Autonomous SOC: Navigating the Journey Toward Smarter Security Operations
The autonomous Security Operations Centre (SOC) is already here: as different organizations work to increase their SOC maturity and team efficiency, however, the next step toward tighter AI efficiency can be hard to identify, and difficult to trust.
This article identifies the major stages of SOC automation maturity, the challenges faced along the way, and the joint partnership that AI and SOC analysts need to form to pave the way to truly autonomous security operations.
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How AI and Automation Propel the Autonomous SOC Journey
A SOC is the beating heart of enterprise cybersecurity: across its several tiers of incident responders and managers, SOCs detect, analyze, and respond to cybersecurity events by leveraging a combination of skilled personnel, well-defined processes, and advanced technologies.
Modern security teams are grappling with a growing set of challenges; they range from increasingly sophisticated cyberattacks, to the overwhelming alert volumes that cover ever-expanding attack surfaces. Put them together, and the real-world impact starts to erode cybersecurity professionals’ efficiency, and drastically increase their demanded hours.
The result is a persistent talent shortage. These factors make it more difficult than ever for SOC teams to effectively triage, investigate, and respond to threats. As a result, critical tasks like proactive posture management and threat hunting are often sidelined, as they require significant time, specialized expertise, and a great deal of financial backing. It’s in this environment that the AI-driven SOC is becoming an increasingly popular milestone.
As organizations advance through the autonomous SOC journey, their threat detection capabilities grow. AI engines can analyze logs and device behavior connected to previously single-dimensional alerts, analysts’ workflows can be prioritized with greater clarity, and security operations can be scaled up to far greater capabilities than ever before. At the very height of the SOC maturity model, organizations are able to leverage visibility and response capabilities far surpassing their team’s headcount.
Key Benefits at Different Stages of SOC Automation
#1. Manual SOC
There are not many SOCs that rely purely on manual processes today: the proliferation of more advanced security tools has pushed the average SOC far deeper into the automation pipeline. However, this reliance on manual intervention may still linger in some security processes like patch management and threat hunting. It’s immensely time-consuming, and relies on large staff numbers to churn through demanding workflows.
#2. Rule-Based SOC
#3. AI-Unified SOC
AI-unified capabilities evolve runbooks into playbooks, or automated workflows. AI-unified SOCs add an extra layer of analysis over all the log correlation happening in phase 2. This starts to shift it from log correlation to alert correlation – eliminating some of the time that alert clustering usually
demands, and therefore allowing the team to respond to genuine IoCs faster.
SOAR is a common tool seen in AI-Unified SOCs: it gives the SOC a console that incorporates the real-time activity of an organization’s segmented security software, like its SIEM, EDR, and firewalls. This collaboration isn’t just visible: for it to be AI-unified, SOAR automatically cross-references the alerts and data being shared between these disparate tools. They’re able to leverage application programming interfaces (APIs) to transfer data between relevant sources.
From all of this data, a SOAR platform is able to ingest an alert from one tool – like an endpoint detection and response (EDR) solution – and begin connecting other tools’ findings. For example, the EDR may have identified an unusual background application running on a device. The SOAR can compare the application in question against relevant logs within other tools, like threat intelligence feeds and firewalls. This extra data then allows the SOAR’s analysis engine to assess the legitimacy of the EDR’s alert.
Note that the SOAR itself is not full AI: it still relies on vast swathes of playbooks to respond. Developing these SOAR playbooks demands a thorough understanding of each security operation, and what potential threats could look like. Each playbook is built by pinpointing repetitive tasks, and then establishing clear metrics to evaluate the playbook’s performance, such as response times and the rate of false positives. This saves a lot of time in the incident response process – once it’s all up and running.
#4. AI-Augmented Human SOC
This stage sees automation capabilities grow from alert correlation to partial automatic triage. Triaging is the process by which alerts are responded to – and up until this stage, all triage steps have been defined manually. Rather than a trigger for set playbooks, AI-Augmented SOC benefits from investigating each alert as an individual datapoint; and their incident response combines automated suggestions with analyst input.
The specific demands of each investigation process are established by the organization’s own analyzed data: with a baseline of network access, data sharing, and endpoint behavior, the AI is able to spot deviations from this norm – alongside monitoring for known IoCs that match connected threat intelligence databases. Most importantly for this phase, however, are the responses taken: once an alert is linked to a genuine attack path, the AI engine is able to respond through the security tools to cut an attacker off. Throughout this process, it produces and prioritizes alerts and streams to the correct tier of SOC specialists. It connects each alert with consistent, well-documented summaries and findings that quickly bring the human component up to speed.
Tools for achieving this and the final phase of automation include Stellar Cyber’s automated SecOps platform: it grants human SOC experts the ability to rapidly automate triaging, while retaining human analysts as the final decision-makers on remediation. To support this, these capabilities and underlying information are made accessible through a central platform.
#5. Human-Augmented AI SOC
The final stage of AI-SOC integration, this phase sees AI’s capabilities spread from incident detection and response to include wider and more specialist-specific areas.
For instance, detailed forensic investigations are one field in which AI-led SOCs can outpace their human-led counterparts. Starting from a known security incident, a central AI engine can extract relevant IOCs and re-assemble them into likely attack chains – from initial intrusion, across lateral movement, and finally to malware deployment or data exfiltration. These IoCs can remain internal, or be used to enrich the detection capabilities of a central information sharing and analysis center (ISACs). Alongside identifying attackers’ methods and ultimate objectives, this focus on shared knowledge can also allow an AI-driven SOC to pinpoint an attack’s potential perpetrators, especially if their tactics and techniques align with those of known groups.
In this phase, incident communications can also benefit: the growth of niche Large Language Models (LLMs) allows SOC leaders to quickly communicate the core issue at hand, as the central autonomous SOC platform condenses the highly-complex attack into more accessible language. It’s how Stellar’s Copilot AI provides assistance throughout complex investigations. Integrated LLMs also allow for organizations to rapidly inform impacted customers, too – and let SOC analysts focus on AI-guided remediation.
Forensics aside, full SOC automation can proactively identify and automatically the gaps in current security controls. This could be fully automated threat detection; patching; correcting for firewall vulnerabilities discovered during file sandboxing; or integrating with the CI/CD pipeline to prevent vulnerable code from being deployed internally in the first place.
SOC Challenges Along the Journey
Data Integration
Connecting disparate tools and systems to a unified platform can be one of the first SOC automation hurdles. And it’s not even as simple as sharing data between different tools; an autonomous SOC needs an extensible security architecture – one that can integrate seamlessly with the full security stack and ingest, consolidate, and transform data in any format.
At the same time, it’s not just all security, device, and network data that needs to reach the central AI engine: it also needs to support the analysts’ own remediation and investigation attempts, making a centralized platform and cross-tool UI a necessity.
Cultural Resistance
Adapting to automation can require significant shifts in team workflows. if a SOC is familiar with manually maintaining their own firewall and SIEM rules, they may resist the changes posed by automation. It’s why an incremental process is often the best – jumping from phase 1 to 5 in the span of a year would likely represent too much of a disruption.
There’s also a degree of fear to contend with: because automation can now replicate all 3 tiers of SOC analysts’ skill sets, there are valid concerns that human input will no longer be deemed necessary. The truth is far from this: the human SOC team is the best source of real-world understanding and intelligence of an organization’s own architecture and vulnerabilities. Their current challenges need to lead the AI-driven security integration within any SOC; their support will remain crucial even in fully-evolved setups, as they’re at the helm of an AI’s corrective and ethical decision-making.
Skill & Budget Restraints
When implementing AI, it’s vital to draw on subject-specific expertise across AI, automation, and advanced threat detection. This specific mix of skill sets can be difficult to find, however – and not to mention expensive to bring on board. Even the newest SecOps analysts can cost $50k a year, and suitably-trained, AI-first specialists are orders of magnitude more expensive. This links neatly with another challenge: budget.
SOCs used to be confined to the highest-turnover companies; smaller organizations would rely on Managed Security Service Providers (MSSPs) to help balance the cost of cybersecurity against the risk of attack. This means that cost is still one of the greatest hurdles to implementing AI, especially given the time and money sink that manual processes can perpetuate.
How Stellar Cyber Removes the Barriers to Autonomous SOC
An Open, Unified Platform
AI-driven security requires heavy, continuous access to data. Some providers lock this access behind rungs of their own tools. Stellar Cyber, on the other hand, places open integration at the core of the tool’s philosophy. An API-driven architecture allows Stellar Cyber to ingest data from any source and security tool – and further allows the AI engine to remediate incidents via the same bi-directional connections.
The full reach of the organization’s security environment is then unified into a single platform. This places all AI SOC operations at the fingertips of its corresponding analysts. It combines the analysis and remediation actions offered by SIEM, NDR, and XDR – further simplifying a SOC’s tech stack. Since Stellar can embed a host of different frameworks into this wide range of response capabilities, the dashboard also serves to detail the steps that go into each automated response.
A Multi-Layer AI
Detection AI
Correlation AI
Response AI
Multi-Tenancy for MSSPs
Stellar Cyber supports this by offering its capabilities across multiple tenants whilst still maintaining data separation. Preventing this commingling is critical to ensuring back-end security, while still lending highly-trained analysts the tools and visibility of the Stellar Cyber platform.
Scalability for Lean Teams
Whether based within an MSSP or in the organization itself, it’s vital for AI enablement to focus on cost-effective, scalable security operations. Stellar Cyber allows lean teams to achieve the same degree of protection as larger manual teams, thanks to its two core components: automated threat hunting, and accessible decision-making.
While collecting and analyzing the real-time data within an organization, Stellar Cyber collates all possible security oversights into its threat hunting library. This overview shows the different alert types, and the number of each that has been detected. These can be manually connected to ongoing cases, or handled individually. For a different view, Stellar Cyber’s asset analysis process quickly sorts the highest-risk assets, alongside their locations and connected cases, further providing analysts with a higher resolution picture for each potential flaw.
Automated SOC shouldn’t happen at the expense of the team. Stellar Cyber translates each automated decision according to the corresponding framework it uses to get there. For instance, it doesn’t just align with MITRE – it also shares how each triaging decision aligns with this framework. This keeps the triaging process accessible even when handling complex attacks.
Enhance the Efficiency of Your SOC with Stellar Cyber
The result of Stellar Cyber’s AI enablement is an accessible platform that drives a SOC analyst’s confidence in their own processes – elevating both human and AI capabilities. This human-first approach is also why Stellar Cyber prices its platform on a single license. This includes all of its open SecOps capabilities – purpose-built to enhance the efficiencies of each SOC member’s own expertise. To explore Stellar Cyber for yourself, schedule a demo with one of our experienced team members.
