Providing input on key assets or users/departments can increase the risk score accordingly (for example, data exfiltration involving staff in your organization’s research lab may be more of a threat than data theft on your marketing team). Last but not least, you’ll need to customize your anomaly scoring models. The needs and objectives for retail, e-commerce, financial services, the public sector, etc., vary immensely. Remember, while most UBA solutions will provide use cases that can apply to almost every customer in the form of rule sets, they aren’t necessarily priorities of your business. You should also involve your organization’s security architects and engineers so they can provide feedback. This can be anything from data exfiltration to account compromise and misuse. Next, you’ll need to determine what use cases you’d like to address with your UBA tool. By hammering out a streamlined investigation and feedback loop, security teams won’t have to scramble when trouble comes. Integrated workflows allow security teams to quickly respond to incidents by opening tickets or placing surveillance on high-risk users. Once you’ve successfully prepped your environment and onboarded relevant event data, the next step is to configure your workflows. Your tool should provide a number of options for identifying, validating, then ingesting data from multiple sources across your infrastructure - from reading simple, well-known log file formats, to invoking programs to handle custom data formats. Getting data into your UBA solution is the first step toward understanding your user data. Generally, you’ll start using UBA with four basic steps: ingest data, configure workflows, target use cases, and customize your models. Check out Gartner’s “ Reviews for User and Entity Behavior Analytics Solutions” to learn about industry leaders in this space, and how they can help you take your security journey to the next level. Ultimately, choosing the best UBA tool for your organization depends on your set of priorities and concerns. You can also detect behavior-based irregularities (e.g., unusual machine access, unusual network activity) or pinpoint botnet or command and control activity (e.g., malware beaconing, etc.) and much more. Malware and insider detection and attack vector discovery: This means you can detect movement of malware across apps and devices, as well as malicious insider proliferation, in real time.Leveraging machine learning algorithms, statistics and anomaly correlations improves your ability to identify malicious insiders without human analysis. Streamlined threat workflow: Reducing billions of raw events to thousands of anomalies, then to mere hundreds of threats, speeds up review and resolution.Anomaly scoring creates a methodology around an array of events to better improve accuracy as well as indicate the intensity of a threat. User feedback learning: With user feedback learning, security teams can customize anomaly models based on the organization’s processes, policies, assets, user roles and functions.Threat review and exploration: This allows the user to visualize a broad range of suspicious behavior and gain context across multiple sources, including users, accounts, devices and applications.When choosing a UBA tool, weigh four key features: threat review, user feedback learning, streamlined workflow and kill-chain detection. Put simply, organizations can improve detection speed, analyze impact and respond quickly to any security incident. With machine learning, analysts and security operations center (SOC) teams can perform rapid investigations, find meaningful insights, determine the root cause of an incident, draw on historical trends and share findings without being bogged down by thousands of alerts and false alarms. By stitching together threat indicators detected by a variety of algorithms, machine learning helps the software or solution identify high-probability threats. The result is automated, accurate threat and anomaly detection. This provides a far more nuanced monitoring and response capability for UBA. The threat detection capabilities in a UBA solution can correlate anomalies across multiple data sources within any environment that generates machine data.Īnalytics tools based on machine learning methodologies require no signatures or human analysis, enabling multi-entity behavior profiling and peer group analytics. Machine learning plays a critical role in UBA and is absolutely key to powering a scalable data platform that supports advanced analytics. What is the role of machine learning in UBA?
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