Written by: Nimesh Chakravarthi, Co-founder & CTO, Struct
Key Takeaways for Choosing a SOAR Platform
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SOAR platforms automate alert intake, enrichment, triage, and remediation, replacing manual multi-tool investigations that slow MTTR.
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AI-driven automation can reduce MTTR by 40–90% and cut triage time by up to 80% for teams handling high alert volumes.
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Platforms differ in integration depth, coding requirements, and threat-intel capabilities; Struct focuses on zero-code setup and observability-native correlation.
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Low-code and no-code SOAR options like Struct deliver first-pass investigations in under 10 minutes without dedicated automation engineers or playbook scripting.
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Struct can automate your on-call runbook so AI completes the first investigation pass before an engineer opens their laptop.
Why Automated Investigation Reduces MTTR and Alert Fatigue
Alert volume and SLA severity push engineering teams toward automation because manual triage consumes expensive senior time. A $200K-per-year senior engineer who spends an entire sprint reacting to recurring pages delivers no product velocity. IBM’s 2025 Cost of a Data Breach Report found that organizations using AI and automation extensively reduced average breach costs by $1.9 million per incident compared to organizations without AI. Separately, SANS Institute’s 2025 survey found that 73% of security teams cite false positives as their primary detection challenge, which inflates mean time to acknowledge and indirectly extends MTTR.
Mainstream AI SOC deployments cut MTTR by 40–50%, with leading platforms claiming reductions up to 90% by collapsing mean time to acknowledge to seconds, running investigation in parallel, and executing containment under predefined authority. Struct customers working at large scale with many services report an 80% reduction in triage time, turning a 30–45 minute manual investigation into a 5-minute review. Achieving those reductions requires clarity on which investigation stages each platform automates and which steps still demand manual effort.
How SOAR Platforms Map to the Investigation Lifecycle
Automation in incident response maps to stages via automated enrichment for context gathering, behavioral analytics for post-detection triage accuracy, agentic AI that runs investigations when cases open, and playbook automation via SOAR platforms that execute containment without human intervention. The table below highlights a clear divide. Traditional SOAR platforms rely on heavy scripting to progress from enrichment to remediation, while newer options like Struct automate the entire investigation lifecycle with minimal or no code.
|
Platform |
Alert Intake & Enrichment |
Timeline & Root-Cause |
Handoff & Remediation |
|---|---|---|---|
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Cortex XSOAR |
Broad SIEM/EDR connectors, playbooks require ongoing maintenance to stay compatible with integrated tool APIs |
Python-scripted correlation, high customization ceiling |
Automated ticketing and containment, heavy scripting required |
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Splunk SOAR |
Native Splunk SIEM integration, custom app connectors needed for non-Splunk products |
Playbook-driven investigation, strong log correlation |
Automated response actions, complex playbook design |
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Torq |
No-code triggers, integration consistency issues reported alongside complex SIEM setups |
Visual workflow builder for timeline logic |
Webhook-driven handoffs, limited native remediation |
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Swimlane |
Low-code intake with API connectors |
Case management with correlated evidence |
Automated playbook execution, scripting for edge cases |
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Tines |
No-code story builder, REST API-first enrichment |
Event-driven logic, manual timeline assembly |
Webhook actions, no native code remediation |
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Palo Alto XSIAM |
AI-driven alert correlation across endpoints and cloud |
Unified XDR timeline, strong threat-intel stitching |
Automated containment, enterprise licensing required |
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Struct |
Automatically pulls metrics, logs, traces, monitors, and code the moment an alert fires, Slack, PagerDuty, Sentry, Linear triggers |
Slack conversational AI for follow-up, hands off to CLI, AI coding agent, or PR creation |
See how your current stack maps to these stages with Struct’s automated runbooks.
Best SOAR Choice for Phishing and Abuse Triage
Phishing triage demands rapid header analysis, URL detonation, sender reputation lookup, and mailbox remediation within minutes of a report. Cortex XSOAR and Splunk SOAR both offer mature phishing playbooks, but SOAR platforms require strong integration across diverse tools and can encounter workflow design difficulties early in deployment, which adds days to initial configuration. Tines handles phishing intake cleanly through no-code stories but still requires manual assembly of the investigation timeline.
Many engineering teams see “phishing” as internal tooling abuse or credential-stuffing alerts surfaced through Sentry or Datadog anomalies. For those teams, Struct integrates directly into Slack alerting channels and automatically investigates issues the moment they fire. It correlates log anomalies with GitHub commit history to show whether a suspicious authentication event coincides with a recent dependency change. The Slack-native conversational AI lets engineers ask follow-up questions such as “show me all login attempts from this IP in the last hour” without switching tools.
Low-Code SOAR and What It Means for Security Teams
Gartner predicts that by 2026, developers outside of formal IT departments will account for 80% of the user base for low-code development tools, up from 60% in 2021. For engineering teams evaluating SOAR platforms, this shift makes implementation speed and coding requirements more important than raw feature breadth. The decision matrix below compares how each platform balances customization flexibility against setup complexity.
|
Platform |
Coding vs. Low-Code |
Typical Implementation Time |
Pricing Model |
|---|---|---|---|
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Cortex XSOAR |
Heavy Python scripting for custom playbooks |
Weeks to months |
Enterprise license, per-user or capacity-based |
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Tines |
No-code story builder, JSON for advanced logic |
Days to weeks |
Per-action or per-user tiers |
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Torq |
No-code visual builder, integration consistency issues with complex SIEMs |
Days |
Consumption-based |
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Swimlane |
Low-code with scripting for edge cases |
Days to weeks |
Per-user enterprise pricing |
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Struct |
Zero-code, natural language runbook input via Slack |
Startup, Growth, and Enterprise tiers, 30-day pilot included |
Compare your current rollout timeline against Struct’s 10-minute setup and see how quickly you can reach your first automated run.
SOAR Platforms with Strong Threat-Intelligence and Engineering Context
AI-driven security platforms can detect anomalies and correlate events across endpoints, networks, cloud environments, applications, and user identities within seconds, but enrichment quality depends on the data sources a platform can query. Cortex XSOAR and Palo Alto XSIAM lead on traditional threat-intel feeds such as VirusTotal, MISP, and Shodan. Splunk SOAR enriches effectively inside the Splunk ecosystem. Tines and Torq rely on REST API calls to external feeds, which requires ongoing connector maintenance.
Struct focuses on enrichment for software engineering stacks rather than classic SOC feeds. Struct pulls and analyzes metrics, logs, traces, monitors, and code simultaneously, correlating Datadog metrics with AWS CloudWatch logs, Sentry exceptions, Azure traces, and GitHub commit history in a single unified timeline. This cross-layer correlation, which combines observability data with code context, surfaces root causes that threat-intel feeds alone cannot identify, such as a latency spike caused by a specific pull request merged 20 minutes before the alert fired.
Decision Factors: Coding Overhead, Integrations, and Compliance
Reddit threads in r/sysadmin and r/netsec consistently surface two friction points: playbook complexity that requires a dedicated automation engineer to maintain, and vendor lock-in from proprietary data schemas that make migration expensive. These friction points appear differently across vendors. IBM QRadar SOAR works best with QRadar SIEM, but connecting to non-IBM SIEMs such as ArcSight requires significant configuration effort and is not plug-and-play. This pattern exemplifies vendor lock-in. Rapid7 InsightConnect detailed customization requires scripting knowledge and skilled automation engineers. That requirement illustrates the playbook complexity problem.
Governance and compliance matter especially in regulated environments, so teams should verify security controls such as role-based access control, SSO, encryption, audit trails, and certifications like SOC 2, GDPR, or HIPAA before choosing a low-code approach. Struct meets SOC 2 and HIPAA standards, which satisfies the compliance bar for most Seed-to-Series-C companies without forcing enterprise procurement cycles.
Why Struct Delivers First-Pass Investigations in Minutes
Struct deploys in five to ten minutes and integrates with leading observability platforms, Slack, GitHub, Linear, and Claude Code. The setup sequence follows the investigation data flow. Teams authenticate their alert source such as Slack or PagerDuty to capture when incidents fire. They connect their code repository like GitHub to correlate alerts with recent changes. They link observability tools such as Datadog, CloudWatch, GCP Logs, or Sentry to pull metrics and logs, then enable auto-investigations to trigger the AI workflow. No playbook scripting and no dedicated automation engineer are required.
Once live, Struct intercepts every alert, runs regression analysis, correlates anomalies across the full stack, and delivers a dynamically generated dashboard before the on-call engineer opens their laptop. The dashboard includes an impact summary, unified timeline, supporting charts, and suggested fixes. The Slack-native conversational AI allows engineers to ask follow-up questions, test alternative hypotheses, or pull additional log windows directly in the alert thread. Custom runbooks encode team-specific operational procedures so the AI investigates as a senior engineer would. When root cause is confirmed, Struct hands off to a local CLI, an AI coding agent, or generates a pull request directly.
Companies like FERMAT and Arcana use Struct to investigate thousands of alerts monthly, achieving the triage reductions described earlier. A Series A fintech with 40+ engineers and strict SLA requirements compressed its investigation workflow to match that benchmark after a sub-10-minute Struct setup, which protected SLA commitments and enabled junior engineers to take on-call shifts confidently.
Run your first AI-powered investigation with Struct and see how quickly your team reaches a completed first-pass report.
Frequently Asked Questions
How much can SOAR automation realistically reduce MTTR for engineering teams?
Results vary by stack maturity and logging quality, but the range is significant. Mainstream AI-assisted SOAR deployments reduce MTTR by 40–50% on average. Leading implementations, where logging is structured, trace IDs are consistent, and alert thresholds are tuned, report reductions up to 90%. Struct’s customers at scale report 80% reductions in active triage time, compressing a 30–45 minute manual investigation into a 5-minute review of an already-completed AI report. The prerequisite is a baseline observability stack with structured logs, alerting triggers, and at least one APM or exception-tracking tool such as Sentry or Datadog.
What is the difference between low-code SOAR and scripting-heavy SOAR for a small engineering team?
Scripting-heavy platforms like Cortex XSOAR and Splunk SOAR offer high customization ceilings but require a dedicated automation engineer or significant senior-engineer time to build, test, and maintain playbooks. For Seed-to-Series-C teams where engineering headcount is limited, this overhead becomes prohibitive. Low-code and no-code platforms such as Tines, Torq, and Struct reduce that burden substantially. Struct removes it entirely for the first-pass investigation use case because engineers paste their existing on-call runbook into Struct’s configuration, and the AI follows those procedures automatically without workflow design work.
Can Struct replace a traditional SIEM for security incident investigations?
Struct does not replace a SIEM. It acts as an automated investigation layer that sits on top of existing observability and alerting infrastructure. SIEMs aggregate and store log data for compliance, long-term retention, and broad threat detection across an organization’s full environment. Struct consumes that data, alongside Datadog metrics, Sentry exceptions, GitHub commits, and cloud logs, to perform the first-pass investigation automatically. Teams with an existing SIEM continue using it for log storage and compliance, while Struct accelerates the investigation workflow that starts after an alert fires.
What logging and telemetry quality does Struct require to function effectively?
Struct relies on the data available through its integrations. Teams already using Sentry for exception tracking, Datadog or CloudWatch for metrics and logs, and GitHub for code context will see the highest investigation accuracy. If a system lacks structured logging, consistent correlation IDs, or any alerting triggers, Struct cannot infer system state from code analysis alone. The platform targets engineering teams that have already adopted modern observability tooling and want to automate the manual work of correlating that data during an incident, not teams building their observability foundation from scratch.
How does Struct handle compliance requirements for fintech or healthcare engineering teams?
Struct maintains SOC 2 and HIPAA compliance, which covers the requirements of most Seed-to-Series-C companies in regulated industries. Log data is accessed and processed ephemerally during the investigation and is not stored in Struct’s systems beyond what is needed to generate the investigation report. Teams with strict enterprise requirements that prohibit log data from leaving their VPC or that require full on-premise deployment should evaluate whether Struct’s current architecture fits their security posture before proceeding.
Conclusion: Moving Toward AI-Standardized Incident Investigations
Future SOCs and engineering operations centers will increasingly rely on AI agents to analyze alerts, perform threat hunting, investigate incidents, generate security insights, and coordinate response actions. The NIST 800-61 incident response lifecycle, preparation, detection and analysis, containment or eradication and recovery, and post-incident activity, maps cleanly to what modern SOAR platforms automate. Teams that standardize AI-supported investigation practices now will compound that advantage as alert volumes grow and engineering organizations scale.
The platforms reviewed here span a wide range of deployment complexity, integration depth, and pricing models. The right choice depends on stack maturity, team size, compliance requirements, and tolerance for playbook maintenance overhead. For engineering teams that prioritize speed-to-value, observability-native enrichment, and minimal scripting overhead, Struct offers a direct path forward. Let Struct automate your on-call runbook so AI can complete your next incident investigation before your engineer reaches for their laptop.
