You can use your Sumo Logic environment as a security data lake, a single repository to store all your security log and event data. With all your security data in one place, it is optimized for quick access and analysis using Cloud Security Analytics.
About data lakes
A data lake is a centralized repository to store data. Data in the lake can then be used for analytics. A data lake can store both structured and unstructured data, unlike a data warehouse, which typically only stores structured data. Data lakes are more flexible than data warehouses in that you can retrieve data in any format used by your consuming services.
Database versus data lake
“Database” and “data lake” are sometimes used in similar contexts, but they aren’t interchangeable terms. Databases require structured or semi-structured data for real-time data processing and record keeping, whereas data lakes use both structured and unstructured data for analytical workloads.
Data lakes can:
- Store structured, semi-structured, and unstructured data.
- Ingest data without having to define schema.
- Optimize performance and efficiency due to separated storage and compute functions.
What is a security data lake?
When a data lake is used to store security data, it is known as a security data lake. Sumo Logic is ideal to use for a security data lake. Our platform can search your data and deliver actionable results quickly and at scale.
Scattered data is a security risk. When it comes to quickly addressing security threats, it’s common for data to be spread throughout different tools, clouds, and functions, making it hard to access, see, and use effectively. Under these circumstances, identifying potential threats is an arduous task. To get ahead of potential threats, most organizations need detailed security data they can act on in an easy-to-access location. A security data lake is one solution.
A security data lake is a centralized repository that collects and analyzes large amounts of security data from various sources, offering a complete view of an organization’s security posture. A security data lake allows security teams to identify potential threats, investigate security incidents, and respond proactively to potential risks.
Security data lakes provide a historical overview of security events. You can proactively use this information to identify patterns and anomalies, ensuring that your data serves a valuable purpose in protecting your organization’s assets.
Retaining data in its original, raw format is also a benefit of security data lakes. This feature provides valuable information for investigation and forensic purposes, resulting in easier threat hunting and investigation through standardized querying and visualization of all data.
A security data lake allows you to store and access various data types and formats, making it easier to process and analyze data from multiple tools and technologies. Traditional solutions cannot handle such significant volumes of data, leaving valuable security logs and event data scattered across systems and tools.
What types of security logs can data lakes store?
- Firewall logs. Firewalls deliver valuable information to identify potential threats, including malware, application types, and command and control activities.
- Proxy and web filtering logs. In the absence of proxy and web filtering logs in your firewall, you must scrutinize IP, URL, and domain data to identify potential links to malicious locations. User-agent logs are also valuable in unraveling complex breaches and resolving issues.
- Endpoint security solutions. By collecting data from each device connected to your network, you can filter out false positives and focus on the real threats.
- Network security products. For standalone systems like intrusion protection or network data loss prevention, centralizing their logs with the rest of your data allows you to get fuller security coverage.
- User access. Tracking a user’s Windows authentication, single sign-on, and Active Directory are great sources to tie one user to the event in the system, even if they change IP addresses in the middle of their activity.
- Threat intelligence. Accessing logs and data from recent threats at other organizations can help you recognize similar patterns or behaviors faster.
Best practices guide to building a security data lake
Following are the basic steps involved in building a security data lake.
Step 1: Define your security data goals
Determine what data you need to analyze by considering your use case. Data may come from your security detection tools, network devices, applications, servers, and endpoints, but what data addresses your biggest concerns? For example, if you’re concerned about insider threats, collecting data on user activity and access logs is a great place to start.
Step 2: Choose Sumo Logic as your data lake solution
Sumo Logic manages vast amounts of data efficiently and adheres to compliance requirements. Certain platforms may require saving data in cold storage, which hinders your investigation capabilities. Sumo Logic offers data tiering, allowing you to store data cost-effectively without compromising accessibility.
Step 3: Implement data ingestion
Set up a process for collecting data into Sumo Logic. With Sumo Logic as your security data lake, you can begin data ingestion in a secure and compliant manner out-of-the-box. Our high event-per-second throughput ensures you can handle the most demanding workloads and data ingestion without issue.
Step 4: Enforce data security
Security controls are necessary to protect your security data lake from unauthorized access and breaches. At Sumo Logic, we employ state-of-the-art encryption techniques to safeguard your data, using AES 256 encryption at rest and TLS encryption in transit. But it’s not just about protection. You must also prioritize compliance and monitoring to ensure your security data lake meets all regulatory requirements. Sumo Logic holds numerous certifications, such as FedRAMP, HIPAA, ISO 27001, FISMA, and CSA Star, to name a few.
Step 5: Search your security data
With the vast amounts of data generated by security tools, it can be overwhelming to sift through it all. Sumo Logic's Search Query Language helps identify anomalous behavior and flags potential security threats in real-time. Query your security data lake to uncover patterns, investigate alerts, and identify potential security risks.
Step 6: Establish data governance
Sumo Logic uses role-based access control (RBAC), data classification, retention, and archiving. Our data classification tools allow you to easily identify and manage sensitive information, ensuring that only authorized users have access. And you can keep your data for as long as you need to meet regulatory requirements while also ensuring that it is easily accessible.
Step 7: Analyze security data
Once you connect your data sources in Sumo Logic, you can start analyzing security data. Cloud Security Analytics is built on a security-first principle. It is designed to analyze security data with real-time threat detection, compliance reporting, and dashboards. Our cloud-native architecture unifies security events and investigations across multiple cloud platforms, including AWS, Azure, and Google Cloud Platform.
Step 8: Monitor your data
With security data flowing in, you can continuously monitor and analyze your data to identify potential threats and respond proactively. Our platform is also updated with the latest security content from our Sumo Logic Threat Labs team. This team moves quickly to support customers in crisis, like when the Log4j vulnerability surfaced. They continuously monitor and analyze data to identify threats, both known and unknown.
Collect logs from external data lakes
You can collect logs from external data lakes and use that data in Cloud Security Analytics. See the following articles for more information:
If you want to collect logs from other data lakes, ask your Sumo Logic account representative to engage the Professional Services team to guide you through the process.