How to Increase SOC Maturity in Cybersecurity Operations

SOC automation you must need to know. Even before COVID-19 fundamentally disrupted business operations, the proliferation of systems, data, cloud technologies, apps, devices, and distributed endpoints was already increasing cybersecurity risks. Not only are the challenges facing security operations centers (SOCs) – the centralized “hearts” of cybersecurity detection, analysis, and prevention in organizations – ever-changing, but their toolsets are evolving rapidly.

 Among cybersecurity technologies, AI holds great potential to reshape how organizations manage, resource, and protect against cybersecurity threats. In particular, software products use machine learning, deep learning, computer vision, and various related techniques to rapidly inspect vast amounts of data and detect potentially malicious behavior at scale. 

 The Research Identified the Following Seven Unique Use Cases for SOC Automation

 1. Incident Analysis

 Using various AI techniques, security incident data is extracted, parsed based on parameters, grouped by commonalities, and assigned a risk score. A SOC analyst’s primary job is to monitor threats, but historically this has required time-consuming and repetitive triage. This can lead to misidentified hazards, inefficient use of highly skilled analysts, burnout, and staff turnover. AI can help you efficiently scale your analytics by casting wide networks (and ever-growing networks).

2. Landscape Analysis–AI is used to defend growing topologies. Companies are digitizing more and more processes. This includes updating the old and developing new in-house (often hybrid) platforms and networks. As more employees use cloud apps and mobile devices for work, not to mention the proliferation of IoT configurations, corporate security perimeters are expanding beyond the organization’s traditional four walls. Extensive network and endpoint safety resources are needed to manage all communications, transactions, connections, applications, and policies. These resources are often disconnected, limiting the visibility and detail of your risk profile. AI can support, reach, and scale these heterogeneous topologies, correlating threats and evaluating how threats affect different resources.

3. Incident Detection – this automation use case helps differentiate and prioritize different classes of threats and distribute alerts or mitigations accordingly. This can take many forms, from automating ticket creation and adding relevant remediation information to detecting the presence of malware before a malicious file is opened. AI-powered incident detection is critical to preventing attacks, as it reduces dwell time and remediation time and enables proactive measures.

4. Incident Response

 AI is used to prevent malicious attacks by automating mitigation. Software, device, or network orchestration or use of other specific safeguards. Incorporating AI predictive capabilities can help organizations transition from passive cybersecurity mitigation to proactive cybersecurity strategies in their fight against hackers. Organizational deployment of AI-powered incident response applications is still on the rise, but taking a proactive approach to the never-ending cybersecurity storm is critical to enterprise security.

 5. Emerging Threat Mitigation

 SOC Automation is used to learn about emerging threats by recognizing patterns or clusters and providing feedback. Some companies train machine learning algorithms to detect attacks performed by other machine learning algorithms, such as clever malware that personalizes attacks to specific victims or artificial hackers and bots.

How do Data Science and Cybersecurity Automation Drive SOC Maturity?

Data science and automation allow you to look at telemetry from different angles and narrow it down to the relevant incidents for investigation. The benefits of data science in SOC include

• Reduced Alert Fatigue
• Increased SOC Team Efficiency

The function of the SOC in Cybersecurity is to monitor, prevent, detect, investigate and respond to cyber threats 24/7.

The next-generation, cloud-native SIEM Solution incorporates key SOC capabilities, natively leverages data science and cybersecurity automation, and streamlines the work of SOC professionals. It is supported by experts, including data scientists and engineers, security analysts and engineers, threat researchers and hunters. This full-featured professional is ready to accelerate threat detection and response across your IT environment. Both local and remote infrastructures include cloud services, containers, serverless technologies, and IoT/OT.

The goal of any initiative to automate a security operations center is to reduce mean time to detection and mean time to remediation without increasing headcount in proportion to business growth. The key is to measure automation results and SOC efficiency and gain insight and determine where automation efforts need to be made to improve the organization’s security posture.

For more details on SOC, click here: https://en.wikipedia.org/wiki/SOC.