Security leaders are in a tricky position trying to discern how much new AI-driven cybersecurity tools could actually benefit a security operations center (SOC). The hype about generative AI is still everywhere, but security teams have to live in reality. They face constantly incoming alerts from endpoint security platforms, SIEM tools, and phishing emails reported by internal users. Security teams also face an acute talent shortage.
In this guide, we’ll lay out practical steps organizations can take to automate more of their processes and build an autonomous SOC strategy. This should address the acute talent shortage in security teams, by employing artificial intelligence and machine learning with a variety of techniques, these systems simulate the decision-making and investigative processes of human analysts.
First, we’ll define objectives for an autonomous SOC strategy and then consider key processes that could be automated. Next, we’ll consider different AI and automation products, then finally look at a few examples of how those tools could be used as part of an autonomous SOC strategy.
The Goal of an Autonomous SOC Strategy
The goal of the autonomous SOC strategy is to automate every step of alert triage from start to finish, reducing risk by independently investigating, triaging, and resolving as many alerts as possible without any human intervention.
It’s important to set expectations here – the objective of an autonomous SOC strategy should not be to replace every human on a security team with AI tech. Like any well-rounded cybersecurity strategy, the bottom line is about protecting the organization by incorporating “people, processes, and technology.” No reasonable security professional thinks we can remove people from that equation.
You can think of an autonomous SOC functioning like an extra team of Tier 1 or 2 analysts, expanding your team’s capacity and skills. The system should be designed to escalate critical threats to human analysts. An autonomous SOC should work for people, using technology that fits into your processes, makes your job easier, and extends your capabilities.
6 Key SOC Processes to Automate
First, we have to recognize that every SOC is different (we’ll talk about tools for automation in the next section.) You’ll need to consider the specific needs of your SOC, so you can prioritize automating the workflows that create bottlenecks or overwhelm your team. Manual tasks that are repetitive and time-intensive are key opportunities to consider for automation.
Here we’ll look at 6 key SOC processes – these will outline what we’ll call our Autonomous SOC:
Monitor – The Autonomous SOC continuously monitors and collects alerts 24/7 from your integrated security tools, ensuring that no potential threat goes unnoticed.
Collect Evidence – Upon receiving an incoming alert, the Autonomous SOC collects all relevant data associated with the alert. That includes files, processes, command lines, evidence from process arguments, URLs, IPs, parent and child processes, memory images, and more.
Investigate – The Autonomous SOC analyzes each piece of collected evidence using AI and a variety of sophisticated techniques. That includes sandboxing, genetic code analysis, static analysis, open-source intelligence (OSINT), memory analysis, and reverse engineering. The results of these individual analyses are then summarized into a cohesive incident-wide assessment using generative AI models.
Triage – The Autonomous SOC categorizes the risk associated with each alert and decides whether to escalate it based on the investigation results. In addition, the Autonomous SOC reduces noise by auto remediating false positives within the detection systems, since these require no other action.
Respond – Serious threats get immediately escalated to the analysts. For all confirmed threats, the Autonomous SOC provides assessments, recommendations, creating tickets in the case management system. These include detection content and ready-to-use hunting rules to guide the response process.
Report – The Autonomous SOC generates reports to keep your team informed and provide tuning suggestions, allowing for continuous improvement in your security operations.
These steps use technology to “autonomously” sift through alerts, escalating only those that truly require human analysis. This helps effectively manage a high volume of alerts and drastically reduces time spent on false positives.
SOC Automation Tools for Building Your Autonomous SOC
On a practical level, you need the right tools to execute your strategy. Let’s look at some of the key tools that you can integrate into your systems to design a step-by-step implementation plan.
SOAR products: This is an established product category, with many SOC teams automating tasks using Security Orchestration, Automation, and Response (SOAR) tools. It has challenges since SOAR usually involves heavy engineering or building complex playbooks. Some SOARs have recently integrated AI, or offer pre-built playbooks and no-code tools that simplify automating some processes.
Autonomous SOC products: This is a newer product category, that uses native automated workflows and AI to ingest, investigate, and triage alerts. The newest startups in this category launched in 2023 or 2024, using technology based on generative AI. More mature Autonomous SOC products have integrated generative AI, using it to complement core technologies like genetic analysis or machine learning.
AI Co-Pilot products: This is the newest category here, which emerged in 2023. New “co-pilot” tools can use generative AI to assist analysts so they can easily query systems to get answers during an investigation. These could potentially integrate with other tools, accelerating incident response or autonomously taking action, but it’s not clear how effective or popular these AI assistants will become.
Different environments require different tools, but we are at a point where the tools are getting easier to deploy and it’s feasible to select tools that play nice together. Security products used should support integrating with SOC automation tools to enable automating investigation and alert triage processes for any type of alert.
Three Different Autonomous SOC Strategy Examples
An autonomous SOC strategy should be adaptable since every security team and organization has different needs. Here we have a few examples of autonomous SOC strategies, showing how different types of security teams or organizations can implement an autonomous SOC strategy.
Example #1
Let’s consider this scenario: A SOC team already has a SOAR that provides some automation, but their workflows for alert triage aren’t fully automated. Triage, investigations, and response are handled by a small internal team of SOC analysts, with assistance from an outsourced managed security service provider. They’re still doing a lot of manual tasks, too many false positives, and they want to improve their mean time to respond. They don’t want to automate more processes by building and maintaining more complex incident response playbooks. They decided to use an autonomous SOC platform that can integrate with their detection tools.
In the above illustration, we can see the processes automated by the autonomous SOC product, which will be a key part of this team’s strategy.
They start by integrating it with their endpoint security product to monitor and triage those alerts. They test the results and build confidence in their autonomous SOC system for endpoint alerts, using their SOAR for escalating alerts and case management. With this system, their triage time for endpoint alerts averages under 2 minutes. Once the analysts are satisfied the autonomous SOC process is implemented effectively, the team integrates the autonomous SOC product to also ingest and triage user-reported phishing emails and SIEM alerts.
Example #2
Next, let’s look at a SOC team in a Managed Detection and Response provider. This MDR team sees adopting an AI-driven strategy as a competitive advantage to enhance client services and increase revenue. They need to monitor and triage alerts from many clients, who use many different tools for detection and response.
They decided to implement an autonomous SOC strategy, which includes using an autonomous SOC product that can integrate with any of their clients’ tools. This will enable them to efficiently monitor, investigate, and triage every alert from multiple client environments, providing fast triage times driven by AI and automation. By expanding their capabilities with AI and automation, the MSSP team can onboard additional clients and handle higher alert volumes, without the challenges of recruiting and hiring additional analysts. After implementing the autonomous SOC product, they’re also able to expand client offerings, providing new services like coverage for user-reported phishing emails.
Example #3
Next, let’s imagine an example SOC team with an established autonomous SOC strategy. The Autonomous SOC product investigates and triages alerts from integrated detection systems and the SOAR is used for escalations and case management. After those tools are fully implemented, then the team adds an AI co-pilot to help the security team query for more information.
This helps show how these tools could fit into different parts of a SOC, but it’s less realistic since tools like AI co-pilots are very new and few teams are using them effectively yet.
3 Benefits of Autonomous SOC Products
The processes for alert monitoring, investigations, and triage are significant opportunities for automation for many SOC teams. Since alert triage processes include a number of repetitive and time-intensive tasks, streamlining this workload with an autonomous SOC product makes analysts more effective and efficient.
Autonomous SOC products offer a compelling option, especially since they’re built to be easy to deploy and integrate with other security tools. They can help teams address challenges from high volumes of alerts as well as talent shortages.
These specialized products provide three important benefits:
Reduce risk by ensuring every artifact and alert ingested from integrated alert sources is comprehensively investigated and efficiently triaged.
Enable analysts to focus on real threats and prevent alert fatigue by triaging alerts using AI automation to make decisions and resolve specific types of alerts.
Escalate the most critical alerts via the autonomous SOC processes, providing key information and allowing analysts to prioritize response for serious incidents.
Ultimately, artificial intelligence and automation can integrate data sources to provide a unified and automated triage experience, enhance investigations, support analysts, and accelerate response times. An autonomous SOC strategy should be designed to use these advanced technologies to support your security team and extend their capabilities.
About Intezer
Intezer is a leading provider of AI-powered technology for autonomous security operations. With a focus on innovation and quality, its Autonomous SOC Platform is designed to investigate incidents, make triage decisions, and escalate findings about serious threats like an expert Tier 1 SOC analyst (but without burnout, skill gaps, and alert fatigue).
Intezer’s customers include Fortune 500 companies like Adobe and Equifax, mid-sized companies, as well as MSSPs that use Intezer’s Autonomous SOC Platform to triage alerts and fully automate their Tier 1 SOC processes.
In 2016, Intezer was founded with a mission to research and develop technology to help SOC teams that had too much work, too many alerts, and not enough people. The Autonomous SOC Platform first launched in 2022. Its core technologies use an Artificial Intelligence framework that incorporates machine learning, generative AI, and proprietary genetic analysis.
Want to learn more? Book a demo with Intezer to see the Autonomous SOC Platform for yourself.
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