SecOps Automation: Use Cases, and How to Overcome Key Challenges
Learn what SecOps automation is, the various use cases for SecOps automation, and how Stellar Cyber can help organizations overcome key SecOps automation challenges.
Security Operations (SecOps) has reached a tipping point: the tools used to keep organizations safe are numerous, overlapping, and highly granular – analysts are pushed to full throttle in identifying and cross-referencing the issues each discovers. However, attackers keep slipping through the gaps.
Security Operations automation promises to reform the way in which SecOps interacts with today’s endless security data – offering enhanced threat detection and compliance. This guide will explore the numerous forms of automation on offer – from Next-Gen SIEM automation, to fully automated response playbooks. Along the way, we’ll cover the key challenges faced by new automation projects.
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What is SecOps Automation?
Cybersecurity is a field that is in constant flux: even the existence of SecOps is a result of the field evolving away from heavy siloed teams. As SecOps has merged IT and cybersecurity into a more cohesive team, enterprises have been able to benefit from faster and more efficient processes. SecOps automation builds on this progress, by streamlining the workflows of employees across the SecOps spectrum.
To illuminate how automation can make a tangible difference, let’s dig into the five key roles that SecOps teams comprise. These are:
- Incident responder: This role is responsible for monitoring security tools, configuring them, and triaging incidents that the tools identify.
- Security investigator: Within an incident, this role identifies the affected devices and systems, performs threat analysis, and deploys mitigation strategies.
- Advanced security analyst: Like a security investigator for unknown threats, this role can sometimes focus on novel threat discovery. From a managerial perspective, they have significant input into vendor and third-party program health and can help identify any deficiencies within the SOC’s tools and procedures.
- SOC manager: Directly overseeing the SOC is the manager: they’re the interface between the security team and the wider business leaders. They’re familiar with each individual role, and are able to steer the team toward greater efficiencies and collaboration.
- Security engineer/architect: This role focuses on the implementation, deployment, and maintenance of an organization’s security tools. Since they manage the overall security architecture, they define what capabilities and visibility the team can handle.
With the roles defined, it’s easier to see how automation promises such massive gains for the SecOps space. The more focused roles – like incident responder – have already benefited massively through tools such as Security Information and Event Management (SIEM). SIEM tools automatically collect and normalize the log files that are generated by each network-connected device.
The Importance of Automated Analysis
Analysis engines are uniquely well-positioned to handle that data – and even more. Consider how a large part of incident responders’ roles focus on cross-referencing alerts and data generated from different tools. Automation tools like Security Orchestration Analysis and Response (SOAR) represent a way to compare data from multiple sources such as SIEM, firewalls, and endpoint protection solutions, and collate all of this data into a single central platform. This offers a unified view of threats, which is marginally faster for incident responders to look through – and far faster for AI analysis engines to ingest. In this way, Security Operations automation is essentially stackable – from data collection and normalization, through to alert analysis and response – Mean Time to Respond is teetering on the edge of minutes, rather than months.
For instance, when an automation-capable SIEM tool notices a deviation in how a user is interacting with high-sensitivity resources, a playbook can tell the AI to assess other streams of information, like recent login data and what webpages the device has interacted with recently. All of this can be used to verify a threat and – when the collected details arrive in an incident responder’s inbox – the security investigator’s manual response is accelerated.
Today’s cutting-edge SecOps automation still requires incident responders to select which action they take in response to certain threats: this is achieved through playbooks. With the correct playbook in place, a suspect user can be prevented from downloading high-risk material, or accessing sensitive networks. By reducing the reliance on manual intervention, automation tools like SOAR not only accelerate SecOps efficiency and response times but also free the teams to focus on strategic initiatives and complex threats.
Use Cases for SecOps Automation
Threat Detection and Response
Threat detection has always been one of the most time-consuming components to SOC teams: given the need for full-stack visibility, an entire decade’s worth of cybersecurity progress saw the rise of hyper-granular monitoring platforms, like SIEM tools. However, this ever-increasing volume and complexity of security data went on to place more strain on upstream systems – like incident responders.
Because traditional, manual methods of monitoring and analyzing security events struggle to keep up with the speed and scale required for modern enterprises, it’s one of the highest-ROI use cases to apply automation. By integrating alongside the SIEM tool you have in place, it’s able to ingest greater amounts of data far faster than humans can.
Chief to the success of threat detection automation is the analytical engine it’s based on. Most SOAR providers will employ a mix of supervised and unsupervised learning: the first operates by explicitly training the model on labeled datasets of known threats. This lets them build a database of threat patterns that can then be applied to the real-world data coming in from an enterprise. Unsupervised learning, on the other hand, sees models that are essentially trained to understand ‘normal’ network and endpoint activity. Whenever a deviation from this is spotted, it can classify it – unsupervised models are able to continuously improve over time, as their output ‘threats’ are judged as correct or not.
Stellar Cyber’s multi-level AI combines a hybrid learning model with GraphML – which correlates all events that are occurring across your enterprise’s networks. This allows for all attacks to be discovered, even the complex ones that are spread across a number of disparate systems. By employing a hybrid model, enterprises can get up and running with the former, while the latter adjusts to the enterprise’s own network contours over time.
Incident Response
In traditional manual workflows, tasks such as alert triaging, data collection, and executing a response often require significant time and human effort. Because SOAR tools span the width of an organization’s security tools, it’s able to implement incident response automation – meaning that the response to a threat can happen at the very endpoint it occurs from.
For instance, emails have traditionally been a significant source of threats. Typically, when confronted with a phishing email, the SecOps team wouldn’t be aware of any wrongdoing until the user had fallen for it and the device had attempted to load the suspect URL. Even worse, a central SIEM tool may not even register a phishing site – especially if it was stealthily stealing inputted credentials. SOAR tools are able to respond immediately on multiple fronts: at the network level, it can identify that the phishing website is suspect via the firewall’s IP reputation; and at the endpoint level, it can employ Natural Language Processing to flag the grammatical warning signs of a phishing message. Both of these allow for action: first blocking the user from accessing the fake login site, and then flagging the email and sending it on to the SecOps team for analysis.
SOAR automation doesn’t just automate the incident response capabilities of SecOps, but decentralizes its just-in-time capabilities, allowing SecOps to secure even remote endpoints.
Compliance Management
SecOps can automate compliance management in a number of different ways: from basic log admin duties, to the higher-level threat management aspects.
By centralizing and aggregating logs, system configurations, and incident details, SOAR platforms enable comprehensive recordkeeping. This is basic, but still critical: article 30 of GDPR and ISO 27001 both explicitly require log records, reports, and documentation to be up to date. By automatically centralizing and storing this data, SOAR can significantly reduce the administrative workload on SecOps teams.
The push for accountability within modern compliance frameworks doesn’t stop at clear and central record keeping: they also need to demonstrate that role-based access controls are being adhered to. SOAR ensures that only authorized personnel can execute specific tasks, due to their implementation with identity and access management (IAM) controls. SOAR takes this further than simple credential checking, however, and takes all data streams into account before access is granted to a user or device. Location, time period, OTP success, resources being requested; they’re all able to play a role in authorization, without impacting the legitimate end-user.
Vulnerability Management
Automated patch management streamlines the otherwise tedious process of monitoring and manually applying patches. By automating these tasks, organizations can address vulnerabilities more quickly and efficiently, ensuring critical systems remain secure.
Integrating a SOAR platform with your organization’s configuration management system simplifies the ever-constant demands of patch management. Vulnerability management automation can continuously monitor the state of different system versions, identifying any deviations from the approved security baseline. When a missing patch is detected, the SOAR platform can initiate an automated remediation process to apply the patch. It then performs an independent verification to confirm the patch was successfully implemented. Should the patching process be unsuccessful, or if certain systems are excluded from automated patch management for operational reasons, the SOAR platform flags these issues for manual review. This means that no vulnerabilities remain overlooked.
User Behavior Analytics (UBA)
UBA is the beating heart of SOAR functionality. It’s made possible by the fact that SOAR platforms aggregate data from vast swathes of data sources, including endpoint detection systems, access logs, and network traffic monitors. Collectively, each data point represents an action or decision being made by an end-user. UBA tools allow SOAR to analyze this data and establish behavioral baselines for each user or entity. For example, a user’s typical working hours, device usage, or data access patterns are recorded over time. When deviations occur—such as accessing sensitive files during unusual hours or a device initiating abnormal network connections—the SOAR platform flags these as potential threats.
Once anomalous behavior is detected, the SOAR platform automates the response process. For instance, if UEBA identifies suspicious activity, the platform can initiate predefined workflows, such as temporarily restricting access, notifying security teams, or launching an investigation into the entity’s recent activities. These workflows ensure swift action while minimizing disruption to legitimate operations.
How Stellar Cyber Overcomes Key SecOps Automation Challenges
While SecOps automation promises a vast amount of growth, it’s worth establishing the biggest hurdles that face teams today – and exploring how SecOps automation challenges can be overcome.
Data Overload
The first question facing every new automation project is where to start. This is one area where the quantity of data involved in SIEM data overload can muddy the waters, and make it harder to judge
what automation project would yield the highest returns.
To combat this, Stellar Cyber’s AI engine ingests all of this endless security data and renders it into two primary data types: Alerts and Incident Cases. Alerts represent specific instances of suspicious or high-risk behavior, and serve as the foundational elements of Incident Cases. To ensure that all of this core data is correctly assessed, Stellar Cyber maps them to the XDR Kill Chain. Each alert includes a clear, human-readable description of the activity and recommended remediation steps.
If it stopped here, analysts would still remain bogged down in the sheer quantity of data that then needs triaging. Stellar’s engine combats this by also cross-referencing alerts. GraphML allows them to be categorized into Incidents by automatically comparing and grouping alerts and events into a smaller set of precise, actionable incidents. This capability provides security analysts with enhanced visibility into attack pathways, their severity, and the areas of highest concern. It’s another example of how small-scale automation – analyzing and mapping alerts – can lead to further efficiency gains, such as deduplication.
Once all alerts are pulled into a central analysis engine, SecOps can benefit from a host of administrative automations: deduplication, for instance, allows for the identification and elimination of redundant alerts and events – this systematic filtering process significantly reduces noise.
So, to combat the challenge of data overload, it’s best to start at the bottom of the SecOps chain: see what sections of the analysts’ workflows are taking the longest, and act accordingly. For most organizations new to SecOps automation, this is the alert triage and analysis processes – hence the focus on automating centralized data analysis.
Integration Complexity
Integrating disparate security tools can be complex, but open APIs and SIEM’s ability to ingest multiple log sources offer a solution.
Given SecOps automation’s reliance on interconnectivity, the challenge of integrating it with every single other security tool in your stack can be a significant barrier to entry. Solving this requires two steps: asset discovery and automated integration.
- Asset Discovery: Stellar Cyber automates asset discovery by passively collecting data from various sources, including endpoint detection and response tools, directory services, cloud audit logs, firewalls, and server sensors. This real-time aggregation identifies assets such as IP and MAC addresses to associate them with their respective hosts. The system continuously updates this information as new data enters the network; by automating this process, Stellar Cyber ensures comprehensive visibility across the network without manual intervention.
- Automated Integration: Stellar Cyber solves the issue of integration via pre-configured APIs: these connectors are developed based on each application’s own access methods; once in place, they actively fetch data according to the pre-set schedule. In addition to collecting data from external systems, connectors can execute responsive actions, such as blocking traffic on a firewall or disabling user accounts. These connectors can handle essentially any form of data – whether raw log data, like a SIEM, or outright security alerts from other security tools. All of these are pulled into the secure Data Lake for further automated analysis.
Collectively, these two steps significantly reduce the demands that a new tool can make on the SecOps team.
False Positives
Unsupervised learning can allow an algorithm to identify novel attacks – but they also flag any previously unknown pattern in a dataset. This is a perfect recipe for false positives, and eventually alert fatigue. This is because an unsupervised learning system learns what constitutes “normal” behavior and flags any deviation from this baseline as a potential anomaly. An Intrusion Detection System (IDS) might recognize normal network traffic patterns and alert when a device attempts to access a different port from normal – but this may also be an IT team member setting up a new app.
Because of this, systems based on unsupervised learning often produce a high number of false positives – and after an alert is generated, it can lack the context necessary for security analysts to assess what’s really going on. At Stellar, this challenge is addressed by using unsupervised ML as simply a foundational step: on top of any unusual behavior, it monitors the full breadth of an organization’s data lake to correlate it against any other data points. This gives each incident a risk factor, which in turn informs how the tool responds.
For instance, consider an executive logging into the network at 2 AM. In isolation, this might appear as a false positive and not warrant an alert. However, if the login originates from an IP address in Russia or China and includes the execution of unauthorized PowerShell commands, these additional data points create a pattern indicative of an account takeover. By connecting these dots, the system provides the necessary context to generate a meaningful alert. And thanks to the flexible connectors we just mentioned, this account can be automatically quarantined in response.
Skill Gaps
Implementing SecOps automation requires a tailored approach that closely aligns with the organization’s security objectives and maturity level to ensure a seamless rollout. Without these competencies, the process may face delays or even risk failure.
For example, integrating security tools or developing playbooks often demands hands-on expertise in scripting languages such as Python, Ruby, or Perl, depending on the SOAR solution. If the SOC team lacks proficiency in these coding skills, it can hinder their ability to perform the required integrations and create effective automation workflows, ultimately impacting the platform’s overall effectiveness.
Next-Gen SecOps automation tools help reduce this gap with NLP prompts, but some of the best improvements in skill gap reduction have been in accessible interfaces. Rather than a complex mish-mash of different tools, SOAR and SIEM integrations like Stellar Cyber have allowed SecOps to see all critical information in an accessible and actionable format. This includes recommended remediation options, and visualizations of the data points that make up each Incident.
Cost and Scalability
While automation reduces operational costs by streamlining repetitive tasks, it’s worth noting the significant cost this can incur: many security tools on the market have individual specializations, making a tool that ingests the data from each, as well as the surrounding networks and endpoints, a real headache. And then when apps, users, and networks change, it only demands further time and resources to maintain.
This is why relying on a SaaS tool can be significantly more cost-effective than building something from scratch. Even this isn’t straightforward, however: since automation relies on such heavy data consumption, pricing models that scale depending on data volumes can be massively volatile. This increases the risk faced by a burgeoning automation project. It’s why Stellar Cyber packages its SecOps automation tool under a single, predictable license.
Achieve Automation-Driven SecOps with Stellar Cyber
Stellar Cyber redefines how organizations approach automation-driven SecOps. It combines Next-Gen SIEM, NDR, and Open XDR capabilities into a single seamless, powerful solution that automates data correlation, normalizes and analyzes information from all sources, and cuts through noise to deliver actionable insight. With pre-built incident response playbooks, teams can react swiftly and consistently to threats, while Multi-Layer AI provides unparalleled visibility across endpoints, networks, and clouds, leaving no blind spots.
By reducing detection and response times and streamlining workflows, Stellar Cyber empowers lean security teams to protect expansive environments efficiently and cost-effectively. Enterprises seeking faster, smarter security operations can explore the Stellar Cyber SecOps platform with a demo.
