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.

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.

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.