How Cybersecurity Solutions Leverage AI to Detect Insider Threats

The next generation of cybersecurity is approaching, thanks to the advancements that artificial intelligence (AI) has provided to analysts. The data analytics, machine learning capabilities and automation potential AI enables is monumental, specifically for identifying problems within organizations. What is the best cybersecurity solution for detecting insider threats, and how does AI influence its potential?

Self-Teaching AI With Anomaly Detection

Some cybersecurity organizations provide clients with a blanket solution, featuring programmed rules to identify the most common threats. While there are identifiable patterns that can span organizations, each environment is unique, utilizing a completely different defensive and offensive infrastructure. 

Every case study demands curated AI. Providers like Darktrace leverage multilayered AI that learns based on the specific company it operates for, making it more adept at detecting the subtle differences between threat indicators. After repeated exposure to different attack variants, it will eventually be able to classify attempts based on rarity, characteristics and other parameters. 

The threat profile will differ from that of competitors across the street and entities of similar sizes across continents. This makes self-learning AI the best option for accurately identifying typical security behaviors within an organization to flag suspicious behavior more effectively. This could prevent alert fatigue among teams, as the model becomes progressively attuned to the niche threat environment. 

Bayesian Probabilistic Techniques

With the right modeling, AI could also identify when an employee starts exhibiting behavioral changes. Some insider threats originate from the most trusted and tenured staff members. Consider an individual whose daily work ethic and virtual behaviors have trained AI to form a specific belief system about them — the data serves as evidence, or a pattern of life, of their trustworthiness. 

Bayesian probabilistic techniques introduce reasonable uncertainty without compromising judgment. Small changes may otherwise go unnoticed, and AI can adapt and reassess individuals as malicious or not. The most gradual shifts are the most challenging to detect, and Darktrace ensures this paradigm is ingrained into AI threat detection for greater peace of mind.

AI-Powered Dynamic Risk Scoring

Less intuitive AI models may assess insider threats as anomalous without providing specific details. It could also cause false positives by flagging benign yet atypical behaviors as suspicious, potentially hindering workflows. Organizations can design AI to be more dynamic, considering historical information while contextualizing real-time events.

For example, an algorithm may notice an employee sending more emails with attachments than normal. This could suggest an issue, and AI would assign the individual case a higher risk score than others. It could alert teams to keep an eye on a situation before it escalates, prompting interviews with the suspected insider threat or initiating an investigation to clarify a misunderstanding.

Clustering Algorithms

Clustering algorithms take similar information and group it together to discover outliers, and Darktrace’s solution compares this data against other internal analytics. It could examine the behaviors of individuals on a specific team, showing users with abnormally high download usage or login times. 

AI could also identify potential threats by noticing task differences in comparison to other groups. If an employee in human resources starts regularly using generative AI to write code, or if an office administrator is logging into developer tools, then AI could flag these as potential concerns based on the trends of each cluster. It is one of the best cybersecurity solutions for detecting insider threats, as it also prevents false positives by designating specific activities to specific clusters.

User and Entity Behavior Analytics (UEBA)

Companies like Exabeam and Gurucul use techniques such as UEBA to maximize the utility of AI in cybersecurity. According to recent research, 83% of organizations endured an insider threat in 2024, signaling the need for more robust protections. It combines the power of defined programming and rules with machine learning. This establishes a baseline understanding for a model to assess trends across in-house networks and devices. 

The guidelines can be informed and categorized by employee expectations, permissions access and role descriptions, so the algorithm can notice when an account or device is acting out of order. Deviations notify analysts and IT teams, allowing them to isolate and cease activity in real time. AI can take the information about this insider threat, observe how the workforce responded to it and continue learning from these data points. 

Large Language Model (LLM)-Based Intent Analysis

Companies can also utilize LLMs to monitor communications within an organization. AI could scrape public emails, messages and announcements, noticing differences in how people typically speak and interact. Organizations could also use it to spot suspicious anchor text and hyperlinks. It could cite an insider trying to phish a colleague or discover suspicious emails that appear legitimate. 

Spotting intent is key, which can be reviewed with sentiment analysis and connotative implications within language. AI can notice something as nuanced as social engineering tactics between multiple employees. It could automatically alert analysts of keywords like “hack” or “bribe” while reporting the potential implications of more subtle language, such as phrases like “convince” and “hide.” It can judge wording associated with previous threats and predefined incorporations from the team to catch even the most complex deceptive communications.

Attack Path Modeling

Darktrace also utilizes attack path modeling to identify insider threats by visualizing the most common paths for an attack to occur. It analyzes digital environments end-to-end, determining which assets an insider could exploit to access vulnerable systems. The routes an employee would take vary significantly from someone on the outside attempting to access the network. Therefore, conventional cybersecurity practices may overlook these pathways.

This is almost like simulating penetration testing, as the model can show analysts how an attack would unfold from in-house pathways. It could reveal backdoors that teams never noticed before because they focused too much on external hazards. This informs teams about proactive approaches and additional defenses to implement in the future, especially if the model identifies it as a frequent threat type.

AI-Driven Playbooks and Automated Responses

While most insider threat solutions using AI discover malicious behaviors, some use cases are more defensive. Instead of alerting analysts to take offense, AI models are generating playbooks to guide threat response. It can be calibrated to focus on or take special actions with insider threats based on how AI engineers encourage learning.

Over time, AI develops rules that it follows to make decisions about suspected activity. This constantly evolving ruleset is something the model can refer to for different phases of the response process. For example, it can take action to isolate a user from accessing the network while simultaneously alerting someone on the team to the severity of the attack. It could also generate hypotheses about the best solutions for each step, including automated access revocation or manually disconnecting vulnerable hardware. 

Identity and Access Analytics

Insiders often take advantage of their credentials to gain easy access. However, some employees may have varying degrees of freedom tied to their accounts. Models could spot when someone’s login information is being used on a certain machine or device. 

It could also notice when an account is superseding its established permissions and gaining access to places it should not. AI models notify teams of these instances, allowing them to actively manage permissions in real time. They can boot people from the network or require reauthentication, if necessary.

The Best Cybersecurity Solution for Detecting Insider Threats

Many providers offer services and products to fend off threats, but fewer have specialized tools to stave off insider attacks. With the frequency of these threats on the rise, it is more crucial than ever to partner with experts at organizations like Darktrace to provide a secure foundation for business operations. It will be the decision that protects employees, clients and stakeholders more than anything in the coming years, especially when teams never know what type of social engineering and manipulation is happening behind the scenes.