Anomaly Detection 101: Identifying and Preventing Security Breaches

As attackers continuously develop more sophisticated tactics, organizations need to implement threat detection measures capable of identifying both insider and outsider threats. This is particularly important given the explosion of hybrid workforces and the growing reliance on cloud-based services.

Anomaly detection is the important process of identifying unusual or rare events that deviate significantly from standard behaviors or patterns. These deviations are also known as standard deviations, outliers, noise, novelties, and exceptions.

Although anomaly detection is gaining a lot of traction lately and should serve as the cornerstone to every organization’s compliance and data protection, many of today’s solutions have proven to be effective at only detecting data breaches, and not so much at actually preventing them.

In this post, we share everything you need to know about today’s anomaly detection solutions so your organization can better identify abnormal events and prevent data breaches.

 

Common Industry Challenges of Effective Detection Adoption

Despite the hype surrounding anomaly detection, broad adoption has been hampered for a few reasons:

• Building an anomaly detection solution from scratch is difficult due to the extensive background required in machine learning, AI, neural networks, domain knowledge, and foresight. This is so time-consuming and costly to build and adapt over time that few organizations attempt this project.
• Most anomaly detection solutions are deployed at the firewall or network level rather than the data access level, limiting their ability to detect data requests that may be malicious at the data level.
• Log file and user behavior analysis tools do not operate in real-time, meaning they can help discover hacking or unauthorized access but cannot prevent it in real-time. This necessitates additional anomaly detection technology that uses machine learning and quarantines suspicious behavior.

How 3 Recent Advances in Anomaly Detection Have Enabled Better Identify and Prevent Security Breaches

Fortunately, there are three critical advances in anomaly detection technology that have enabled organization to not just detect, but prevent costly data breaches:

1.     Anomaly Detection at the Data Level

Traditionally, anomaly detection has focused on identifying bad actors at the network level. However, this approach leaves data vulnerable to breaches even if the network is secure. Imagine a museum where there are security guards patrolling the entrances but no security measures surrounding the works of art themselves—if a criminal slips by the security guards, they can easily walk away with a priceless painting!

However, by employing anomaly detection at the data level, organizations can protect the actual assets that are targeted by malicious actors, preventing them from accessing sensitive information even if they infiltrate the network. To return to our museum analogy, think of high-level security directly surrounding the Mona Lisa!

2.    Machine Learning

Machine learning (ML) has proven highly effective for improving anomaly detection accuracy and helping organizations manage big data. ML systems can learn from their own experience, refining their analytical and predictive capacity. This makes them particularly valuable for accurate anomaly detection.

The advantages of an ML-based anomaly detection solution include the system’s ability to proactively handle unlabeled data and determine what is normal and what may be considered an anomaly. Furthermore, ML systems are more sensitive to distinguishing anomalies from noise, allowing them to differentiate data units based on their deviation from the norm.

3.    Machine Learning and Real-Time Anomaly Detection

Real-time anomaly detection combined with machine learning enables organizations to proactively detect malicious attempts to access, use, and steal information. Algorithms that enable pattern detection while the model constantly learns with every signal or event provide organizations with the ability to process large amounts of data while eliminating mistakes.

For example, some anomaly detection solutions use a combined probability score called a threat score, which ranges from 0 to 100. If the threat score is high enough, the system can quarantine or stop requests in real-time, preventing data loss. The threat score can use multiple thresholds to trigger actions, allowing anomalies to pass through while logging every anomaly. If an anomaly is deemed above a certain threshold, it can trigger a quarantine action and refuse the execution of the anomalous query.

Machine learning algorithms can analyze each data access request and categorize them based on their threat potential. Anomaly detection solutions can achieve this through real-time, self-learning ML models that not only detect threats but also stop their execution and quarantine them. Typically, anomaly detection solutions create a baseline of what is normal versus anomalous over a period of time by either: 1) having a baseline window where the system trains itself or 2) processing historical logs on which the system can train itself.

 

Partner with Sotero

Sotero uses advanced ML algorithms to utilize behavior-based detection and creates an active defense, detecting, isolating, blocking access and sending alerts for abnormal usage in real-time.

While many security solutions may provide encryption or access control services, Sotero helps you build a cohesive data security approach that goes beyond legacy encryption solutions. Sotero bundles active threat detection with an end-to-end encryption solution. Sotero is the only solution to keep data encrypted throughout its lifecycle, without the need to decrypt data for access or analysis. This technology removes the risk of having to pay ransom for stolen data, instead keeping data protected at all times.

Schedule a demo today to discover how Sotero can help your organization with a defense in depth approach to data protection.