Strengthen IT security with an AI-driven SIEM

The use of SIEM (Security Information and Event Management) software provides the business with threat monitoring, event correlation, incident response, and reporting. SIEM collects, centralizes, and analyzes log data through enterprise technology, including applications, firewalls, and other systems. It then alerts your IT security team to failed logins, malware, and other potentially malicious activity.

However, over the years, SIEM has barely evolved beyond the ability to provide a better, more queryable, rules-based logging engine. The marriage of recent artificial intelligence (AI) and machine learning (ML) technologies with cybersecurity tools promises a glorious future.

In 2016, Gartner coined another new term, artificial intelligence for IT operations, or AIOps. AI-based algorithms and machine learning coupled with predictive analytics are quickly becoming a core part of SIEM platforms. These platforms provide automated, continuous analysis and correlation of all activity observed in a given computing environment. This integration gives SIEM deep learning capabilities and a myriad of built-in tools to achieve more informed results.

Here are the advantages of such an integrated SIEM.

Read also : AIOps Trends and Benefits for 2021

Sneak attack prevention

Typical SIEM analytics correlate events from different sources collected over a relatively short period of time (usually hours and days). This, against the baseline of an infrastructure, will produce a priority alert if they exceed predefined thresholds. AIOps represent systems that store event information gathered over a long period of time (perhaps years) in a database and then apply analytics to that data.

These analytics allow AIOps to adjust the infrastructure baseline and adjust alert thresholds over time, as well as automatically taking certain corrective actions based on correlated events. Additionally, the use of Big Data gives SIEM the ability to detect even very slow or stealthy activities on a network that SIEM would otherwise miss or reject as a one-off. By detecting these slow or stealthy activities, a security team can prevent a major security incident.

Threat detection

In addition to offering standard log data, AI and machine learning technologies can also integrate threat intelligence feeds. Some products may also offer advanced security analysis features that examine both user and network behavior. Machine learning enables your SIEM to make it easier to detect threats on large datasets, which relieves some of the threat-hunting responsibilities of your security team. Threat Intelligence provides information about the likely intent of individual IP addresses, websites, domains, and other entities on the Internet. This allows them to distinguish “normal” activity from malicious activity.

Providing your SIEM with continuous access to one or more threat intelligence feeds enables machine learning technologies to use the context provided by threat intelligence. And as he learns more, he begins to understand the warnings of malicious behavior beyond his initial data entry. Therefore, it can stop threats that your cyber security has never seen before. It improves SIEM decision making, especially in terms of accuracy, helping to deepen your security layers.

There is a caveat, however. Machine learning works better on larger datasets than on smaller ones, but because big data is lossy, it can complicate compliance reporting. But since this is a known issue, there are several workaround options available.

Read also : What is SIEM software and how can it protect your business?

Eliminate data noise

A typical SIEM provides a massive amount of monitoring data/logs, but SIEM reporting data is not actionable, difficult to understand, and too noisy. An AI-integrated SIEM solution efficiently manages big data and can replace repetitive and redundant tasks with automated workflows.

Although most AI programs make it easy to classify data, the AI ​​element is not capable of grouping unrecognizable data points and event information. On the other hand, machine learning can take advantage of data clustering capabilities to identify these unknown values ​​and group them into categories based on detected similarities.

Eliminate blind spots as the business evolves

As a business grows, it becomes more susceptible to the appearance of blind spots. And every blind spot can go unattended for months or even years at a time. Therefore, these parts of the network may go uncorrected for long periods of time. These blind spots additionally become an ideal infiltration spot for hackers to plant habitation threats.

Fortunately, AI in SIEM can help improve your network visibility, quickly and periodically uncovering blind spots in your networks. It can also pull security logs from those newly discovered blind spots, expanding the reach of your SIEM solution.

Read also : Steps to improve your data architecture

Improved responsiveness of the IT security team

Security Operations Center (SOC) teams in any enterprise are limited and the amount of log data generated from any SIEM is quite extensive. This makes the challenge of dealing with incidents in a responsive and efficient manner extremely daunting. Additionally, many SIEM tools also provide a lot of unrelated data, causing SOC teams to deal with alert fatigue.

This situation occurs when you process too many alerts and you don’t know which alerts to pay attention to and ignore. The automated and standardized workflows provided by ML can reduce the risk of human error and get the job done much faster.

SIEM also requires constant monitoring by your IT security team. Manually monitoring every checkpoint in the system is not only exhausting, but will also lead to burnout. SIEM supported by ML capabilities can offer:

  • Self-learning to automate repetitive and unstructured processes
  • The ability to automate system alerts
  • Data visualization dashboards
  • Real-time analysis
  • High level enterprise security
  • Interdepartmental sharing

Unfortunately, SIEM supported by simple machine learning capabilities cannot match the power of human ingenuity and the collective collaboration of cybersecurity adversaries. Therefore, the enterprise security team must take the lead in threat hunting and incident response.

However, a properly implemented AI-augmented SIEM can optimize these processes with its predictive and automated capabilities. Such a SIEM can serve as the basis for your IT security team:

  • For example, through your security correlation rules, it can perform automated threat hunting.
  • The AI ​​element of SIEM can identify false positives through the automatic application of contextualization on all alerts.
  • AI-enhanced SIEM can accelerate detection and response times for organizations with limited security workforces.

Essentially, you can think of this technology not just as a second pair of eyes, but also as another pair of hands. However, keep in mind that specialized human intelligence will always triumph over AI.

Predict patterns

Machine learning algorithms augment SIEM systems, allowing them to use past models to predict and anticipate future data.

For example, consider the data models provided during a security breach. Machine learning capabilities allow systems to internalize these patterns and then use them to detect suspicious activity that may indicate a subsequent breach or infiltration.

An AI-augmented SIEM can shut down processes they suspect to be malicious. Not only can this help with threat investigations and remediation, but it also mitigates damage before incident response even begins.

Future security

For relatively small businesses or those with a simple IT infrastructure, the cost of an AI-enabled SIEM would likely be prohibitive while offering little to no benefit when paired with good security hygiene. A large and complex IT infrastructure can justify the cost of an AI-enabled SIEM for a business. However, it is always advisable to get a detailed product review.

Gartner predicts that by 2023, $175.5 billion will be spent on information security and risk management. And data security, cloud security, and infrastructure protection are the fastest growing areas of security spending through 2023. In 2018, $7.1 billion was spent on AI-based cybersecurity systems and services, which are expected to reach $30.9 billion in 2025, according to Zion Market Research.

As the world generates more and more data in an increasingly digital marketplace, the security of your organization’s critical information is of the utmost importance. Cybersecurity tools based on threat intelligence will become your company’s most valuable asset as cyberattacks grow in sophistication and frequency.

Read next: Best Practices for Application Security

Comments are closed.