying less attention to how they use personal and company devices. Employees might receive phishing emails like a supposed ‘yearly bonus’ from the CEO, which could lead to catastrophic consequences for the company.”

But on dark web forums, people discuss not only cybercrime. A big part of forums is about sharing pirated software and media, like movies.

This number grows in dark months. Comparing the summer months of 2023 with November—January, the number of dark forum posts about all kinds of pirated content surged by 105%.

Vakaris Noreika (Head of Product at NordStellar): “I think it’s the weather, to be honest. People tend to stay at home more and sit at their computers bored, which makes them more active in their cybercriminal activities. We’ve seen a similar effect during the COVID lockdown when the number of dark web users increased a lot. We also see fewer large data breaches in the summer, and this cycle seems to repeat every year.”

Like advanced persistent threats, “advanced persistent teenagers” are now a problem. Bored but skilled threat actors cause major disruptions. They trick employees with emails and calls, posing as help desk staff. These attacks lead to data breaches affecting millions. Teenagers now show techniques once limited to nation-states.

Another factor is adding to the boredom of dark web forum users. They are mostly from countries where winter is pretty harsh. Most users accessing Tor—the browser used for dark web activities—are from Germany (36%), the US (14%), and Finland (4%). For countries where users access Tor via bridges, the top is Russia (41%). Maybe dark web forums are just the coziest winter hangouts.

Changing platforms and AI effects on cybercrime

Our research shows that September and October of 2024 had much fewer posts about illicit things on dark web forums than a year before. Why is that?

Vakaris Noreika: “There could be many reasons why this happens. The most notable ones are maybe the platform changes; some hacker forums close, others open up, some become popular to fade out later.

There are some hacker communities, especially from Russia, which have been active for more than 20 years now. This is because the forum owners don’t get arrested, unlike forum owners from the US, UK, etc., who do get arrested way more often.

Telegram has also been a huge platform change. We’ve seen exponential growth in hacking-related activity on Telegram since the beginning of the war in Ukraine. But Telegram activity is focused on niche topics, while forums cover a wider range of ideas.”

Another trend affecting dark web discussions could be AI use in cybercrime.

Retail and cloud computing giant Amazon, which can now view activity on around 25% of all IP addresses on the internet, says it is seeing hundreds of millions more possible cyber threats across the web each day compared to earlier this year. They used to see about 100 million hits per day, but that number has grown to 750 million over six or seven months.

Amazon’s Chief Information Security Officer is sure AI is making tasks easier for ordinary people, allowing them to do things they couldn’t do before just by asking the computer. This might explain fewer discussions on dark web forums—why ask others when AI can do the work for you?

How to protect organizations during peak cybercrime seasons

So, winter months bring not only holidays but also heightened cyber risks. Instead of enjoying time with your family, you might find yourself dealing with cyber-attacks.

But don’t worry—there are steps you can take to protect your organization. The good news is these measures aren’t expensive or hard to implement.

Many of these precautions are the same as those needed year-round. Basic cybersecurity practices like employee training, strong passwords, and regular software updates are essential.

Employee education is the first line of defense.

Vakaris Noreika: “It’s hard to control what happens with your employees. It’s unavoidable that their data will be leaked online, and this data might be used to attack your company. Here’s what I always encourage companies to do:

Educate employees about phishing, credential stuffing, and other popular attack methods.
Take care of the information that’s already leaked: monitor it and react. NordStellar can help with that.
Manage access to important company resources carefully.

By doing this, you will be better off than 99% of companies around.”

Prepare now to minimize risks during the peak cyber-attack season.

Carlos Salas: “Double down on cybersecurity awareness in months before the high season. Consider having a pentest done beforehand to know what could be exploited by criminals.

That said, we’re humans, and there will always be a chance of clicking the wrong link or sharing the wrong files. So, practices such as network segmentation, setting up security policies for devices, or using toolsets such as Data Loss Prevention suites and malware protection are a must-have. They help contain the threats and minimize the ‘blast radius’ of any security incident.”

With AI making cyber-attacks easier, it’s crucial to think about these things right now, when the cyber-attack season is at its peak. The next year could bring even more advanced threats.

So, give your company a Christmas present and invest in a solid cybersecurity solution.

Methodology

NordStellar acquired data from over 80 forums where illicit activities are most often discussed. These forums span different web layers: the clear web, the deep web, and the dark web. We gathered textual content from forum threads between June 2023 and October 2024. The numbers we obtained represent the number of forum posts.

We used a fine-tuned AI model to categorize dark web posts into 67 tags. These tags were then grouped into 10 broader categories. For example, the tag “SERVICE” refers to posts where users offer services for a fee, including hacking or hiring hitmen. This tag falls under “Illicit services and marketplaces.”

The study is thorough but has limitations from analyzing posts on approximately 80 forums only. Additionally, the shorter lifecycle of criminal sites and the rapid rise of mirror sites can affect data consistency and completeness.

About NordLayer
NordLayer is an adaptive network access security solution for modern businesses – from the world’s most trusted cybersecurity brand, Nord Security.

About Version 2 Digital

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The role of machine learning in cybersecurity

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.

Machine learning can also boost a company’s cybersecurity by detecting and responding to threats faster than human analysts. This has led to the term “machine learning security,” which, while still a bit niche, describes how ML is used for security tasks like spotting malware or unusual network activity. With its ability to handle massive amounts of data, machine learning has become a key tool for keeping systems safe.

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.

The importance of data quality in machine learning security

To get the most out of machine learning, you need to give it high-quality data. Think of it this way: ML can only analyze and learn from what you put into it, so if the data’s flawed, the insights will be too. This is especially critical for companies using ML to support decision making. Without quality data, ML models may lead to misguided decisions.

Alongside accuracy, machine learning security is also a vital part of data quality. Sensitive information should be prepared and protected before feeding it into ML models. Some ML platforms, while powerful, have vulnerabilities that could expose data if not managed carefully. In short, quality data should be both precise and secure.

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?

Frequently, 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.

And so, integrating artificial intelligence in security, particularly through machine learning, can significantly enhance how your cybersecurity framework adapts to the evolving IT landscape. 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.

Challenges of Machine Learning in Cybersecurity

Just like in life, the things that bring us the most value come with their own set of challenges. After all, you can’t expect great results without putting in some effort. The same goes for using machine learning in cybersecurity. It can be incredibly powerful, but getting the most out of it requires navigating a few obstacles along the way. So, here are a few challenges you might face when applying ML to data security:

Adaptation to threats: Cyber threats are becoming increasingly intricate and complex, requiring ML models to undergo continuous retraining to identify new vulnerabilities effectively. This ongoing adaptation is essential to ensure that ML security systems remain capable of countering the latest tactics employed by hackers.
Adversarial attacks (ML poisoning): By manipulating input data or introducing deceptive data, attackers can compromise an ML model’s effectiveness, reducing system reliability and jeopardizing operations by making it more difficult to accurately identify malicious activity.
Operational issues: Integrating machine learning into an established cybersecurity framework isn’t always straightforward. There are a few challenges to consider, like the complexity of the implementation process, the risk of false positives that can add to analysts’ workloads, regulatory compliance requirements, and the limited availability of professionals skilled in both ML and cybersecurity.

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’re likely to see even more use cases in the future. However, machine learning in IT security will be one of the key areas that will continue to evolve.

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 IT team‘s online experience and enhancing overall company security, explore what enterprise password management can offer for your company.

About NordPass
NordPass is developed by Nord Security, a company leading the global market of cybersecurity products.

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How to choose the best DNS filtering solution for your business

Summary: Discover key factors for selecting a DNS filtering solution that enhances network security, boosts productivity, and ensures compliance for your business.

Now, businesses face many online threats that can jeopardize network security, reduce employee productivity, and compromise regulatory compliance. Domain Name System (DNS) filtering is a powerful tool for protecting against these threats by blocking access to harmful websites—those that may host malware, phishing attempts, or inappropriate content.

Beyond protecting your network, DNS filtering tools improve workplace productivity by limiting access to non-work-related websites. They also help ensure compliance by restricting access to certain types of content.

However, with many DNS filtering providers available, selecting the right one can be overwhelming. This guide will walk you through the key factors to consider when choosing the best DNS filtering solution for your organization.

How DNS filtering solutions work

DNS filtering is like a gatekeeper for internet usage, preventing access to malicious or inappropriate websites before they can harm your network. By intercepting DNS queries—requests users make when accessing a website—the filtering system determines whether the requested domain is safe based on predefined security policies.

Typically, DNS servers function like an internet “phonebook,” translating domain names into IP addresses to connect your browser and the required website.

With a DNS filtering solution in place, however, each query undergoes additional checks. If the requested site is flagged on a blocklist or is identified as a security risk, the DNS resolver blocks the request, preventing the page from loading and neutralizing potential cyber threats.

Benefits of implementing a DNS filtering solution

Deploying a DNS filtering solution offers a range of benefits that go beyond basic Internet browsing controls:

Internet threat prevention

Each organization should control employee online traffic. By blocking access to sketchy sites full of malware, phishing, or ransomware, DNS filtering solutions shield your network from all kinds of cyber-attacks before they even have a chance to strike.

Keeping productivity on point

Let’s face it—distractions are everywhere. DNS filtering tools help minimize those distractions by blocking non-work-related sites so your team can stay focused and get more done.

Improved network performance

No more bandwidth hogs. A DNS filtering solution ensures your network runs smoothly and efficiently by limiting heavy streaming or large file downloads.

Security compliance

Worried about regulations? DNS filtering helps you meet industry standards by controlling access to restricted content and protecting your business from potential legal and reputational risks.

Keeping remote workers safe

With more people working remotely, DNS filtering solutions block online threats and secure sensitive data, no matter where your employees log in.

Filtering for safer Internet access

Whether it’s a school, home, or workplace, DNS filtering blocks inappropriate or harmful content, creating web filtering for schools or employees.

5 considerations for choosing the best DNS filtering solution

When it comes to selecting a DNS filtering provider, it’s essential to weigh your options carefully. With so many choices out there, understanding the key factors can help you find the right fit for your organization. Here are some critical considerations to keep in mind:

#1 Technical architecture

The backbone of a solid DNS filtering solution is its technical architecture. You’ve got two main options: cloud-based or on-premise. Cloud-based solutions are super scalable. They make it easier to grow with your business’s security needs. They are also easier to deploy, need less maintenance, and usually come with real-time updates.

On-premise solutions give you more control over your data. This can be a big help if you have strict privacy rules. However, they might require higher initial costs, more time, and greater expertise to maintain.

Another thing to keep in mind is DNS resolution speed—how fast it can process requests and load websites. A provider with a global network will keep things running smoothly with less lag when accessing sites.

#2 Advanced threat detection

In today’s world, you need more than just the basics. Look for a DNS filtering solution that’s equipped with advanced threat detection. Such a solution must monitor network activity in real-time, spotting and blocking threats like malware and phishing before they can mess with your network. As cyber threats keep evolving, having a tool that adapts is a must.

#3 Integration with existing systems

Whatever DNS filtering solution you pick should be compatible with your current system. Make sure it works well with your existing security infrastructure, like your firewall or Security Information and Event Management (SIEM) tools. Some providers even offer API access for easy integration with third-party tools or custom solutions. A smooth integration means less hassle for your IT team and a more seamless security experience.

#4 Granular policy management

DNS filtering is designed to restrict access to specific content, but when it comes to defining exclusive rules for network access, we enter a different technological area. Therefore, when selecting DNS filtering solutions, it’s best to look for comprehensive products beyond content restriction and address network access use cases.

Fine-tuning access with your DND filtering solution helps boost productivity and security, keeping everyone where they need to be.

#5 Real-time analytics and reporting

Keeping tabs on what’s happening in your network is essential. Make sure your DNS filtering provider offers real-time analytics and reporting so you can spot potential threats, check network activity, and stay compliant. Detailed DNS query logs and custom reports are especially useful for digging into incidents or proving you’re following industry regulations.

Tips for selecting the best DNS filtering solution

Check out content control features: Look for customizable filtering options that let you block malware, phishing attempts, adult content, gambling sites, and more. Keeping distractions and risks at bay is key for productivity and compliance.
Make sure it has solid security features: Don’t settle for basic protection. Your DNS filtering solution has strong encryption, advanced threat detection, and malware protection. These features add extra layers of security, especially when your data is in transit.
Go for user-friendly setup and centralized management: Setting up DNS filtering shouldn’t be a headache. Look for something simple to install with centralized management so your IT team can control everything from one spot, enforce policies, and quickly handle any issues.
Look for customization options: Every business is different, so you’ll want a solution that lets you fine-tune filtering rules to fit your specific needs. Flexibility is key to keeping security tight without slowing down business activities.

Conclusion

Choosing a DNS filtering solution for your business is critical. It impacts everything from your cybersecurity to productivity and compliance. Take the time to evaluate things like the technical architecture, how the provider handles threats, and how well the solution integrates with your current systems. Opt for providers that offer robust security, real-time reporting, and detailed control over access to make sure you’re getting the best DNS filtering solution possible.

With the right DNS filtering in place, you can protect your network, control online interactions, and create a safer, more productive work environment for your team.

How NordLayer can help

NordLayer offers easy-to-use DNS filtering capabilities to protect your network. With features like DNS filtering by category, Web Protection, and Download Protection, keeping your team safe is simple. Setup is quick, even for non-tech users, and managing security for your whole team is straightforward.

DNS filtering by category allows IT admins to block content from over 50 categories. This helps keep your network secure and your team focused.
Web Protection automatically blocks access to websites that are flagged as potentially malicious.
Download Protection scans every new file download and removes harmful files before they can infect your devices.

These features can work together to prevent risks like malware infections and phishing. But that’s not all. All NordLayer customers get encrypted connections and masked IP addresses. This ensures your internet access is secure, no matter where you are.

Want to learn more? Contact NordLayer’s sales team to see how we can help protect your network.

About NordLayer
NordLayer is an adaptive network access security solution for modern businesses – from the world’s most trusted cybersecurity brand, Nord Security.

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