MSPs are Bolstering Security Programs with Machine Learning and Automation

Advanced threats, a shortage of security experts and the rise in work-from-home together form a catalyst for MSPs to enhance cybersecurity effectiveness for their customers. As MSPs seek ways to increase efficiency and do more with less, they’re turning to advanced analytical capabilities like machine learning, security analytics and automation. All of these have moved past their initial hype cycle and are now adopted and delivering enhanced ROI and outcomes in IT and cybersecurity.

“The future of your business is Big Data and Machine Learning
tied to the business opportunities and customer challenges before you.”

                                    — Eric Schmidt, then CEO of Google
CloudNext Conference in 2017

Machine learning and automation are more than popular buzzwords in the cybersecurity industry. These analytic capabilities make sense of large volumes of raw data to create context and find unknown attacks that speed up decision making. When combined with cybersecurity experts, they hold real promise for their ability to transform IT and security operations for organizations of all sizes. While not a magic potion that instantly perfects data security, these advanced tools offer MSPs a way to augment limited staff in the ongoing battle against cyber criminals.

The Value of Machine Learning and Automation in Cybersecurity

With digital transformation serving as a catalyst for larger volumes of data and technology, use cases for ML and automation in IT and security operations are growing. While not exhaustive, key use cases include:

Analyzing vast reams of data for suspicious activity: It’s challenging to process billions of logs with an all-manual approach. Machine learning does the initial correlation work to process incoming log streams, reduce false positives and alert security operations center (SOC) analysts who perform a second level of triage and potential threat hunting.

Improving SOC efficiency and effectiveness: Machine learning and automation manage repetitive and potentially error-prone tasks that can overwhelm security teams. The result is higher job satisfaction and retention of hard-to-find cybersecurity professionals.

Increasing speed, accuracy and scale of threat detection: Automated incident response can launch a set of corrective actions, open a ticket for SOC triage and even block suspicious processes. Faster detection and remediation reduce the potential damage of attackers.

Detecting anomalous behavior by users and supply chain partners: Detect insider threats and advanced attacks with machine learning to understand and predict normal baseline system activity and identify exceptions that signal a cybersecurity risk. A SIEM (security information and event management) solution provides user and entity behavior analysis (UEBA) to detect insider threats, lateral movement and advanced attacks.

Through advancements and adoption of machine learning and security automation, MSPs are harnessing the vast reams of device and client data to foster better cyber decision making.

Cyber Criminals Also Embrace Advanced Tools  

Defenders aren’t the only ones looking at emerging technologies. Global cybercrime damages are predicted to reach $6 trillion annually by 2021, according to the 2019 Annual Cybercrime Report by Cybersecurity Ventures. Cybercriminals are upping their game to use the latest tools and technology to improve outcomes for their exploits. Hackers are using