Introduction
Have you ever thought about how to protect your business from threats? Whether you want to stop internal or external threats, Splunk has the solution. With the User Behavior Analytics (UBA) tool, you can prevent threats by uncovering them by analyzing how a user or entity behaves and comparing them against past analytics. Splunk offers a complete overview of Features and integrations and even helps you with resources so that you can get started right now and not waste a second when protecting against threats.
So, how does Splunk UBA Work?
The Splunk UBA tool is part of Splunk Security, Enterprise Security, SOAR (Security Orchestration, Automation, and Response), and Mission Control. The main job of UBA is to detect user behavior threats to see if any unauthorized activity could threaten the organization. These four parts of Splunk Security often work together to provide a complete view of an organization’s threats, but Splunk’s UBA tool offers its own set of detection capabilities.
Increase threat detection abilities to detect threats and anomalous behavior using machine learning by detecting cyberattacks and insider threats. Boost productivity by automating the stitching of hundreds of detected anomalies into a single threat to simplify incident investigation. Accelerate threat hunting by investigating behavior baselines and any strays from that baseline.
UBA helps you increase security analysis effectiveness by reducing the number of false positives and prioritizing threats. Sophisticated cyberattacks can be complex to find but must be responded to quickly eliminate them from becoming significant threats. Splunk UBA helps you find known and unknown threats using multi-dimensional behavior baselines, group analysis, and machine learning. This detects anomalies in data while making sure it requires little monitoring and administration so that your team does not have to waste time checking up on Splunk.
Using machine learning methodologies to perform this analysis eliminates the need for human analysis, resulting in automated and accurate threat detection, called behavior-based threat detection. The entire lifecycle of security operations (prevention, detection, response, mitigation, and ongoing feedback) is unified so that all aspects of Splunk Security can work together.
What are some features of Splunk UBA?
There are four key features that Splunk UBA offers and four different ways Splunk UBA can help you. When talking about features, here are the highlights:
- Streamlined threat workflow reduces billions of raw events to tens of threats through machine learning algorithms to identify hidden threats without human analysis.
- Threat review and exploration create threat visualization using machine learning to stitch anomalies across users, accounts, devices, and applications to see threat patterns.
- User-feedback learning creates models based on an organization’s processes, policies, assets, and user roles to provide feedback on individual activities and see if they poses a threat or are supposed to happen.
- Kill chain detection and attack vector discovery detect lateral movement of malware or malicious software to respond to real-time anomalous activity and detect irregular user behavior such as unusual machine access or network activity.
When talking about how Splunk UBA can help you, here are the highlights:
- Incident response to defend your organization’s infrastructure when threats are detected and discover weaknesses in the existing configuration of your systems.
- Compliance with requirements such as GDOR, HIPAA (Health Insurance Portability and Accountability), PCI, and SOX by creating rules and reports to identify and stop threats against sensitive data before they occur.
- Fraud analytics and detection by introducing new data to train the system to detect when data is not supposed to be there to help reduce financial losses and protect the organization’s reputation.
- Insider threat detection is when former employees, contractors, or partners have access to sensitive data and either accidentally or deliberately misuse or destroy said data. Splunk uncovers threats posed by insiders that may otherwise go undiscovered.
What Goes into a Successful Behavior Analytics Program?
There are four components of a successful behavior analytics program. These four steps build on each other to help create a comprehensive guide. Splunk Security and User Behavior Analytics are designed to work together in this program.
Step 1 – Ingest: Use Log Data to get results to determine addressable use cases. Splunk is known as a Data-to-Everything platform because it provides insights from machine data no matter the source or format. This allows security analysts to determine the cause of any issues and make informed decisions on how to stop them.
Step 2 – Alert: You can set thresholds, statistics, and machine learning to trigger alerts, and it is necessary to act on each alert. For example, suppose someone has suddenly tried to print out 100 pages of a document. This may be determined as ‘abnormal’ behavior because you can evaluate the standard behavior limit. You can track who is trying to print the 21 pages and compare it to the number of pages they usually print. If the user normally prints 100 pages per day, this would not be flagged, but if they print 20, it would be.
Machine learning allows organizations to solve advanced use cases that may not have been solved before. Splunk UBA’s scalable machine learning detection capabilities help you enhance overall functionality with statistics that produce higher confidence detections and provide a backstop to approach detections from different perspectives.
Step 3 – Aggregate: You act on the alerts triggered by the previous step. Accurate detection is about stitching all alerts and anomalies into a unified view. The prerequisite for this step is that a simple detection mechanism using thresholds and statistics has been implemented. You cannot aggregate data without setting alerts.
Splunk approaches aggregation by running a series of ML-powered threat models over the anomalies to identify threats. Through machine learning, UBA helps organizations detect insider and external threats, leverage a custom use framework to generate custom content and create use cases, and provide a context around the threat through visual mapping of the stitched anomalies.
Step 4 – Investigate: Once you have determined your threats and seen them in one unified view, you can investigate aggregated alerts to decide what to do. Any security detection requires an investigation, especially as attacks become more sophisticated. Splunk UBA provides you with context to quickly understand the current threats to stop it from advancing any further. Using dashboards and form search, Splunk UBA gives analysts access to information to have all the data necessary to prevent attacks.
Splunk UBA works with Enterprise Security to help with real-time data and threat monitoring by getting a clear picture of the organization’s security posture to customize views. It also prioritizes threats by gaining a security-specific view of data to increase detection capabilities and optimize incident response. It uses rapid investigation using ad-hoc search dynamic and visual correlations to determine malicious activities. It also handles multi-step investigations by conducting breach and investigative analyses to trace dynamic activities associated with advanced threats.
Conclusion
Do not make the mistake of waiting until a threat strikes before addressing vulnerabilities in your security posture. Strong security has become necessary, and Splunk’s security solutions are built to help you act on data from any source at any time and speed.
Adopting a user and entity behavior analytics tool like Splunk User Behavior Analytics, powered by a data-to-everything platform and integrated easily with Splunk Enterprise Security, provides many benefits.
Contact Prudent now to make Splunk UBA integration the easiest thing you have ever done and get your quote now!