The cyber environment has undergone a paradigm shift. Although organizations are still reinforcing their borders against external attacks, it is the internal attacks that cause the most catastrophic breaches. The total cost of insider risk is on the increase, with the annual average standing at $17.4M, compared to its 2023 level of $16.2M, in the most recent 2025 Ponemon Institute Cost of Insider Threats Global Report. Not only are these incidents expensive, but they are also time-intensive, as the average annual cost of insider threat incidents that required more than 91 days to identify in 2024 was 18.7M. In contrast to outside attackers who need to exploit several security layers, malicious insiders have valid access credentials, have deep insight into organizational weaknesses, and are trusted by their co-workers. Conventional methods of cybersecurity, which are based on the idea that threats are external, are essentially ineffective in dealing with this challenge. This paradigm shift with human-centric cybersecurity, including the utilization of behavioral science and artificial intelligence in understanding, predicting, and eliminating risks caused by internal organizational sources, is the solution.
The Evolution of Insider Threats
Insider threats have outgrown the stereotype comprised of a discontented employee who wants to take revenge. The current insider threat environment is complex and has three major types, including malicious insiders who are deliberately damaging the organization, negligent insiders whose laxity can leave the organization vulnerable, and compromised insiders who have had their credentials stolen by malicious individuals. According to research by Proofpoint, the number of insider threat incidents has increased 44 percent over the last two years, and the average cost per incident has increased more than a third to $15.38 million. This development requires advanced mechanisms of detection that are beyond the mere use of rules to monitor.
The Human Factor Challenge
The risk of insider threats is that they are unpredictable, and it is difficult to draw the line between the intent of malicious actions and the natural variation of behavior. Behavioral change is the natural behavior of employees because of changes in workload, personal condition, change of roles, and change in work requirements. Conventional security systems find it hard to distinguish between these normal variations and what is actually suspicious. The picture is also complicated by regional differences, where the total cost is highest among the companies in North America at $19.09 million, and the companies in Europe were the closest with a total cost of $17.47 million. Moreover, the financial services industry has the most substantial expense, and the Ponemon Institute showed the average expenses of 20.68 million.
Behavioral Science: The Foundation of Human-Centric Security
The behavioral science is important in offering essential information with regard to how human beings make decisions, how they respond to incentives, and how they behave in different situations. In the context of cybersecurity, this could be interpreted as the knowledge of the psychological and social factors that could predispose individuals to insider threat actions or put them at risk of falling victim to social engineering attacks. The major behavioral patterns identified by research are temporal patterns, access anomalies, and social-psychological indicators. Insider threat events are usually preceded by abnormal working hours, hurried data access patterns, and timings.
The Neuroscience Connection
Studies in neuroscience have shown that the process of decision making, particularly when encountered in situations of stress or ethical decisions, is predictable. This intelligence is operationalizable in cybersecurity systems to determine when individuals can be more vulnerable to make poor security decisions or when the behavior pattern may indicate some internal conflict that could be a security risk. In combination with technical indicators, alterations in patterns of communication, growing isolation of the colleagues, displays of dissatisfaction, or financial pressure can become early warning signals, which will give the complete picture of the potential insider threat risks.
AI-Powered User and Entity Behavior Analytics (UEBA): The Technical Revolution
User and entity behavior analytics (UEBA) is a cybersecurity tool designed to identify anomalies in the behavior of not only the users of a corporate network, but also the routers, servers, and endpoints of said network. The technology is a great progress in the insider threat detection capabilities. UEBA leverages ML and data analytics to define the common pattern of behavior of users and entities within an organization. It is through this behavioral baseline that UEBA is able to identify any malicious trends of activity within systems and networks within an organization. The example of Microsoft implementation proves the given principle: Microsoft Sentinel User and Entity Behavior Analytics (UEBA) simplifies anomaly detection and investigation through machine learning models to create dynamic baselines and peer comparisons.
Advanced Machine Learning Approaches
Modern AI systems are able to analyze large volumes of behavioral data to reveal subtle patterns that human analysts cannot discern. With the help of machine learning and sophisticated analytics, UEBA can create a behavioral baseline of users and entities, and identify abnormal activities and threats, including insider threats or compromised accounts, that would otherwise be overlooked by more conventional rule-based security tools.
Dynamic Baseline Creation: UEBA is a state-of-the-art cybersecurity technology that builds a baseline of normal user behavior within your network using machine learning algorithms. These systems are constantly trained and evolve with the evolving behavioral patterns, decrease false positives, and maintain high detection rates.
Broad Threat Detection: UEBA solutions are intended to avert, stop, and neutralize the wide range of cyber threats with their ability to detect a deviation from the usual patterns of activity, such as unusual times of logins, as well as changes in user behavior, which indicate a possible security breach.
Real-Time Processing: The current generation of UEBA applications is able to analyze user actions in real-time so that suspicious activities can be responded to instantly.
Privacy and ethical considerations
Human-centric cybersecurity poses privacy threats, which require privacy-by-design principles. Data minimization, purpose limitation, and proportionate monitoring of behavior are required so that organizations can strike a balance between the privacy of employees and threat detection. UEBA helps companies to prevent and identify suspicious behaviors, and thus to help comply with regulations, such as GDPR, CCPA, and industry-specific ones. Both audit trails and automated reporting can be used to improve compliance in UEBA systems. Behavioral analysis systems that utilize AI should be clear, interpretable, and objective, and need carefully crafted algorithms, bias testing, and well-defined governance structures, with ethics boards supervising AI deployment to provide equitable treatment to employees.
Key Takeaways
- This is because insider threat costs amount to an average of $17.4 million per year, and they have to be addressed and invested in by the executive urgently. Conventional perimeter-based security cannot be used to deal with these advanced threats.
- Significant insider threat mitigation requires the comprehension of human behavior, motivation, and decision-making. Behavioral science knowledge should be used together with technology to reach the best outcomes.
- User and Entity Behavior Analytics is the latest generation in insider threat detectors that offer machine learning-enhanced baseline generation and anomaly detection, which is a vast improvement over rule-based systems.
- The threat environment also keeps changing and necessitates dynamic capabilities and constant enhancement of security postures. Organizations have to be agile in their management of insider threats.
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Er. Kritika is a dynamic and experienced cybersecurity researcher whose work delves deep into the human side of digital risk, exploring how our brains, behaviors, and biases shape our responses to cyber threats. What makes her work truly stand out is her unique interdisciplinary lens, centered on the rapidly evolving field of neuro-cybersecurity, where the frontiers of the human brain and digital security intersect. She investigates how cognitive vulnerabilities and neural processes shape cybersecurity behavior, exposing new risks and redefining resilience in the digital age. She has authored 40+ publications, with her insights featured in top journals and thought leadership platforms like the ISACA, CXO DigitalPulse, IGI Global, etc, and delivered 10+ keynote sessions. Her research isn’t just about identifying problems; it’s about designing human-centered, ethically sound solutions for tomorrow’s cybersecurity threats.
