When the definition of SOAR evolved to include security automation in 2017, there was a lot of needless worry and talk about SOAR solutions replacing SOC analysts. History is repeating itself with the advent of an autonomous SOC. This new advent in security management solutions must be viewed as a silver bullet that empowers analysts to take the center stage as opposed to replacing them.

In this blog, you will learn:

What is an autonomous SOC?

Since the idea of an autonomous SOC is relatively new, there seem to be a myriad of definitions as to what it is. Let us try to understand it by seeing what it sets out to achieve.

In 2021, Google Cloud Platform defined autonomous security operations as:

A combination of philosophies, practices, and tools that improve an organization's ability to withstand security attacks through an adaptive, agile, and highly automated approach to threat management.

PwC, on the other hand, looks at autonomous SOC as a service offered to clients which comes equipped with characteristics like:

  • High alert fidelity
  • Extended visibility and ability to collect data from all endpoints
  • Threat detection that stems from behavioral patterns detected through AI

In late 2022, Forrester analyst Allie Mellen referred to an autonomous SOC as "a security system that you set and forget."

She also described it as a "pipe dream" and dismissed it, stating that it wouldn't be possible to automate a SOC based on AI and ML algorithms or rules. Technology, she said, would not be a match for human ingenuity, since hackers are all about breaking rules. Fast forward to 2023, where vendors are knee-deep in a race to automate SecOps. The rise in popularity of AI, fueled by the invention of large language models like GPT, seems to have sped things up.

An autonomous SOC, we can safely conclude, is a SOC that has automated redundant tasks to the maximum extent using the latest technologies the field of cybersecurity has to offer, including AI and ML.

The difference between SOAR and autonomous SOC solutions

An autonomous SOC is an integration of SOAR, SIEM, extended detection and response, and, most importantly, AI. SOAR, on the other hand, continues to rely more on human inputs for its basic functions.

An autonomous SOC is smart. It aims to mimic a security analyst and age like fine wine. Every time there is an incident and every time there is an anomaly, the autonomous SOC is soaking it up, studying it, and storing it in its databases for continuous future reference. Over time, it learns which incident needs more time before a response, which alert qualifies as a false positive, and which suspicious activity qualifies as an anomaly or warrants a high risk score for the user or entity causing it.

What's missing in SOAR is continuous learning. In SOAR, you have threat intelligence platforms (TIPs) bringing in real-time information, and automated workflows and alerting mechanisms facilitating incident response. It is the SOC analyst that tells the solution which TIPs to integrate with and, with time and experience, they decide which workflows work best for an incident and which alerts must be considered severe.

In the case of an autonomous SOC, the solution is programmed to do this task by itself. The security analyst simply adds the various devices from which log data must be collected and indicates the kind of data it must bring in.

How to choose the best vendor for your security needs

The cost of cybercrime is at an all-time high. It has become essential for organizations to invest in the latest cybersecurity technology available on the market. An autonomous SOC, one of the latest developments in security management solutions, could lead to several cybersecurity challenges being addressed. These include:

  • Risk scoring accuracy: An autonomous SOC can dynamically adjust its scoring models based on evolving threat landscapes and organizational priorities. This flexibility, along with features like peer grouping and seasonality, ensures that the risk scoring process remains accurate and aligned with the organization's specific risk profile.
  • Real-time threat monitoring: Traditional security systems often struggle to keep pace with the speed at which cyberthreats evolve. Autonomous SOCs leverage advanced technologies like ML, AI, and behavioral analytics to continuously monitor network traffic, detect anomalies, and identify potential threats in real time. This enables security teams to respond swiftly and proactively to emerging threats.
  • Operational efficiency: Manual security operations can be time-consuming and resource-intensive. Autonomous SOCs automate repetitive tasks, freeing up security personnel to focus on more strategic and complex security challenges. By streamlining processes through capabilities like automatic service ticket allocation, these systems enhance operational efficiency and enable security teams to make the most of their expertise and skills.
  • Improved threat hunting: Autonomous SOCs enable security teams to proactively hunt for potential threats within their network infrastructure. By leveraging information obtained in real time through integration with threat feeds, these systems can identify patterns, detect hidden threats, and uncover indicators of compromise that may go unnoticed by traditional security tools. This proactive approach helps security teams stay one step ahead of cybercriminals.
  • Enhanced incident response: Autonomous SOCs automate incident response procedures, reducing the time and effort required to investigate and resolve security incidents. By employing playbooks and predefined workflows, these systems can automatically triage, analyze, and prioritize incidents based on their severity and impact, ensuring efficient incident response and minimizing the risk of human error.

Organizations must check if the security vendor they are investing in comes equipped with these features. A robust and comprehensive IT security management solution can help analysts save time they would have spent on manual and resource-intensive processes through automated incident response workflows, real-time threat monitoring, and prebuilt playbooks to correlate security alerts.

ManageEngine's SIEM solution, Log360, offers enhanced incident management and response, built-in SOAR capabilities, and Active Directory management and auditing, all in one tool. Its UEBA component, which is powered by ML algorithms, provides greater visibility into threats with score-based risk assessment for users and entities through its dashboard. It provides more context to the risk scoring process by using dynamic peer grouping.

Cybercriminals of today and tomorrow will use AI and ML technology to deploy automated, sophisticated attacks. They're coming for you. Are you equipped with the right technology to hold them off?

Learn more about how Log360, ManageEngine's new-age SIEM solution with autonomous SOC capabilities, can add value to an organization's security posture and help you bridge the gap between where you are today and where you should be. Click here for a personalized demo.

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