Download PDF
Digital government solutions security team gains unparalleled visibility with Sumo Logic
Technology Category
- Cybersecurity & Privacy - Network Security
- Analytics & Modeling - Predictive Analytics
- Analytics & Modeling - Real Time Analytics
Applicable Functions
- Business Operation
- Quality Assurance
Use Cases
- Predictive Maintenance
Services
- System Integration
- Cybersecurity Services
- Training
The Challenge
The government solutions provider’s security team faced significant challenges in maintaining visibility across multiple remote sites. Without addressing the problem at each remote office, it was impossible to detect if an attacker was targeting one office and then expanding efforts throughout the company. The team lacked access at the network level and had no practical way of identifying such threats in real-time. Analysts had to painstakingly go through historical packet capture data to search for past attack patterns while also managing new threats. Despite leveraging AI and machine learning in their antivirus solutions, they needed a more effective way to improve threat detection and network visibility.
About The Customer
The customer is a top digital government solutions provider in the United States, responsible for delivering cutting-edge cybersecurity technology to various government entities. The company’s security team oversees governance across all business units, continuously assesses the current security posture, and hunts for and responds to threats. The team operates in a highly distributed network environment, making deep network visibility a critical component for success. The company is known for its early adoption of advanced technologies, including artificial intelligence and machine learning, to enhance its cybersecurity measures.
The Solution
The security team turned to Sumo Logic’s Cloud SIEM Enterprise solution to address their network visibility issues. Sumo Logic’s ability to create metadata and query traffic in a SQL-type format was identified as a powerful tool for threat hunting. The solution provided real-time data traffic insights and visual representations of patterns and timelines, enabling quick trend analysis. The team integrated Sumo Logic with Carbon Black to enrich threat alerts with additional context, focusing on high-priority indicators. The implementation was swift, with the platform monitoring the corporate headquarters within an hour and expanding to other remote sites in the following days. This deployment allowed the team to uncover threats that were previously undetectable, such as a virus-infected HVAC system communicating with an external server.
Operational Impact
Quantitative Benefit
Related Case Studies.
Case Study
Remote Monitoring & Predictive Maintenance App for a Solar Energy System
The maintenance & tracking of various modules was an overhead for the customer due to the huge labor costs involved. Being an advanced solar solutions provider, they wanted to ensure early detection of issues and provide the best-in-class customer experience. Hence they wanted to automate the whole process.
Case Study
Predictive Maintenance for Industrial Chillers
For global leaders in the industrial chiller manufacturing, reliability of the entire production process is of the utmost importance. Chillers are refrigeration systems that produce ice water to provide cooling for a process or industrial application. One of those leaders sought a way to respond to asset performance issues, even before they occur. The intelligence to guarantee maximum reliability of cooling devices is embedded (pre-alarming). A pre-alarming phase means that the cooling device still works, but symptoms may appear, telling manufacturers that a failure is likely to occur in the near future. Chillers who are not internet connected at that moment, provide little insight in this pre-alarming phase.
Case Study
Aircraft Predictive Maintenance and Workflow Optimization
First, aircraft manufacturer have trouble monitoring the health of aircraft systems with health prognostics and deliver predictive maintenance insights. Second, aircraft manufacturer wants a solution that can provide an in-context advisory and align job assignments to match technician experience and expertise.
Case Study
Integral Plant Maintenance
Mercedes-Benz and his partner GAZ chose Siemens to be its maintenance partner at a new engine plant in Yaroslavl, Russia. The new plant offers a capacity to manufacture diesel engines for the Russian market, for locally produced Sprinter Classic. In addition to engines for the local market, the Yaroslavl plant will also produce spare parts. Mercedes-Benz Russia and his partner needed a service partner in order to ensure the operation of these lines in a maintenance partnership arrangement. The challenges included coordinating the entire maintenance management operation, in particular inspections, corrective and predictive maintenance activities, and the optimizing spare parts management. Siemens developed a customized maintenance solution that includes all electronic and mechanical maintenance activities (Integral Plant Maintenance).
Case Study
Asset Management and Predictive Maintenance
The customer prides itself on excellent engineering and customer centric philosophy, allowing its customer’s minds to be at ease and not worry about machine failure. They can easily deliver the excellent maintenance services to their customers, but there are some processes that can be automated to deliver less downtime for the customer and more efficient maintenance schedules.
