How to Overcome the Drawbacks of SIEM Tools

These days, “SIEM” (Security Information and Event Management) is all over the place. SIEM tools work by collecting data from multiple systems and noticing patterns in the data. This adds immediate value to the business by providing insights, security recommendations, and actionable intelligence.

Despite being helpful tools for many companies, SIEM tools do have their drawbacks. This article will describe the four main ones and offer suggestions for how they might be overcome.

Common Pitfalls in SIEM Tools

The best SIEM approach leverages a few new things in the industry to overcome their pitfalls. The best examples include recent trends in data storage, computing flexibility, and easier ML algorithms. This is at the core of a winning, modern SIEM security strategy.

What should you look for when selecting (or building) a SIEM platform for an organization? How can you identify the solutions that might fall short? The answers to these questions depend on a number of factors which can be challenging to grasp. In this article, we will divide these factors into four different categories and examine each of them. These categories are: scalability and compliance, security and threat intelligence, automation and suggested actions, and data analysis and incident response.

Scalability and Compliance

The first generation of SIEM tools were expensive and deployed for on-prem data. They lacked ready integrations and advanced intelligence capabilities. Back then, rolling out a SIEM product company-wide was a project that took months and tended not to add much business value.

Two key challenges for these old solutions were 1) ingesting data from many sources (usually custom-made integrations) and 2) dealing with booming storage demand.

Modern cloud-based SIEM tools overcome the drawbacks of scalability and also handle data growth. Either an SaaS or a solution that is deployable in your cloud environment will be your best bet. It is important to find a solution with connections to popular enterprise software. They should also offer custom REST API integrations. These features solutions make it easier to start ingesting data and to modify settings based on a company’s wishes.

Adopting a SIEM application in highly regulated environments or organizations handling sensitive data that must meet certain compliance standards (e.g., GDPR, CCPA, PCI DSS, and ISO 27001) poses additional challenges. When purchasing a SIEM tool, make sure that it’s already got certification with the required compliance programs. This’ll ease the certification process for you and get you up and running faster.

Security and Threat Intelligence

SIEM products should be able to juggle handle large amounts of data. But, their true value comes from having meaningful, quality data and security intelligence.

At first, traditional SIEMs’ design focused on using security intel to expose threats to IT infrastructure. And note, those threats were mostly external. This focus on external threats presents a huge drawback for a security solution because it’s becoming apparent that exposing external threats is not enough. New SIEMs need to take into account all elements—internal and external—that can compromise application security in accordance with the Zero Trust Security Model.

Taking all threats into account is a harder security challenge, and a good SIEM tool requires more than just the data collected by the organization to do this effectively. Given the rapidly evolving threat landscape, an effective SIEM enriches the data it analyzes with additional data—metadata from third party security feeds.

Take, for instance, For an unknown IP seen across a company’s data. A newer SIEM setup can look it up, then add up-to-date reputation info about that address to enrich its analysis of the IP.

Today’s fast-paced and rapidly evolving threat landscape poses an incredibly complex challenge for security solutions such as SIEMs. Detecting threats is often a cat and mouse game, and, to complicate things further, each organization’s environment is unique. The best SIEM software identifies and often predicts threats by looking at behavior patterns. One of the major SIEM trends is UEBA, or User and Entity Behavior Analytics. A SIEM capable of detecting and predicting anomalies, trends, and malicious patterns, as well uncovering hidden relationships based on the behavior of users and entities (i.e., systems) will excel at providing insightful information and have an upper hand in the marketplace.

Security Automation and Recommendations

SIEM software has traditionally fallen short in their ability to deliver recommendations and actionable intelligence. Historically, security researchers and analysts were the exclusive users of used SIEM tools. After all, most of the data needed expertise to understand clearly. That limit led to another pitfall: SIEM vendors were designing software that streamed mostly irrelevant alerts. In other words, productivity tanked because of information overload and analyst fatigue.

Today, SIEM security tools are more inclusive in accessibility and exclusive in alerts. They let non-security experts use them meaningfully. They will also generate more relevant alerts.

Additionally, instead of only displaying data or providing alerts, SIEMs will suggest a course of action.

For example, consider a SIEM that has been analyzing traffic behavior patterns. This SIEM application might recommend restricting inbound access to a given API endpoint if it finds over-exposure to the public internet. A next generation SIEM solution can go even further, including automatically remedying the issue.

To accomplish these tasks, SIEM now includes out-of-the-box integrations. Many of those integrations are with major enterprise programs like Salesforce, Kubernetes, AWS, and Slack. They also give users the means to customize their own.

Look for a SIEM with behavior-based security intelligence and analytics capabilities. It’s crucial that it provides meaningful alerts and relevant recommendations as opposed to simply gathering data and providing irrelevant information.

Data Analysis and Incident Response

With traditional SIEM solutions, serious incidents might go under the radar because irrelevant alerts distracted admins. Add to that that these older security programs did not have prediction-based alerts, much less recommendations, to act on.

Today, SIEM solutions need the ability to dive deep into data, then retrace steps to gather additional info. Integrations are vital here, n both data analysis and incident response. SIEM solutions should be linked to or part of the company’s standing infrastructure, including monitoring and incident management tools. Once it integrates, the data will be directly accessible to your business intelligence tools.

You can also export or replicate it to your organization’s data lake. That syncs it with other tools your company uses. From there, employees can use the collected data.

Conclusion

SIEM capabilities have evolved significantly in recent years, and, as a result, SIEM solutions have become very popular. Whether you are developing SIEM capabilities on your own or looking to purchase a SIEM solution, it is important to implement one that offers real business value.

As we discussed, there have traditionally been several drawbacks to SIEM solutions; however, they can be easily overcome with modern SIEM solutions. A cloud-based SIEM solution that can handle data growth, scale on demand, and meet compliance requirements is your best option.

It is equally important to have behavior-based security intelligence that can act on the ingested data to provide actionable tips. A SIEM solution is no longer a tool exclusively for security researchers and information security analysts. The implementation of a SIEM tool can benefit any company by predicting security threats, enabling data exploration, and leveraging business intelligence capabilities.