Cybersecurity Analytics: The Assistive Technology That Gives Your SOC an Edge

Security3 minutes readNov 28th, 2017

In cybersecurity, our guard can never be let down when it comes to potential threats. Virus programmers and identity thieves are consistently finding new ways to steal information and jeopardize both businesses and individuals. Cybersecurity defense teams struggle to keep pace on threat detection and timely response. New methods are required to reduce false positives and predict attack vectors in order to regain a responsive threat posture.

As cybersecurity threats evolve, so too should the security systems ability to detect and respond. However, the threat landscape is not only evolving but also expanding with the explosive growth in Internet-connected devices. The straightforward malware attacks once considered the biggest threats in the cyber world have evolved with self-morphing and complex subversive techniques that can elude existing detect and respond systems. This poses an increasing problem to cybersecurity professionals. Enter the data scientists, who are exploring innovative approaches to apply advanced analytics and machine learning techniques to the mountains of event data collected with successful results that reduce false positives, foresee trends, and predict malicious behavior.

The human element of the cybersecurity realm is and will continue to play a vital role. However, machine learning threat detection systems are proving to be beneficial assistive technologies. While cybersecurity analysts provide the essential human point of view to malicious programming logic, they cannot always keep up with the pace of threats. For one, so much data is captured each day that it cannot be sifted through in a manner that stays ahead of cybersecurity threats. Secondly, it is not feasible or realistic to attempt this with human resources alone in a time-efficient manner.

Additionally, many organizations who employ cybersecurity individuals have issues of their own that cause a potential lack in organization. While the turnover rate for these individuals is not necessarily high, there is such high demand that it is nearly impossible to employ enough individuals to deal with this demand. Even if such individuals are employed in a timely manner, there may be gaps in employee skills, training deficiencies, and other factors that are crucial for consistent execution.

Today, cybersecurity organizations are taking measures to better these situations. First and foremost, budgets have been allotted to focus on advanced analytics. With investment concerns taken care of, formal plans are being put in place to address known issues within companies. Network and endpoint security tools are being integrated, as well as security analytics and operations tools. Additionally, common data management platforms are being implemented, as well as APIs provided by security tech vendors for product integration.

Overall, the greatest way to handle increased cybersecurity threat is to implement advanced analytics, automation, and machine learning. This will aid human analysis as needed and bridge the gaps in the industry that have been left vulnerable in this increasingly hostile digital age.

See our recent whitepaper on Cybersecurity Analytics and Operations for more information.

Tags-   Analytics CyberSecurity Cybersecurity Analytics Security