The Case for SOC Automation
Security operations centres (SOCs) are under enormous pressure. Vectra's survey of 2,000 analysts found that "SOC teams receive 4,484 alerts each day on average" (Vectra AI, 2023). The skills shortage shows no sign of abating: ISC2 put the global workforce gap at 4,763,963 people in its 2024 study (ISC2). And adversaries are moving faster than ever. CrowdStrike reports that "the average eCrime breakout time dropped to just 29 minutes" in 2025 (CrowdStrike 2026 Global Threat Report), while defenders often take days or weeks to detect and respond.
Automation is the only sustainable answer to this imbalance. By handing repetitive, time-consuming tasks, such as alert triage, enrichment, containment, and reporting, to machines, security teams can focus their scarce human expertise on the work that actually requires human judgement: complex investigation, threat hunting, and strategic decision-making.
But this is not a single technology or product. It is a spectrum of capabilities, from simple alert-enrichment scripts to sophisticated multi-step playbooks that orchestrate actions across dozens of security tools. This guide provides a practical framework for organisations at any stage of the journey.
SOAR vs Native Automation
The security industry offers two primary approaches, each with distinct trade-offs.
SOAR Platforms
Security orchestration, automation, and response (SOAR) platforms, such as Splunk SOAR, Palo Alto Cortex XSOAR, and Tines, provide a dedicated environment for building and managing playbooks. These platforms connect to security tools via APIs and orchestrate actions across the stack. Splunk, for example, says its product "integrates across 300+ third-party tools and supports 2,800+ automated actions" (Splunk SOAR).
Advantages of SOAR:
- Tool-agnostic orchestration: SOAR platforms can connect to virtually any security tool that exposes an API, making them ideal for heterogeneous environments.
- Visual playbook builders: Most SOAR platforms offer drag-and-drop playbook editors that make it possible for analysts (not just developers) to build and modify workflows. Palo Alto Networks advertises a "visual playbook editor for code-free automation" (Cortex XSOAR).
- Case management: Many SOAR platforms include built-in case-management capabilities that tie automated actions to investigation workflows.
Disadvantages of SOAR:
- Integration tax: Each tool connection requires a dedicated integration that must be built, tested, and maintained. When APIs change or tools are upgraded, integrations can break.
- Operational overhead: SOAR platforms are themselves complex systems that require dedicated administration, version control for playbooks, and ongoing maintenance.
- Data-quality dependency: Playbooks are only as good as the data they receive. If upstream tools produce noisy or poorly structured alerts, the playbooks amplify the noise rather than reducing it.
Native Automation
Native automation refers to capabilities built directly into a detection and response platform. Rather than orchestrating actions across separate tools via APIs, the detection platform itself performs enrichment, correlation, and response actions using the telemetry it already collects.
Advantages of Native Automation:
- No integration overhead: Because the detection platform and the response engine operate on the same data, there are no APIs to maintain, no data transformations to manage, and no integration failures to troubleshoot.
- Higher fidelity: Actions are triggered by the platform's own high-confidence detections rather than by alerts that have been passed through multiple systems, each of which may introduce latency or data loss.
- Simpler operations: Security teams manage a single platform rather than a detection stack plus a separate SOAR layer.
Disadvantages of Native Automation:
- Scope limited to the platform's telemetry: Native automation can only act on the data sources the platform covers. If you need to orchestrate actions across tools that the platform does not integrate with, you may still need a supplementary layer.
- Vendor dependency: Relying on a single platform for both detection and response increases your dependency on that vendor.
SenseOn's Approach
SenseOn provides native response capabilities that use its unified telemetry across endpoint, network, and identity domains. When the cross-domain correlation engine identifies a high-confidence threat, automated response actions, such as endpoint isolation, process termination, and user-session revocation, can be triggered immediately without requiring a separate SOAR platform. For organisations that require broader orchestration, SenseOn's API enables integration with external SOAR platforms as well.
Playbook Design Principles
Whether you adopt SOAR or the native route, effective playbook design follows common principles:
Start with High-Volume, Low-Complexity Scenarios
The highest return comes from scenarios that consume significant analyst time but follow predictable, repeatable steps. Common starting points include:
- Phishing alert triage: Automatically extract URLs and attachments from reported phishing emails, check them against threat intelligence, analyse them in a sandbox, and classify the email as malicious or benign.
- Alert enrichment: For every alert, automatically query threat-intelligence feeds, asset databases, identity providers, and vulnerability scanners to add context before an analyst reviews it.
- Endpoint isolation: When a high-confidence malware alert fires, automatically isolate the affected endpoint from the network while preserving the alert for analyst review.
- Account lockout investigation: Automatically correlate account-lockout events with recent authentication logs, geographic information, and known-bad IP addresses to distinguish between forgotten passwords and credential-stuffing attacks.
Design for Human-in-the-Loop
Not every step in a playbook should run without a person. Design playbooks with explicit decision points where an analyst must review and approve before the workflow continues. This is particularly important for:
- High-impact response actions: Isolating a server, disabling a user account, or blocking a network range can have significant business impact. Require analyst approval before executing these actions.
- Ambiguous classifications: When automated analysis cannot definitively classify an alert as malicious or benign, route it to an analyst for manual review rather than auto-closing it.
Build in Feedback Loops
Every playbook should include a mechanism for analysts to provide feedback on the automation's decisions. This feedback serves two purposes:
- Continuous improvement: Analyst feedback identifies playbook logic that produces incorrect results, enabling iterative refinement.
- Metrics collection: Tracking the accuracy of automated decisions over time provides data to support the case for expanding to additional use cases. Microsoft's Defender for Endpoint documentation describes the same pattern for its automated investigation feature: "all remediation actions, whether pending or completed, are tracked in the Action center" where they can be approved, rejected or undone (Microsoft Learn).
Version Control and Testing
Treat playbooks as code. Store them in version control, review changes before deployment, and test them in a staging environment before promoting to production. A misconfigured playbook that auto-blocks legitimate traffic or isolates the wrong endpoint can cause more damage than the threat it was designed to address.
Key SOC Automation Metrics
Measuring the impact requires tracking specific metrics before and after implementation. The financial case is real: IBM's 2026 Cost of a Data Breach study reports "1.93M cost savings, in USD, from extensive use of AI and automation in security, compared to organizations using none" (IBM, 2026).
Mean Time to Detect (MTTD)
The average elapsed time between the initial compromise and the first alert. Automation reduces MTTD primarily through faster alert enrichment and correlation, enabling detection logic to fire sooner because the necessary context is available immediately rather than waiting for manual investigation.
Mean Time to Respond (MTTR)
The average elapsed time between the first alert and the completion of the response action. Automation has the most dramatic impact on MTTR by eliminating the delay between alert generation and response execution. Automated containment actions that would take minutes for an analyst to perform can execute in seconds.
Alert-to-Incident Ratio
The proportion of alerts that are escalated to confirmed incidents. Effective automation improves this ratio by auto-closing false positives and enriching true positives so that analysts can escalate them faster.
Analyst Time per Incident
The average amount of analyst time spent investigating and resolving each incident. Automation reduces this metric by performing enrichment, evidence collection, and initial triage before the analyst engages.
Automation Coverage Rate
The percentage of total alert volume handled by playbooks. This metric tracks the maturity of your programme and identifies alert categories that still require manual handling.
SOC Automation Maturity Model
Maturity is a journey, not a destination. NIST's incident response guidance (SP 800-61 Rev. 3) frames incident handling as part of continuous risk management rather than a one-off capability, and the same applies here. The following model provides a framework for progressive adoption:
Level 1: Manual Operations
All alert triage, investigation, and response is performed manually by analysts. This is the starting point for most organisations and is characterised by long MTTR, inconsistent processes, and analyst burnout.
Level 2: Assisted Enrichment
Automation is used to enrich alerts with contextual information, including threat intelligence, asset data, and user profiles, before analysts review them. Analysts still make all triage and response decisions, but they work with richer data.
Level 3: Semi-Automated Triage
Automation handles initial triage for well-understood alert types, auto-closing confirmed false positives and escalating confirmed true positives. Ambiguous alerts are routed to analysts for manual review. Response actions still require human approval.
Level 4: Automated Containment
High-confidence detections trigger automated containment actions, such as endpoint isolation, account suspension, and network blocking, without waiting for analyst approval. Human-in-the-loop review occurs after containment rather than before, dramatically reducing MTTR.
Level 5: Continuous Autonomous Operations
The SOC operates as a primarily automated system with human oversight. Analysts focus on threat hunting, detection engineering, and strategic improvement rather than alert triage and incident response. This level requires extremely high-fidelity detection to avoid automated actions against false positives.
Most organisations should aim to reach Level 3 or 4 before thinking about Level 5. Level 5 remains aspirational for most and requires both exceptional detection fidelity and organisational trust in the system.
Getting Started
The path begins with a short procedure you can run in a fortnight:
- Measure your current state. Pull four weeks of alert data and record MTTD, MTTR, daily alert volume, and analyst time per incident. You cannot demonstrate improvement without a starting point.
- Rank alert categories by analyst hours. Multiply each category's volume by its average handling time. The top of that list is where the time goes.
- Pick one candidate. From the top three, choose the category with the most predictable triage steps (phishing reports and lockout investigations are usual winners). Write the analyst's current steps down as a numbered list; that list is your first playbook.
- Choose the delivery route. If your security stack is consolidated on a single platform like SenseOn, native response will deliver the fastest time to value. If you operate a heterogeneous tool environment, a SOAR platform may be the better starting point.
- Run it with a person in the loop for 30 days. Compare the playbook's decisions with the analyst's on every case, fix the logic where they differ, then remove the approval step only for actions that are cheap to reverse.
- Re-measure and report. Repeat step 1 and publish the difference to leadership before you pick the next candidate.
None of this is about replacing analysts; it is about freeing them to do the work that only humans can do. The organisations that get this right will not only improve their security outcomes but will also retain their talent by eliminating the drudgery that drives analyst burnout.
Sources
- Vectra AI, 2023 State of Threat Detection, 2023, accessed 20 September 2026
- ISC2, 2024 Cybersecurity Workforce Study, 31 October 2024, accessed 20 September 2026
- CrowdStrike, 2026 Global Threat Report, 2026, accessed 20 September 2026
- Splunk, Splunk SOAR product page, accessed 20 September 2026
- Palo Alto Networks, Cortex XSOAR product page, accessed 20 September 2026
- Microsoft, Use automated investigations to investigate and remediate threats (Defender for Endpoint), updated 2 July 2026, accessed 20 September 2026
- IBM, Cost of a Data Breach Report 2026, 2026, accessed 20 September 2026
- NIST, SP 800-61 Rev. 3, Incident Response Recommendations and Considerations for Cybersecurity Risk Management, April 2025, accessed 20 September 2026
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