Insider Threat Monitoring and Hunting with DataBee

Enterprises have an ever-growing asset and user population. As organizations become more complex thanks to innovation in technology, it becomes increasingly difficult to track all assets in the environment. It's difficult to secure a user or device you may not be aware of. DataBee can augment and complement your configuration management database (CMDB) by enhancing the accuracy, relevance, and usability of your asset, device, and user inventory, unlocking 3 primary use cases for security teams:   Security Hygiene  Owner & Asset Discovery   Insider Threat Hunting  Security Hygiene  DataBee for Security Hygiene can help deliver more accurate insights into the assets in your environment while automatically keeping your asset inventory up-to-date and contextualized. Bringing more clarity about your users and devices in your environment, enables your business to enhance its security coverage, reduces manual processes, increases alerts’ accuracy, and more rapidly responds to incidents.   Security hygiene uses entity resolution, a patent pending technology from Comcast, to create a unique identifier from a single data source or across multiple data sources that refer to the same real-world entity, such as a user or device. This technique is performed by DataBee helps reduce the manual entity correlation efforts by analysts, and the unique identifiers can be used to discover assets and suggest missing owners.  DataBee supports ingestion from multiple data sources to help keep your security hygiene in check, including a variety of traditional sources for asset management such as your CMDB, directory services, and vulnerability scanner. It also can learn about your users and devices from non-traditional data sources such as network traffic, authentication logs, and other data streamed through DataBee that contains a reference to a user or device.   DataBee can also be used to exclude data sources and feeds from entity resolution that provide little fidelity for entity-related context and support feed source prioritizations. These inputs are used when there is collision or conflicting information. For example, if a device is first seen in network traffic, preliminary information about the device will be added to DataBee such as the hostname and IP. Another example is when your CMDB updates a device’s IP. DataBee will use this update to overwrite the associated IP if the CMDB feed is prioritized over the network traffic information.   Owner and Asset Discovery  Organizations often struggle to assign responsibility for resolving security issues on assets or devices, many times due to unclear ownership caused by gaps in the asset management process, such as Shadow IT and orphaned devices. DataBee can provide a starting point to identifying and validating the owner of a device. When a new device is discovered or the owner is not indicated in CMDB, DataBee will leverage the events streaming through the platform to make a suggestion of the potential owners of the asset. The system tracks who logs into the device for seven days after discovery and uses statistical analysis to suggest up to the three most likely owners.   The Potential Owners are listed in the Entity Details section of the Entity View page that can be expanded to quickly validate the ownership. Once security analysts are able to validate the owner, the system of record can be updated and DataBee will receive the update.  As organizations become more complex thanks to innovation in technology, it can become increasingly difficult to track all assets in the environment. DataBee provides User and Device tables to supplement existing tools. Orphaned assets remain unknown and often unpatched, creating unmonitored levels of increasing risk. Shadow IT is a growing problem as more cloud-based solutions become easily accessible. Entity resolution maintains an inventory of known assets in your organization. This enables continuous discovery of assets that would otherwise slip through the cracks based on events streamed through DataBee.  Insider Threat Hunting  Insider threats are people, such as employees or contractors, with authorized access to or knowledge of an organization’s resources, that can cause potential harm arising from purposeful or accidental misuse of authorized user access. This can have negative effects on the integrity, confidentiality, and availability of the organization, its data, personnel, or facilities. Insider threats are often able to hide in plain sight for many reasons. There is complexity in cross-correlating logs of an individual's activities across various tools and products. Further, the data is siloed, making it difficult for security analysts to see the full picture. DataBee’s security, risk, and compliance data fabric platform weaves together events as they stream through the business context needed to identify insider threats.    DataBee leverages entity resolution to create an authoritative and unique entity ID using data from across your environment that is mapped to real people and devices to enable hunting for insider threats in your organization. Entity resolution aggregates information from multiple data sources, merges duplicate entries, and suggests potential owners for devices and assets. By correlating and enriching the data before storing it in your data lake, DataBee creates an entity timeline, associating each event with the correct entity at the time of its activity. These views enable insider threat hunting by allowing security analysts to see the activities conducted by a user and the related business context in a single view to identify potential malicious behavior. The interactive user experience is intended to make leveraging the Open CyberSecurity Framework (OCSF) formatted logs more accessible to all security professionals. Within the Event Timeline, security analysts can filter the events based on type to focus investigations. Clicking on the event will show the mission critical fields needed to decide if the event is interesting. Clicking on the magnifying glass icon allows you to inspect the full event. DataBee enables one-click pivoting to related entities to simplify diving deeper into the investigation. The views are powered by the data in the data lake. Therefore, the data is available in OCSF format for threat hunters to continue their investigations in traditional tools like Jupyter notebooks to meet hunters where they are with their data.   Take a look under the hood of DataBee v2.0 and DataBee for Security Hygiene  Are you ready for an enterprise-ready security, risk, and compliance data fabric? Request a custom demo to see how DataBee uses a unique identifier that can be used to augments your CMDB and help deliver more accurate insights into the assets in your environment.

Last week, DataBee™ announced the general availability of DataBee v2.0. Alongside a new strategic technology partnership with Databricks, we released new cybersecurity and Payment Card Industry Data Security Standard (PCI DSS) 4.0 reporting capabilities. In this blog, we’ll dive into the new security threat use cases that you can unlock with a security, risk, and compliance data fabric platform. DataBee for security practitioners and analysts In security operations, detecting incidents in a security information and event management (SIEM) tool is often described as looking for a needle in a haystack of logs. Another fun (or not-so-fun) SIEM metaphor is a leaky bucket. In an ideal world, all security events and logs would be ingested, parsed, normalized, and enriched into the SIEM, and then the events would be cross-correlated using advanced analytics. Basically, logs stream into your bucket and the SIEM, and all the breaches would be detected. In reality, there are holes in the bucket that allow for undetected breaches to persist. SIEMs can be difficult to manage and maintain. Organization-level customizations, combined with unique and ever-changing vendor formats, can lead to detection gaps between tools and missed opportunities to avert incidents. Additionally, for cost-conscious organizations, there are often trade-offs for high-volume sources that leave analysts unable to tap into valuable insights. All these small holes add up. What if we could make the security value of data more accessible and understandable to security professionals of all levels? DataBee makes security data a shared language. As a cloud-native security, risk, and compliance data fabric platform, DataBee engages early in the data pipeline to ingest data from any source, then enriches, correlates, and normalizes it to an extended version of the Open Cybersecurity Schema Framework (OCSF) to your data lake for long-term storage and your SIEM for advanced analytics. Revisiting the haystack metaphor, if hay can be removed from the stack, a SIEM will be more efficient and effective at finding needles. With DataBee, enterprises can efficiently divert data, the “hay,” from an often otherwise cost-prohibitive and overwhelmed SIEM. This enables enterprises to manage costs and improve the performance of mission-critical analytics in the SIEM. DataBee uses active detection streams to complement the SIEM, identifying threats through vendor-agnostic Sigma rules and detections. Detections are streamed with necessary business context to a SIEM, SOAR, or data lake. DataBee takes to market a platform inspired by security analysts to tackle use cases that large enterprises have long struggled with, such as: SIEM cost optimization Standardized detection coverage Operationalizing security findings SIEM cost optimization Active detection streams from DataBee provide an easy-to-deploy solution that enables security teams to send their “needles” to their SIEM and their “hay” to a more cost-effective data lake. Data that would often otherwise be discarded can now be analyzed enroute. Enterprises need only retain the active detection stream findings and security logs needed for advanced analytics and reporting in the SIEM. By removing the “hay,” enterprises can reduce their SIEM operating costs. The optimized cloud architecture enables security organizations to gain insights into logs that are too high volume or contain limited context to leverage in the SIEM. For example, DNS logs are often considered too verbose to store in the SIEM. They contain a high volume of low-value logs due to limited information retained in each event. The limited information makes the DNS logs difficult to cross-correlate with the disparate data sources needed to validate a security incident. Another great log source example is Windows Event Logs. There are hundreds of validated open-source Sigma detections for Windows Event Logs to identify all kinds of malicious and suspicious behavior. Leveraging these detections has traditionally been difficult due to the scale required both for the number of detections and volume of data to compare it to. With DataBee’s cloud-native active detection streams, the analytics are applied as the data is normalized and enriched, allowing security teams new insights into the potential risks facing their organization. DataBee’s power and scale complement the SIEM’s capabilities, plugging some of the holes in our leaky bucket. Analyst fatigue can be lessened by suppressing security findings for users or devices that can reduce reliability of a finding. With DataBee’s suppression capability, you can filter and take actions on security findings based on the situation. Selecting “Drop” for the action ignores the event, which is ideal for events that are known to be false positive in the organization. Alternatively, applying an “Informational” action reduces the severity and risk level of the finding to Info, still allowing the finding to be tracked for historical purposes. The Informational level is perfect for tuning that requires auditability long term. The scheduling option uses an innovative approach that gives you a way to account for recurring known events like change windows that might fire alerts or additional issues that could lead to false positives.  By applying the analytics and tuning to the enriched logs as they are streamed to more cost-effective long-term storage in the data lake, security teams can detect malicious behavior like PowerShell activity or DNS tunneling. Additionally, DataBee’s Entity Resolution not only enriches the logs but learns more about your organization from them, discovering assets that may be untracked or unknown in your network. Standardized detection coverage With the ever-evolving threat landscape, detection content is constantly updated to stay relevant. As such, security organizations have taken on more of a key role in content management between solutions. Compounded by the popularization of Sigma-formatted detections with both security researchers and vendors, many large enterprises are beginning their journey to migrate existing custom detections to open-source formats managed via GitHub. Sigma detection rules are imported and managed via GitHub to DataBee to quickly operationalize detection content. Security organizations can centralize and standardize content management for all security solutions, not just DataBee. Active detection streams apply Sigma rules, an open-source signature format, over security data that is mapped to a DataBee-extended version of OCSF to integrate into the existing security ecosystem with minimal customizations. DataBee handles the translation from Sigma to OCSF to help lower the level of effort needed to adopt and support organizations on their journey to vendor-agnostic security operations. With Sigma-formatted detections leveraging OCSF in DataBee, organizations can swap out security vendors without needing to update log parsers or security detection content. Operationalizing security findings One of DataBee’s core principles is to meet you where you are with your data. The intent is to integrate into your existing workflows and tools and avoid amplifying the “swivel chair” effect that plagues every security analyst. In keeping with the vendor-agnostic approach, DataBee security findings generated by active detection streams can be output in OCSF format to S3 buckets. This format can be configured for ingestion to immediate use in major SIEM providers. Leveraging active detection streams with Entity Resolution in DataBee enables organizations to identify threats with vendor-agnostic detections with all the necessary business context as the data streams toward its destination. DataBee used in conjunction with the SIEM allows security teams visibility out of the box into potential risks facing their organization without the noise.