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SIEM is a set of tools that combines security event management (SEM) with security information management (SIM) to detect and respond to threats that breach a network. The system aggregates data from all of the devices on a network to determine when and where a breach is happening. This way, the IT team can respond more quickly and contain the breach before it does any more damage.
For an overview of some of these top products, check out this video:
Enterprise companies with a large number of devices on their networks purchase SIEM tools, allowing them to pull log data from hardware, operating systems, applications, and security tools to monitor the network in real time. These companies also dedicate IT personnel to maintaining the system, monitoring for threats, and responding to those threats. Some common SIEM examples you might have heard of include ArcSight ESM (Enterprise Security Management), AT&T Cybersecurity (formerly known as AlienVault), Fortinet, IBM QRadar, McAfee SIEM, and Splunk.
The software’s scope and resource requirements mean that it’s not a practical option for most small businesses. Additionally, the software can cost tens of thousands of dollars per year, making it cost prohibitive for many small businesses. Thus, SIEM is usually a better solution for enterprise security.
Most companies have started to include behavioral analytics powered by artificial intelligence (AI) and machine learning in their SIEM platforms because it reveals information such as who is accessing a business’s network. AI and machine learning can look at behavior to quickly determine if someone is who they say they are. As SIEM software continues to grow, we expect to see this trend to continue and behavioral analytics to become mainstream in the world of SIEM tools.
SIEM software typically includes:
Many SIEM tools share common features to provide better security. Look for these when you’re choosing an SIEM platform for your business.
SIEM specializes in helping businesses detect threats on their network. It gathers thousands of data points from all of the devices, operating systems, enterprise security software, and applications on your network — including user behavior — to identify abnormal activities and provide actionable intelligence. SIEM keeps logs of device activity, including sites that are visited or applications that are accessed, and uses those logs to flag unusual activity. When these anomalies are detected, the system sends security alerts to the IT team, so they can perform threat intelligence and mount an incident response. Most systems handle threat detection in real time.Also read: Top 5 Security-as-a-Service Providers
Real-time security monitoring is essential to getting the most up-to-date information about your network. It significantly reduces the delays between when the threat occurs and when your SIEM system picks it up. Real-time monitoring provides faster intrusion detection, so any real threats can be detained before they can access much data. When the software detects a threat, it sends an alert to the security teams, allowing them to investigate or isolate the threat quickly.
Real-time security monitoring takes up a lot of computing power, which is another reason SIEM tools tend to be so expensive. By contrast, tools that don’t monitor in real time typically batch their monitoring. This means that cybersecurity software might only run sweeps of the network every few hours or once a day, which leaves hours of unmonitored time where threats could gain access.
While SIEM can’t stop threats on its own, it can communicate with your other cybersecurity tools to block, contain, or remove threats. While an attack is still in progress, the SIEM detects the threat and then gives your other security tools (firewalls, endpoint protection, mobile device management, etc.) instructions to keep the threat from spreading. Because SIEM platforms pull log data from so many different locations, they can identify the threats that another tool may have missed.
An SIEM solution should also include threat hunting features to track the malicious activity and make it easier for the security teams and other tools to find and remove the problem. This speeds up your incident response time and prevents breaches from causing as much damage.
Along with monitoring and detecting security threats, a solid SIEM product should also provide investigation tools, like event log correlation and log forwarding. These tools help your IT team investigate the threat, the incident response, and the outcome of the security event, so you can prevent it in the future. Event log correlation transforms data from your event logs into insights that give more context to incidents and make them easier to resolve, while log forwarding sends incident logs to other applications for further analysis. These features work together to provide more thorough threat intelligence.
By gathering event data on a security incident, your SIEM tools can help analyze and determine whether multiple failed logins was a true breach attempt or simply that someone forgot their password. Investigation tools can also help you improve your defenses against future attacks by learning about vulnerabilities that led to past breaches.
Similar to investigation tools, forensics helps your IT team analyze and investigate a security incident. However, forensics prioritizes the who, what, and when of a breach, if necessary, rather than investigating for the sake of strengthening the network. Because the forensics might become part of a police investigation, they must adhere to strict specifications. Forensics tools must:
For forensics to be valid, they have to show exactly what happened surrounding the breach, who took what actions, and what the result of those actions were. By ensuring the data is immutable, investigators have greater confidence in the threat intelligence information and can use it to find the culprit. This type of forensics can be extremely valuable for both financial institutions and government entities to identify and investigate espionage, embezzlement, or theft. With these tools, cybersecurity teams can find out where the threat originated, how much data it accessed, and any damage it caused before it was contained.
An SIEM tool with behavioral analytics can help your IT team make sure that only authorized personnel and devices are accessing your network. While user and entity behavior analytics (UEBA) currently exists as a standalone product, it’s already incorporated into many SIEM platforms and may only come as a package deal in the future. Many companies who use SIEM solutions also employ zero-trust models, meaning they only allow access to users and devices that the enterprise security team has verified. However, even if the credentials have been verified, the activity still needs to be monitored.
SIEM can flag abnormal behaviors from verified users and catch anomalies before they turn into actual breaches. Let’s say one of your employees suddenly accesses high-value data in the middle of the night from a strange location. Any one of those factors may trigger an alert, but the SIEM system would likely surface all three together as a high priority alert. You may be able to customize your SIEM tool to automatically block certain actions, like downloading files from a USB drive or accessing sensitive data sets, until the user receives admin approval. Using these security analytics, you can also figure out where breaches originated.
Most companies have started to include analytics powered by artificial intelligence (AI) and machine learning in their SIEM platforms because it reveals information such as who is accessing a business’s network. AI and machine learning can look at behavior to quickly determine if someone is who they say they are. As SIEM software continues to grow, we expect to see this trend to continue and these analytics to become mainstream in the world of SIEM tools.
While SIEM solutions are expensive, they come with a variety of benefits to keep your business network secure.
Many SIEM tools allow you to automate repetitive or labor-intensive tasks, like continuous monitoring and malware scanning, so your IT team can focus on using their expertise where it counts. You can set rulesets within the system to flag and halt suspicious behaviors. For example, you might temporarily want to lock the account of a user that tries to access data at odd hours or block new devices logging onto the network until your security operations team can verify them. When one of these cybersecurity rules is triggered, the system automatically sends an alert to your enterprise security team and initiates the incident response protocols.
Additionally, you might want to integrate your SIEM product with next-generation firewalls (NGFWs) to stop breaches or data loss prevention (DLP) tools to prevent IP theft. SIEM integration with other software increases the number of response protocols you can automate, improving your overall incident response time.
Also read: Cybersecurity Trends in 2020: Artificial Intelligence
Because SIEM monitors your network in real time and does so faster than a human, you get faster intrusion detection and response times when there are incidents in your network. Not only does the software not need to take breaks, but it also keeps historical data readily available and can quickly cross-reference code with any known malware signatures.
An SIEM tool with AI can also lower the number of false positives and provide risk assessments, so your IT team only has to worry about incidents that could actually cause harm. For example, a single incorrect password entry might cause an alert in the first couple of months after SIEM deployment. However, as the AI learns from event data and sees how your security team responds to these threats, it will have a better understanding of what actually constitutes a threat for your business, allowing it to reduce the number of security alerts for employees accidentally entering the wrong credentials.
SIEM software collects log data from all of the hardware, applications, operating systems, and cybersecurity tools on your network, providing more context to the investigations and giving you more actionable intelligence to strengthen your network. SIEM consolidates firewall logs, web filtering logs, eventlog analyzers, threat intelligence, and user authentication information, so your security operations team can examine and compare the data to find vulnerabilities. The added data also makes investigations easier because there are fewer dots to connect to determine exactly how a breach occurred. The system organizes the data in one central location and provides easy accessibility for your entire security team. This large amount of information also simplifies compliance reporting and investigations for businesses in heavily-regulated industries, such those who need to ensure PCI DSS compliance.
Traditional SIEM technology has already been around for over a decade, but it continues to provide invaluable knowledge and insights for enterprise businesses. SIEM solutions were developed to keep businesses compliant with security regulations, but today’s SIEM solutions speed up threat detection and improve investigations.
As cybercrime rises, IT security professionals face higher levels of stress and more work. In fact, 66 percent of people working in IT say they’ve considered looking for a job with less stress, and 51 percent would even take a paycut to do so. As security analysts and IT administrators face higher levels of burnout, next-generation SIEM solutions are expected to simplify their jobs and reduce their workload. As SIEM evolves, more vendors are expected to include SOAR capabilities, allowing the tool to respond to minor threats without needing help from your security team. This is meant to remove some of the strain on your security administrators, allowing them to focus on severe threats.
The future of security information management and SIEM uses AI and machine learning to improve the detection of suspicious activity and simplify IT security investigations. Not only can AI capabilities accelerate intrusion detection, but they can also automate tasks such as threat monitoring, reduce the number of false positives security teams have to deal with, and identify abnormalities in user behavior. While some tools today include these features, the ones that don’t will include them before long due to the extra functionality and improved detection rates. Additionally, the software options that currently include AI will continue to fine-tune and improve these features to provide better service and enterprise security.