Top 6 Behavioral Analytics Patents for Combatting Data Breach

In the ever-evolving battle against data breach, innovative technologies play a crucial role in bolstering cybersecurity defenses. Behavioral analytics has emerged as a key player, leveraging advanced patents to enhance the detection and prevention of ransomware and other cyber threats. This article delves into six groundbreaking patents that stand at the forefront of cybersecurity innovation, each offering unique methods to thwart potential data breaches and secure digital assets.

IBM’s Dynamic User Behavior Profiling (Patent US10789588B2)

IBM’s patented technology revolutionizes ransomware defense by dynamically profiling user behavior. For the uninitiated, “dynamically profiling user behavior” means real-time monitoring and analyzing user activities across systems to detect potential threats and incongruities.

Of course, like every cybersecurity patent worth mentioning, it harnesses machine learning and AI to recognize normal human behavior patterns so it can instantaneously flag behavior(s) that deviate from these typical patterns. 

Here are 4 ways this uber-sophisticated patented tech drive results in cybersecurity:

1. Analyzing log-in times to create a pattern of normal log-in times vs. times that are highly unusual for users to log in and analyze whether this unusual log-in time may be a potential data security threat. 
   
2. Examining the volume of typically accessed data and immediately raising a red flag if it deviates strongly from the mean.
   
3. Track the IP address and physical locations where user behavior occurs to trace any data breach.
   
   There is a solid rationale behind simultaneously tracking IP address and physical location. By deploying this strategy, cybersecurity tech can identify cases of “improbable travel velocity.” This is nerd-speak for when a user is physically in one location but logs in from another in such a short time that it would be impossible for them to reach the other area in that time interval.
   
   This incongruence indicates a high probability of a data breach where someone has compromised a user’s credentials and is now misusing them for a cyber attack. 
   
4. This cutting-edge tech analyzes every user’s behavior to form a full-scale, multi-layered, robust model for every individual. If deviations from the model occur, it flags them in real-time for further investigation to mitigate any potential data breach.

Microsoft’s Contextual Anomaly Detection (Patent US10876087B2)

Microsoft’s patent introduces a contextual anomaly detection framework with a highly nuanced technique for detecting a data breach or a potential cyber attack. Their tech deploys machine learning to understand user behavior in various contextual settings such as:

1. Time of the day and/or what device they use to log in at a particular time of day
    
2. The duration of sessions and the types of activities performed (online purchases, downloads, uploads, and similar variables.) in these sessions. They also track the device used to perform these activities while factoring the time of day.
   
3. Tracking other contextual variables that give rich insights including user location, browsing history, and more. 

The model analyzes these contextual user behavior patterns and trains itself to consolidate these variables into a behavior model for each individual user. If there is an anomaly in the behavior pattern within this highly nuanced framework, the system raises a flag to notify the appropriate authorities or requests the user to provide further information to confirm their identity.

Splunk’s Behavioral Baseline Generation (Patent US10900257B2)

Splunk’s patented technology works by taking the following step-by-step approach:

1. Collection: It collates existing data sets from the system to establish a baseline of normal user behavior. This data encompasses logs, metrics, transactions, downloads, log-in durations, devices used, and/or any other relevant information. 
   
2. Cleaning: This data is then “cleaned up” and processed to remove any confounders, such as missing or irrelevant information or outliers, to optimize the baseline information.
    
3. Extraction: It extrapolates features from the data that inform the patented technology on various aspects of normal user behavior. These include but are not limited to frequency, timing, duration, and type of events the users perform. 
   
4. Learning: The machine model learns what normal behavioral patterns are the basis of these extrapolated features. For machine learning to occur effectively, it represents summaries of typical user behavior through various statistical models such as probability distributions. 
   
5. Detection: Now that the baseline behavior is fully established, the patented technology can detect anomalies and deviations from normal baseline patterns. When it detects a potential data breach or cyber attack through these anomalies, it notifies the authorities instantaneously, also informing them of the severity and nature of the possible data breach. 
   
6. Mitigation: Depending on the nature of the data breach, the system can also initiate protocols that can mitigate potential threats. These protocols include incorporating security measures, initiating investigations, and/ or launching corrective measures. 

RSA’s User Risk Scoring Engine (Patent US10846749B1)

RSA’s patented user risk scoring engine revolutionizes ransomware defense by assessing the quality and quantity of user risk based on multidimensional interaction patterns. 

The patented framework deploys the following defense mechanisms to ensure efficacy:

1. A constantly evolving cycle of evaluations: Frequent assessments, not just in light of new events but also at regular timeframes, empower the tech to constantly improve at risk assessment.
   
2. Keeping score: The system creates a risk scorecard that categorizes risk based on low, medium, and high in terms of the likelihood of a potential threat and the damage it can cause. It also factors in variables like recency and frequency. 
   
3. Predefining levels of risk: The system establishes minimum risk thresholds that an organization would be willing to bear before taking pre-emptive action against a data leak or breach.  Only if the potential security threat surpasses this threshold is any action taken. This protocol mitigates a myriad of false positive potential security threats, thereby saving personnel time, effort, and cost. 
   
4. Alerting the authorities: If the potential security threat goes beyond the minimum risk threshold, the respective authorities within the organization are notified so they can take appropriate action. 

Varonis’s File Access Anomaly Detection (Patent US10789590B1)

This patented technology is superlative for detecting suspect behavior within an organization’s IT infrastructure by isolating data patterns that deviate from normal.

Here’s a breakdown of how this potent technology works:

1. Exhaustive Metadata Collection: This patented solution collects comprehensive metadata from the organization’s files. Examples of the metadata include:
   • Identifying who accessed which file
   • The time and duration in which they accessed it
   • The user behavior that occurred during access- examples include reading, writing, or modifying the files.
      
2. Harnessing Sophisticated algorithms that harness User and Entity Behavior Analytics (UEBA): These ultra-refined algorithms are formulated and enhanced through machine learning and artificial intelligence for high impact and scalability. They can detect potential data leaks and data breaches by distinguishing between normal user behavior and any deviations from the same in real time; using this technique, they can flag potential data leaks or security breaches.
   
   For example, suppose a user abruptly accesses large amounts of file datasets or performs any other action that doesn’t conform to their normal behavioral patterns. In that case, the patented system will immediately flag the deviation and report it to the predetermined authorities. 
   
   So, by constantly monitoring file access activities, Varonis’s patented File Access Anomaly Detection tool enables organizations to effectively recognize and mitigate potential data breaches, data leaks, cyber-attacks, and more—whether these threats arise internally or externally. 

Dark Trace’s Autonomous Response Mechanisms (Patent US10846741B2)

Darktrace’s patented technology’s unique strength lies in its ability to detect and mitigate potential threats with minimal human intervention. This autonomy is game-changing for organizations most vulnerable to data leaks, data breaches, and ransomware because it reduces the need for having a disproportionately large team to trace and mitigate these threats. 

Here’s how this cutting-edge tech solution leverages AI and deep learning to mitigate threats in real-time autonomously:

1. Constant Tracking: Darktrace’s patented platform vigilantly oversees network traffic and data flows, enabling it to analyze suspicious user behavior that deviates from normal user behavior within the organization’s IT setup.
   
2. Deep Learning: The patented solution uses deep learning to create AI algorithms that detect deviations in user, device, and network behavior, identifying potential cyber attacks, data leaks, or breaches.
   
   Now, here’s where this technology goes beyond the most advanced cybernetics…
   
3. Autonomous Defense: Darktrace’s solution autonomously unleashes its strong defenses to crush any threats with little to no human intervention. 
   
   For instance, 
   • It can isolate the source of threat in the network to prevent malware from spreading or to prevent a data breach. 
   • Moreover, if a device has been infected, the solution can automatically confine it to mitigate additional damage such as phishing or cyber-attacks. 
   • It can also shift network traffic routes or manipulate access controls to keep malware at bay. 
   • If it feels that user access has been compromised, it can automatically reset credentials or revoke access to prevent data leaks or breaches. 
   • The platform constantly evolves to respond faster and more robustly to potential threats by learning from each incident. Thus, it keeps auto-enhancing its potency and efficacy.
      
4. Combining human and AI: Despite its almost fully autonomous capabilities, it offers full transparency and allows authorities to override decisions if needed.

Final Thoughts

Behavioral Analytics leveraged by AI is becoming an integral piece of the cybersecurity puzzle. They’re not just a compelling use-case for more commonplace threats like data leaks, lock bits, or data breaches but also superior for niche aspects like pre-empting phishing attacks, especially the dreaded spear-fishing attacks. This advanced form of cybersecurity is now an easy and scalable solution due to its constantly evolving and multi-layered learning, understanding, and analysis of human behavior.

– By: The Editorial Team