Every day, cybersecurity teams leverage the strategies and tools at their disposal to fight off potential attacks. In many cases, this means paying attention to third-party risks. Third parties often represent the weakest links in the cybersecurity chain. So it is important that security teams do everything they can to minimize the risks posed by such entities. Enter behavioral analytics.
Behavioral analytics is the practice of monitoring network and cloud behavior for the purposes of detecting anomalies. By monitoring and analyzing data, a behavioral analytics system can help identify potential instances of stolen credentials and unauthorized network access.
Stolen Credentials Are a Common Problem
Stolen credentials are a common problem in the digital era. It is so common that organizations like DarkOwl continually scan the dark web looking for credentials being bought and sold among criminals. Scanning for stolen credentials is a routine part of darknet intelligence gathering.
It turns out that darknet intelligence data can be merged with behavioral analytics to more accurately identify and confirm network activity that could be the result of stolen credentials being used by hackers. Fortunately, behavioral analytics can do a lot more.
Core Mechanisms and Their Relation to Third-Party Risk
Behavioral analytics can be applied to just about any type of network behavior. But specifically in the third-party risk department, security teens rely on three core mechanisms:
- Baseline Behavior – Systems leverage historical data to create baseline behavioral profiles for all verified users. Profiles include tasks normally performed, like paying invoices and verifying payroll. Both tasks and the amount of time taken to complete each one are profiled and patterns are developed.
- Anomaly Detection – Systems compare baseline behavioral data with network activity in real-time. The goal is to detect anomalies that would indicate fraudulent activity. Anomalies include things like unusual transaction locations, access from odd IP addresses, and inconsistencies in data entry.
- Ongoing Adaptation – Systems are equipped with advanced algorithms that allow them to artificially learn and adapt on-the-go. The more historical data they have to work with, the better they are at identifying anomalies.
When you understand the core mechanisms of applying behavioral analytics to third-party risk mitigation, it is easy to see how combining it with darknet intelligence data can go a long way toward preventing third-party breaches.
Helping Third Parties Improve Their Own Security
DarkOwl reminds customers that the combination of threat intelligence data and behavioral analytics doesn’t just help them, it can help their third-party partners as well. An organization can use what it learns from both to help vendors, contractors, and suppliers improve their own security as well.
For example, identifying stolen credentials should motivate a partner to beef up its own internal security so that data breaches do not occur in the future. Another potential benefit is combining darknet intelligence data with behavioral analytics to find synthetic identities and fabricated personas.
Financial institutions, like banks and credit card companies, can protect against third parties being used as money mules by cyber criminals trying to cover their tracks. Both behavioral analytics and darknet intelligence data can track unusual financial movements that seem inconsistent with how affected third parties normally do business.
Whatever It Takes to Stop Threats
Threat actors in the cybersecurity realm are no different than any other kind of criminal. They prey on the weakest link. Unfortunately for third parties, they tend to be the weakest links in any network or cloud environment.
Managing the risk those third parties pose is paramount to maintaining network security. From the security team’s perspective, whatever it takes to stop threats is on the table. That includes behavioral analytics.
