Employing SIEM (security information and event management) software provides the enterprise 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 subsequently alerts your IT security team of failed logins, malware, and other potentially malicious activities.
However, over the years, SIEM has barely evolved beyond the ability to provide a better, more searchable rule-based log 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 and machine learning-based algorithms 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 within a given IT environment. This integration lends SIEM with deep learning capabilities and a myriad of integrated tools to drive more informed results.
Following are the benefits of such an integrated SIEM.
Also read: AIOps Trends & Benefits for 2021
A typical SIEMs analytics correlates events from different sources gathered over a relatively short period (typically hours and days). This, when compared with an infrastructures baseline, will output a prioritized alert if they exceed the preset thresholds. AIOps represent systems that store event information gathered over a long period (perhaps years) in a database and then apply analytics to that data.
Such analytics enables AIOps to adjust the infrastructure baseline and adjust alerting thresholds over time, as well as automatically undertake some remedial actions based on correlated events. In addition, employing big data lends SIEM the ability to detect even the very slow or stealth activities on a network that SIEM would otherwise miss or dismiss as a one-off. By detecting these slow or stealth activities, a security team can prevent a major security incident.
Besides offering standard log data, AI and machine learning technologies can also incorporate threat intelligence feeds. Some products can also feature advanced security analytics capabilities that look at both user and network behavior. Machine learning enables your SIEM to facilitate threat detection across large data sets, alleviating some threat hunting responsibilities from your security team. Threat intelligence provides insights into the likely intent of individual IP addresses, websites, domains, and other entities on the internet. This allows them to distinguish a normal activity from a malicious one.
Providing your SIEM with continuous access to one or multiple threat intelligence feeds enables machine learning technologies to use the context that the threat intelligence delivers. And as it learns more, it starts to understand malicious behavior warnings beyond its initial data input. Therefore, it can stop threats your cybersecurity has never seen before. It improves the SIEMs decision-making, particularly in terms of accuracy, thus helping to deepen your security layers.
There is a caveat, though. Machine learning works better on larger datasets than smaller ones, but because big data is lossy, it may complicate compliance reporting. But as this is a known problem, there are multiple workaround options available.
Also read: What is SIEM Software and How Can It Protect Your Company?
A typical SIEM provides a considerable amount of monitoring data/logs, but SIEM report data is not actionable, hard to understand, and contains too much noise. An AI integrated SIEM solution manages big data efficiently and can replace repetitive, redundant tasks with automated workflows.
Although most AI programs facilitate data classification, the AI element isnt capable of grouping unrecognizable data points and event information. On the other hand, machine learning can leverage data clustering capabilities to identify these unknown values and group them into categories based on similarities detected.
As an enterprise scales up, it becomes more susceptible to blind spots appearing. And each blind spot can go unmonitored for months, if not for years at a time. Consequently, these parts of the network can go unpatched for long periods of time. These blind spots further become a perfect place of infiltration for the hackers to plant dwelling threats.
Fortunately, AI in SIEM can help improve the visibility of your network, thus quickly and periodically uncovering blind spots in your networks. It can also draw security logs from these recently uncovered blind spots, in turn expanding the reach of your SIEM solution.
Also read: Steps to Improving Your Data Architecture
The Security Operation Center (SOC) teams of any enterprise are limited, and the amount of log data generated from any SIEM is quite considerable. This makes the challenge of dealing with incidents in a responsive and effective manner extremely daunting. More so, a lot of SIEM tools also provide a lot of unrelated data, causing the SOC teams to face alert fatigue.
This situation happens when dealing with too many alerts and not knowing which alerts you should pay attention to and ignore. Automated and standardized workflows provided by ML can reduce the possibility of human error and get the job done much quicker.
SIEM also requires constant monitoring from your IT security team. Manually monitoring every system checkpoint is not only exhausting but will also induce burnout. SIEM backed with ML capabilities can offer:
Unfortunately, SIEM backed by simple machine learning capabilities cannot match the power of human ingenuity and collective collaboration of cybersecurity adversaries. Hence, the enterprises security team needs to take the lead on threat hunting and incident response.
However, a properly implemented AI-augmented SIEM can optimize these processes through its predictive and automated capabilities. Such SIEM can provide the groundwork for your IT security team:
Essentially, you can think of this technology not only as a second pair of eyes, but also another set of hands. However, keep in mind that specialized human intelligence will always triumph over AI.
Machine learning algorithms augment SIEM systems, enabling them to use previous patterns to predict and anticipate future data.
For example, consider the data patterns provided during a security breach. Machine learning capabilities enable systems to internalize those patterns and then use them to detect suspicious activities that could show a subsequent breach or infiltration.
An AI-augmented SIEM can halt processes they suspect to be malicious. Not only can this help with investigations and threat remediation, but it also mitigates damage even before incident response begins.
For relatively small companies or those with simple IT infrastructure, the cost of an AI-enabled SIEM would probably be prohibitive while offering little to no advantage when coupled with good security hygiene. A large and complex IT infrastructure might justify the cost of an AI-enabled SIEM for an enterprise. However, it is always advisable to get a detailed evaluation of the products.
Gartner predicts that by 2023, $175.5B 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, a whopping $7.1B was spent on AI-based cybersecurity systems and services, which is predicted to reach $30.9B in 2025, according to Zion Market Research.
As the world generates more and more data in an increasingly digital marketplace, the security of your organizations critical information is of the utmost importance. Threat intelligence-enabled cybersecurity tools will become the most valuable asset for your company as cyberattacks grow in sophistication and frequency.
Read next: Best Practices for Application Security
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