Utilizing SOC Automation to Enhance Threat Detection

Cybercrime has gradually evolved into a thriving industry and is expected to inflict over $5 trillion worth of damages annually by 2021, making the prevention and detection of cyber vulnerabilities paramount. Persistent efforts by enterprises to combat and mitigate the risks arising out of cyberattacks have led to the convergence of AI (Artificial Intelligence) and ML (Machine Learning), across the cybersecurity landscape, triggering the onset of automation practices. Cybersecurity automation is being hailed as the next big step in information security, attributed especially to the cumbersome endeavors required for manually managing cybersecurity policies.


Reports from cybersecurity experts and industry research have consistently highlighted the shortage of skilled IT security personnel. In 2019, it was estimated that over 40% of organizations lacked the required cybersecurity skills to improve their security posture. Ironically, this is also one of the biggest challenges while implementing cybersecurity automation, with only 30% of organizations featuring an in-house team capable enough to use security automation.


Today, there are numerous cybersecurity products designed to automate processes. For instance, vulnerability management products such as anti-malware may be set up to scan and automatically detect BYODs (bring your own device) on an organization’s system. These products identify cyber threats and eliminate identified defects based on the security protocols outlined by the organization. When talking about adopting new best practices in automation, gurus in this industry refer to security equipment like Robotic Process Automation (RPA), and Security Operations Center (SOC) automation are used when a security operations center automates aspects of their cybersecurity defense such as detection, investigation, and response. One of the more common types of SOC automation is via SOAR (Security Orchestration Automation and Response).


The goal of SOC automation is to augment the SOC team to speed up the time from detection to remediation. Most SOCs face a lack of manpower which makes it overwhelming, if not impossible, to handle the number of alerts the SOC sees each day. By automating aspects of the SOC, the SOC team can focus on complex threats and not waste time on benign alerts or known threats. Known threats can be quickly resolved by automating the response process.

Research has identified the following seven unique use cases for SOC automation.

1. Incident analysis

Various AI techniques are used to mine data on security incidents, parse them based on parameters, cluster them for commonalities and assign risk scores. The core role of SOC analysts is to monitor for threats, but historically, this has required tedious and repetitive triage for them. This could result in misidentified threats, inefficient use of highly skilled analysts, staff burnout and turnover. AI helps scale analysis efficiently by casting a wide net that continues to grow wider and wider.

2. Landscape analysis

AI is harnessed to defend widening topologies. Companies are digitizing more and more of their operations. This includes updating old and developing new internal, often hybrid, platforms and networks. As more employees use cloud apps and mobile devices for work, not to mention increase IoT configurations, the enterprise security perimeter spans far beyond the organization’s traditional “four walls.” Extensive network and endpoint security resources are required to manage all communications, transactions, connections, applications, and policies. These resources are often disconnected, thus limiting visibility and details of the risk profile. AI can support, reach and scale across these heterogeneous topologies while correlating threats and assessing how one threat may impact another resource.

3. Incident detection

This SOC automation use case helps differentiate and prioritize different classes of threats and distributes notifications or prevention activities accordingly. This could take many forms, from automating ticket creation and adding pertinent remediation information to detecting the presence of malware before malicious files are opened. AI-powered incident detection is obviously crucial for preventing attacks as it reduces dwell time and accelerates time to repair, but it is also enabled preemptive and proactive measures.

4. Incident response

AI is used to preempt malicious attacks by automating containment actions; orchestration of software, devices or networks; or the deployment of other specific safeguards. Incorporating AI’s predictive capabilities helps complete the shift from reactive cybersecurity mitigation to a proactive cybersecurity strategy in an enterprise’s fight against hackers. The use of AI-powered incident response applications in organizations today remains incremental, but a proactive approach to the never-ending cybersecurity storm is critical for enterprise security.

5. Emergent threat mitigation

SOC automation is used to learn about novel threats by recognizing patterns or clusters and then providing feedback. Some companies are training machine learning algorithms to recognize attacks perpetrated by other machine learning algorithms, such as smart malware or artificial hackers and bots that personalize attacks tailored to specific victims.

These emerging, AI-based threat mitigation techniques will prove useful as attack tactics, such as malware, botnets, and ransomware, continue to mutate along with the pernicious ways AI is used to target and manipulate user and business vulnerabilities.

6. Gamification of security training

AI can also be used to simulate diverse types of attacks and make the education process more fun, engaging and competitive for security analysts. Microsoft’s Into the Breach exercise is one example in which the company divided SOC analysts into different teams. The teams were challenged to defend against AI-generated threats, which were developed based on data and techniques derived from real-world attacks.

While nascent, this SOC automation application has the potential to scale a culture of security far beyond the SOC. Not only will it make security training more accessible, personalized and fun, but it can also arm the front line of cybersecurity with essential awareness and security workflow best practices.

7. Human SOC analyst augmentation

This is the AI market’s parlance for how AI is used to optimize humans’ threat intelligence and mitigating actions. Although automated techniques are better at managing the volume of potential threat vectors, AI is not equipped for complex problem-solving. Human analysts remain the essential arbiters to develop controls, explain threat techniques and uncover attackers’ motives. Tests show that the highest performance and accuracy of machine learning are often achieved due to a combination of human and AI intelligence. Thus, AI is unlikely to completely displace SOC analysts but rather supplement the team’s efforts and talents.


CyboWall is a next-gen cybersecurity solution that utilizes Artificial Intelligence  & Machine Learning to reduce the complexity of cybersecurity and provide an integrated platform with unified visibility and management.

CyboSOC is a cloud-based management application tailored to manage many CyboWall through one management dashboard console. CyboSOC has been created to allow MSP / MSSP organizations that are servicing end customers using their analysts, to manage and monitor several CyboWall entities within one management application.

CyboSOC Superior management platform allows MSPs and MSSP to connect to all CyboWall entities that are spread across end customers’ sites and multisite organizations, whether they are deployed on-premise (Agentless), or operational through a cloud (agent-based).

CyboSOC main features:

  • Remote monitoring and management over different end-customers’ entities which operate CyboWall under one management platform.
  • Clear vision on ongoing breach attempts in every CyboWall (which is assigned to an end-user site).
  • Remote analysis of ongoing alerts and events of every remotely monitored CyboWall, including visibility on Cyber-attacks over these entities.
  • Centralized management of every CyboWall, (end-user), with assignment of new tenants, billing and invoicing procedures.

To learn more and to book a demo click here.

Written by: Ziv Simhon, VP of Sales at CYBOWALL