What is Security Automation?

Security operations (SecOps) can’t keep up with today’s threats, especially as attackers employ automation in their favor to increase the number of attacks. The threat landscape has scaled, and secops teams using manual processes can’t keep up. At the same time, even the most seasoned Security Operations Centers (SOC) are overwhelmed by the sheer volume of potential threats. 

The Importance of Automation in Security 

Security automation’s purpose is to lift the burden of manual, repetitive tasks in both SecOps and the SOCs so human experts can move at greater speed across the entire security organization. 

• Security automation streamlines time-consuming tasks like patch management. 
• Automated security systems can detect and respond to threats faster than humans, enhancing response times and mitigating risks promptly. 
• Automating security tasks helps reduce the risk of human error, leading to more consistent and accurate execution of security protocols. 

Automation vs AI 

Automation and artificial intelligence (AI) have both become critical components in the field of cybersecurity. What’s the difference? Automation in cybersecurity refers to the use of software or tools to carry out predefined tasks without human intervention. This can include tasks such as routine maintenance, monitoring, or incident response. On the other hand, AI in cybersecurity involves the use of machine learning (ML) algorithms to analyze data, identify patterns, and make decisions based on that analysis. AI can be used to detect anomalies, predict potential threats, and adapt to evolving security risks. 

Key Areas of Security Automation 

Automation can be applied to various domains within cybersecurity. Here are some key areas where automation is making a significant impact. 

Threat Detection 

Monitoring network activity, system logs, and user behavior done by the SOC is the first line of defense in cybersecurity. Tools like endpoint detection and response (EDR), security information and event management (SIEM) and extended detection and response (XDR) use automation to analyze these data points and quickly detect anomalies and suspicious activities.  

Endpoint Detection and Response (EDR)

• Automates the collection and analysis of endpoint data, identifying suspicious patterns and behaviors in real-time.  
• Uses behavioral analysis, anomaly detection, and threat intelligence to identify known and unknown threats.  
• Often employs machine learning algorithms to detect deviations from normal behavior that may indicate a threat. 

Security Information and Event Management (SIEM)

• Automates the ingestion, normalization, and correlation of log data from diverse sources to identify potential security incidents.  
• Source data is from across the entire network, including network devices, servers, applications, and sometimes endpoints.  
• Uses predefined correlation rules to identify patterns and relationships in the log data that may indicate a security threat.  
• Analyzes log data for known indicators of compromise (IOCs) and suspicious activities.
• Incorporates threat intelligence feeds to enhance detection capabilities and identify known threats. 

Extended Detection and Response (XDR)

• Automates the collection and analysis of data across different environments, offering unified visibility and reducing blind spots.  
• Uses advanced correlation techniques and machine learning to identify complex threats that span multiple layers.  
• Analyzes behaviors across endpoints, networks, and other layers to detect sophisticated attacks, such as multi-stage intrusions and lateral movement. 
• Integrates threat intelligence from various sources to enhance detection capabilities and provide context for identified threats. 

Another way threat detection uses automation is through detection deployment. Tuning detection rules inside a EDR or SIEM takes a lot of manual work in many different locations. Security operations platforms like ReliaQuest GreyMatter, automate the deployment of detection rules from the cloud. Rules are centralized and tuned in the cloud then deployed from the platform across existing security tools in an organization’s environment. New rules are deployed in a matter of hours rather than days to quickly to shut the window of vulnerability.  

Investigation and Alert Triage 

Automation carries a large load of triage and investigation of alerts even before it reaches a human analyst. After rule-based detections and analytics from EDRs or XDRs automatically pick up behaviors that match criteria, AI and ML trained on baseline activity is able to compare the alert to historical security event data. It can querying other security sources like logs, system artifacts, network traffic captures, and other sources of evidence like threat intelligence. This automated approach to threat detection and investigation reduces time-consuming and resource-intensive processes speeding up response times.

Providing enriched alerts with contextual data gives SOC’s high fidelity information and reduces investigation times. Gathering data from various sources exactly when it’s needed is a process called data stitching. Removing the need for data repositories, while being able to access essential telemetry that exists outside of the SIEM, data stitching aggregates, normalizes and presents data only in the moment it is needed. 

Timely tuned detection rules deployed automatically from a central location also resolves most false positive and duplicate alerts. 

Security Orchestration 

Automation and orchestration complement each other. Security orchestration is the integration of different security tools and systems. Once integrated, automation allows for coordinating responses across these disparate systems. Security Orchestration Automation and Response (SOAR) tools in many ways were the start of integrating security tools for organization and automation of workflows. These security workflows can be tailored to the organization’s specific security needs and can incorporate various security tools, such as SIEMs, firewalls, EDR solutions, and threat intelligence platforms (TIP). Also, things like generative-AI now speed up code generation for building out automation workflows. 

Incident Response 

Automated incident response systems can take immediate action upon detection of a security event through preconfigured response playbooks. From isolating infected systems to initiating backup protocols, automation ensures that the right steps are taken quickly to contain and resolve incidents. 

The ultimate outcome SOAR was meant to deliver was faster incident response through programmed tasks in playbooks and collections of workflows triggered by alerts. This is still the case with SOAR, but more advanced solutions such as ReliaQuest’s security operations platform GreyMatter, doesn’t require the normal time and complexity to maintain workflows and orchestration. GreyMatter’s bi-directional API integrations enable automated implementation and verification of response playbooks throughout your SIEM, endpoint, network, cloud, and on-premises systems. 

Compliance Reporting 

Staying compliant with industry regulations can be a labor-intensive process. Security automation used for data aggregation and correlation can also be used to automatically check data against compliance frameworks and standards (e.g., GDPR, HIPAA, PCI-DSS). It will then use predefined templates for different report types with the content generated from the analyzed data. 

Application and Code Security 

Developing security practices into software and applications from the outset is a huge ask for development teams. Today’s rapid pace of new code pushed to software and the lack of security training necessitated the integration of security automation into the CI/CD pipeline. Unsecure code is an easy target for threat actors to exploit vulnerabilities. Software engineers can generate code to test security. Generative-AI can produce code to test specific vulnerabilities vastly improving IT security checks. AI’s ability to learn emerging threats patterns and recognize them is also used in security checks throughout the development lifecycle.  

Vulnerability Management 

Vulnerability scanning now doesn’t have to wait for a monthly cadence or longer. Scans can be automated once there has been any kind of update to an IT system. It is specifically designed to adapt and respond to the dynamic nature of modern networks, ensuring that scans are always up-to-date and reflective of the latest changes in the network environment. 

Overcoming Challenges with Security Automation 

While the benefits of security automation are clear, implementing it comes with challenges that organizations must navigate. 

Integration with Existing Systems 

Integrating new automation tools with existing security systems can often prove to be a complex and intricate process that requires thorough planning and execution. It is essential to carefully evaluate the compatibility of the new automation tools with the current security systems to ensure seamless integration.  

This may involve conducting detailed assessments of the existing infrastructure, identifying potential areas of overlap or conflict, and developing comprehensive strategies for integrating the new tools effectively.  

Additionally, considerations must be made for potential disruptions to operations during the integration process and plans put in place to mitigate any risks or issues that may arise. 

New automation technology such as bi-directional APIs and data stitching allow you to leverage existing tools and successfully implement security automation.  

Balancing Automation and Human Oversight 

Finding the right balance between automated processes and human oversight is essential. While automation can handle many tasks, human expertise is still needed for complex threat analysis and critical decision-making: 

• The ability to interpret and contextualize data, think critically, and understand the nuances of various threats is a skill that is uniquely human. 
• Years of training and practical knowledge allow analysts to see patterns and connections that may not be immediately apparent. 
• Human analysts can anticipate potential threats and vulnerabilities by understanding the motivations and tactics of malicious actors. They can also provide a level of creativity and adaptability that technology may lack, enabling them to respond effectively to unexpected or unconventional threats. 

Keeping Automation Systems Secure 

Ironically, automation systems themselves can become targets for cyber-attacks. These systems, consisting of interconnected and IoT devices and software, are vulnerable to malicious actors seeking to disrupt operations, steal sensitive information, or cause damage. As technology advances and more devices become connected to the internet, the potential for cyber-attacks on automation systems continues to grow. 

Best Practices for Implementing Security Automation 

To maximize the benefits of security automation, consider the following best practices. 

• Apply automation to what you know, not what you don’t know: Use automation for specific processes you know and trust instead of applying it to every source in the environment. Automation not only requires intimate knowledge of incident response processes, but it also requires insight and access to systems.
• Focus on quick wins – things that are most impactful and easy to automate: To free up security experts for strategic projects, consider applying automation to “low brain, high time” tasks such as enriching data to help provide additional context around threats as well as entities (including users, devices, and apps) and automating low-impact threat remediation actions across multiple tools. Automation shouldn’t be used to replace security professionals: It should be used to allow security professionals to flex their decision-making muscles.
• Inventory and deploy available playbooks: Your existing platform may already have configurable playbooks that you can apply to different response scenarios. A best practice lies in reviewing what is available: consider deploying those existing playbooks to achieve a rapid impact.
• Automate actions for high-fidelity alerts: A subset of detection alerts might be escalated on a regular basis and are consistently true positives. Security operations platforms can frequently take automated response actions to quickly resolve such high-fidelity alerts and avoid having your security team perform manual, repetitive tasks to remediate the alert.
• Use what you have: Existing tools can inform investigations. Using existing security tools and integration capabilities delivers a better return on investment (ROI) and provides a quicker impact. For example, using bi-directional integrations between a security operations platform and your security tools enables the platform to not only ingest data from the tool, but also take action through that same tool.

Conclusion

Security automation is powerful in the fight against cyber threats. As we look to the future, the role of automation in security will only grow, offering promising advancements in predictive analytics, integration, and adaptability. 

Implementing security automation requires careful planning but embracing it with the right approach is critical to stay ahead of the threat landscape.