The SIEM platform can only function as well as the data it receives. This concept is known as “garbage in, garbage out.”
Real-time monitoring of devices, networks, and security hardware helps identify vulnerabilities and unauthorized activity. Correlation rules and insight into attacker tactics, techniques, and procedures help prioritize alerts.
A SIEM solution can detect and respond to complex threats that may elude traditional security tools. This includes detecting and responding to a man-in-the-middle attack that may be intercepting password inputs.
Real-time Monitoring
IT teams use security event and information management (SIEM) technology to collect and analyze data from multiple sources throughout the network. These may include servers, endpoints, applications, cloud environments, and security hardware and software like firewalls and antivirus systems. The information is then analyzed in real-time and can be sent to security teams as alerts when a threat is detected.
The first step in the SIEM process involves identifying goals and expectations for the solution. Understanding what you want from your investment and how it will fit your cybersecurity strategies is important. Once this is determined, the next step involves establishing what you will need to achieve those goals. For example, you should decide how much of your data you want to collect and whether you want the solution to provide unified detection and response (UEBA) capabilities.
Once the data is collected, it must be centralized and stored to be analyzed. Depending on your chosen solution, this may be done as an on-premise appliance or through a cloud-based SIEM-as-a-service platform. Once centralized, the information is sorted and aggregated to help make it easier for humans to read and then analyze for potential threats. If a potential threat is found, the system will generate an alert and notify IT administrators so that they can investigate further.
Log Management
The ability to collect and record log data is one of the core features of a SIEM solution. Businesses can use these records to identify cybersecurity threats based on specific behavior, such as unusual traffic or failed login attempts. By identifying the threat, companies can take action to contain it and remove it from their network.
To ensure that a SIEM solution is effective, businesses must establish specific goals for what they want the tool to do. This helps keep security teams focused and reduces the likelihood of being distracted by unnecessary alerts.
Once logs are collected and centralized, the next step is to analyze them. This involves sorting and indexing the data to make searching and finding connections between events easier. It also includes correlation, which is the process that identifies potential vulnerabilities or suspicious activities.
A top-tier SIEM solution can help by detecting patterns that indicate security threats and then alerting the security team. This is important because security professionals may not receive the necessary information without such a system and could take advantage of the opportunity to prevent a cyberattack.
Threat Detection
SIEM software collects data from the network environment, normalizes it, and makes it human-accessible so security teams can investigate any alerts. As such, it can significantly improve the mean time to detect and to respond (MTTD and MTTR) for cyber threats by offloading manual workflows and making it easier for analysts to locate incidents and determine actions to resolve them quickly.
However, how most current SIEM solutions ingest and process data can create a significant volume of alerts. This can lead to the ‘boy who cried wolf’ effect, where security teams become desensitized to the alarms they receive and don’t take action when it matters most. Enhanced SIEM technology can reduce this problem by applying contextualized alert validation to decrease the amount of false positives.
Additionally, many next-generation SIEM solutions provide threat detection capabilities to help identify specific incidents as they occur. This can include user and entity behavior analysis (UEBA), which uses machine learning to recognize patterns of behavior on the network that indicate a possible attack. It can also detect lateral movement, which is when attackers move around an organization using IP addresses, credentials, and machines to access critical systems.
Lastly, threat hunting uses machine learning to actively search for and detect anomalous activity on the network that might indicate an attack. This can be used to find undetected malware by antivirus tools or new variants of existing attacks.
Incident Response
An essential component of any effective SIEM solution is its incident response capabilities. These tools allow you to quickly locate potential threats and reduce the time it takes to respond. However, they are only as good as the data you feed them. Your team must be able to communicate and work with the tool effectively.
A key feature of SIEM is event correlation, which aggregates data from multiple resources and provides a unified analysis. This can help you gain insights into intricate data patterns and reduce the mean time to detect for faster resolution of security incidents.
In addition, an SIEM solution can detect and identify anomalous behavior that may indicate a threat. For example, a pattern of attempted logins with high failure rates might be suspicious and indicate that an attacker is probing your system for entry points. Machine learning can also identify what represents normal activity versus a deviation from it, improving detection and alerting accuracy.
Once an attack is detected, it’s important to contain and eradicate it from the network. This includes eradicating malware, identifying the vulnerabilities attackers exploited, and testing and recovering systems to return them to a healthy state. Lessons learned during this phase can be used to improve policies and procedures to minimize future incidents. This is a crucial step to ensure that your business remains protected.