Every organization's security operations center (SOC) operates at varying levels of maturity and automation. Those leveraging security automation significantly reduce the time to detect and respond to security alerts and incidents, enhancing the quality and consistency of investigations and responses.
Adopting automation for threat detection, alerts triaging, incident investigation, and incident response in SOCs substantially reduces mundane tasks, improving the efficiency and effectiveness of SOC analysts.
With advancements in AI, cybersecurity leaders are exploring ways to leverage it in SOCs. Key areas of focus include:
- Automated threat detection: Analyzing threat and vulnerability data to understand risk and act accordingly.
- Behavioral analytics: Using AI/machine learning (ML) models to profile user behavior, helping identify insider threats and unauthorized access.
- Threat intelligence: AI tools aggregate and analyze threat intelligence feeds, providing real-time information about emerging threats.
- Automated incident response: AI-driven automation helps SOC teams respond swiftly to security incidents.
- Natural language processing (NLP): Enabling chatbots and virtual assistants to understand and respond to SOC analysts' queries.
Levels of SOC automation
SOC automation spans multiple levels, each enhancing the SOC's efficiency and effectiveness. At the initial level, automation involves basic repetitive tasks such as data collection, log aggregation, and initial alert triage.
The intermediate level includes more complex workflows, such as automated incident response playbooks and threat intelligence integration, enhancing the SOC's ability to swiftly and accurately address threats.
Advanced SOC automation leverages artificial intelligence and machine learning to predict and identify sophisticated threats proactively, orchestrating comprehensive response actions without human intervention.
Manual processes
At this basic level, SOC analysts perform most tasks manually, such as reviewing logs, investigating incidents, and responding without significant automation.
Scripted automation
Organizations create custom scripts or use basic automation tools to handle repetitive tasks. Scripts can parse logs, trigger alerts, or perform basic incident response actions.
Playbook-driven orchestration and workflow automation
Improved SOCs use orchestration platforms to integrate security tools and automate complex workflows. These platforms coordinate actions across different systems, reducing manual effort. For instance, an orchestration tool might collect threat intelligence, enrich it, and trigger a response based on a predefined playbook.
Machine learning-driven automation
AI and machine learning models enhance threat detection and response by analyzing patterns, identifying anomalies, and prioritizing alerts. AI/ML algorithms can detect phishing campaigns, automate vulnerability scanning and patching, or initiate containment procedures based on threat intelligence.
Autonomous SOC
Highly automated with human oversight, autonomous SOCs involve AI systems handling routine tasks, incident investigations, and even decision-making. Human analysts oversee automated systems, manage exceptions, provide strategic guidance, develop policies, and handle complex cases.
Navigating SOC automation: Challenges and strategies
Scaling SOC efficiency requires navigating automation. Challenges include selecting the right tasks, skills, and integrating the tools seamlessly. However, prioritizing repetitive tasks and leveraging automation and AI platforms can streamline workflows, empowering analysts and boosting overall security posture.
1. Capability of the SOC team
Many teams lack the internal expertise to automate playbooks, especially in small to medium-sized companies. These teams often have limited resources, with few SOC analysts managing multiple roles. In such cases, organizations might turn to managed detection and response (MDR) services, but not all MDR vendors offer equally effective automation. Some MDRs are essentially outsourced analyst teams.
2. Cost of automation
While striving for higher-quality services and faster incident response times, capital investment and operating costs naturally increase. However, strategic investment in automation can yield cost savings and enhance SOC quality and effectiveness. Analyzing common tasks and investing in automation where the return on investment (ROI) is evident can lead to significant benefits. For instance, automating the triaging of frequent alert types can significantly reduce security analysts’ investigation time.
3. Skills required for automation
The challenge lies in finding personnel who can bridge the gap between security expertise and technical proficiency. Strategies like upskilling current analysts in scripting languages and automation tools can be crucial. Automating using security orchestration, automation, and response (SOAR) platforms empowers SOC teams to achieve their goals through logic-driven workflow automation. SOAR platforms often prioritize ease of use over power, and analysts may lack development skills. Recent advancements in AI, particularly AI assistants leveraging large language models (LLMs), can address this gap.
4. Automation scope and complexity
It is critical to understand the organization's risk tolerance to determine the level of automation suitable for different security processes. Assess current and emerging threats to prioritize automation in areas most likely to impact the organization. Define clear boundaries for automation, deciding which processes should be fully automated, partially automated, or remain manual. Ensure that automation tools integrate well with existing SOC infrastructure, including SIEM, EDR, and other security tools. Provide adequate training for SOC analysts to leverage automation tools and handle exceptions or escalations.
5. Incident handling, escalation, and feedback
Organizations should define clear escalation protocols for incidents that cannot be resolved through automation. Regularly review automated incident handling to ensure it meets security policies, standards, and regulatory requirements. Successful implementation of automation requires a well-established feedback mechanism for SOC analysts to report on the performance and effectiveness of automation tools.
Building an autonomous security operations center
The concept of an autonomous SOC is edging closer to reality, thanks to advancements in artificial intelligence and machine learning. These technologies can automate a wide range of tasks, from threat detection and incident response to threat intelligence analysis, reducing the need for human intervention. While full autonomy remains a goal, current implementations show significant progress in minimizing manual efforts. Key areas to watch include:
- Advancements in AI and ML: More sophisticated AI and ML algorithms will be crucial for enabling true autonomous threat detection, investigation, and response. These advancements will allow SOCs to automate complex tasks currently requiring human expertise, like threat hunting and anomaly detection.
- Improved threat intelligence sharing: Sharing threat intelligence across organizations will be essential for autonomous SOCs to stay updated on the latest threats and attack methods. This collaboration will allow AI and ML models to be trained on a wider range of data, leading to more accurate threat identification.
- Standardization and interoperability: For seamless integration and information sharing, standardization of security data formats and communication protocols across different security tools is essential. This will allow autonomous SOCs to ingest data from various sources and automate workflows more effectively.
Automation brings significant benefits, but it can't fully address complex situations, strategic choices, or ethical dilemmas. Human oversight remains crucial in these areas. While market trends favor automation and autonomous SOCs, balancing this with robust fail-safe mechanisms is essential to uphold SOC functionality in the event of automation failures.
HPE solution offerings for AI in Cybersecurity
Cybersecurity agility focuses on delivering a flexible, modern, state-of-the-art cybersecurity capability. This ensures the protection and recovery of critical assets, data, and services while safeguarding users from cyber threats and disasters, all aligned with business priorities.
HPE Services offers a comprehensive Security Operations Center Maturity Assessment that evaluates your organization's SOC against industry best practices. Our expert team employs a robust assessment methodology, aligned with established frameworks such as the NIST Cybersecurity Framework and the SOC Capability Maturity Model. This thorough evaluation provides actionable insights to enhance your SOC's effectiveness and maturity, ensuring it meets the highest standards of cyber resilience.
HPE AI-driven security service includes the analysis of the specific customer context, ideating on critical analytics use cases for cyber security, and the design and implementation of a solution to manage (automate) security operations for a selected use case.
Log Analytics and Anomaly Detection service helps you explore enterprise-wide adoption of AI-driven log analytics and anomaly detection for deep AI. Next Gen SOC Design and Implementation service covers SIEM, SOAR and other advanced models for cyber detection and response, and optionally provided by the HPE GreenLake edge‑to‑cloud platform.
Visit global HPE Executive Briefing Centers to talk to experts, see demos, and accelerate your AI, data, and analytics initiatives. Tap into market-leading AI IT services teams of data scientists, solution architects, technologists, and consultants who develop and deliver advisory and professional services in partnership with Hewlett Packard Labs, HPE product teams, and HPE solution partners.
Learn more
HPE AI and data transformation services
Arjun is a passionate cybersecurity architect with more than 20 years of experience in security in security solutioning (infrastructure, information, application, cloud, IoT/OT/IIoT security and data security). He translates companies’ complex security needs into robust, multi-vendor architectures, delivering the utmost security posture. Arjun stays at the forefront of emerging technologies, actively championing new solutions and developing market-sensitive GTM strategies.
HPE Experts
Hewlett Packard Enterprise
twitter.com/hpe
linkedin.com/company/hewlett-packard-enterprise
hpe.com
HPE_Experts
Our team of Hewlett Packard Enterprise experts helps you learn more about technology topics related to key industries and workloads.
