The rapid expansion of IT has transformed our interactions with the world with every mouse click and keystroke generating vast amounts of data, capturing our digital footprints.
While this data may seem mundane on its own, the application of AI-powered behavioral analysis is changing the game in cybersecurity, ushering in a new era of proactive, real-time threat detection. In this article, let’s explore AI-powered behavioral analysis in cybersecurity and explore how it can help an organization’s security posture.
What is Behavioral Analytics?
Behavioral analytics is a subfield of business analytics, characterized by iterative analysis of past business performance to collect insights for decision-making. Behavioral analytics includes a diverse range of methods, all aimed at collecting data to make more informed decisions. It involves analyzing competitors’ performance, using predictive analytics to foresee industry trends, or assessing employee productivity.
In the context of cybersecurity, behavioral analytics harnesses the power of machine learning, AI, and big data analytics to collect user behavioral data. It then identifies trends, anomalies, and patterns within this data using AI.
Why Is AI Behavioral Analytics In Cybersecurity Important?
Traditionally, rule-driven frameworks were the primary means of identifying potential threats in cybersecurity. For instance, if a substantial amount of data was downloaded during off hours, it might trigger a rule violation, alerting the business.
While rule-based systems remain integral to today’s security approach, savvy hackers can often evade many of these predefined rules. Identifying malicious activities by employees, or insider threats, can prove challenging within this rule-based paradigm.
This is where behavioral analytics comes in, offering people-centric security by analyzing entity and user data through an enterprise to detect anomalous behavior indicative of a security breach.
Behavioral analytics in the realm of cybersecurity is also known as User and Entity Behavior Analytics (UEBA), a concept gaining prominence due to its potential for significant time and cost savings.
UEBA sifts through vast volumes of an organization’s data to generate better leads for the security team. Moreover, it has the potential to reduce the demand for security analysts, alleviating the pressure on businesses to compete for scarce security talent.
Use of AI Behavioral Analytics
An important application of AI behavioral analytics in cybersecurity is the identification of insider threats.These threats involve attacks carried out by individuals within an organization, often motivated by malicious purposes.
Insider threats pose a unique challenge as attackers have legitimate access to critical company information for their job roles, rendering many conventional security rules ineffective. Behavioral analytics bridges this gap by flagging unusual employee behavior, and alerting security teams to potential threats.
Another application is the detection of advanced persistent threats (APTs). APTs arise when hackers maintain prolonged access to a company’s server. These types of attacks are intentionally designed to evade traditional detection methods by avoiding triggers that would expose them. However, behavioral analytics excels at identifying APTs by monitoring and flagging unusual activities that align with APT behavior patterns.
By quickly identifying anomalies and potential threats in real-time, AI behavioral analytics enhances the security posture of organizations, offering protection against threats. As the digital realm continues to evolve, embracing AI-enhanced behavioral analysis is vital to protect sensitive data and digital assets in an increasingly complex threat landscape.