Torq excels at building complex, case-management-driven SOC workflows because its architecture treats every automation as a structured case with full audit trails. For example, a Torq playbook for phishing triage can automatically enrich an alert, pause for an analyst to review the evidence in a dedicated case view, and then execute containment actions only after explicit approval. This case-centric model ensures that human decisions are context-rich and fully documented for compliance.
Difference
Torq vs Tines: Security Automation HITL

Introduction
A data-driven comparison of Torq and Tines for building security automation playbooks that require human-in-the-loop approval before executing sensitive agent actions.
Tines takes a different approach by modeling automations as transparent, event-driven stories composed of discrete, reusable actions. Its strength lies in rapid prototyping and extreme flexibility, allowing a security engineer to build a human-in-the-loop approval gate by simply inserting an email or webhook action that waits for a response. This results in a lightweight, composable workflow that can be deployed in minutes, but it lacks a native, persistent case management layer for tracking long-running investigations.
The key trade-off: If your priority is a formal, auditable SOC workflow with integrated case management and rich analyst context for every decision, choose Torq. If you prioritize rapid experimentation, composable building blocks, and a lightweight approval pattern that can be embedded into diverse security and IT processes, choose Tines.
Feature Comparison: HITL and SOC Workflow Capabilities
Direct comparison of key metrics and features for building security automation playbooks that require human approval before executing sensitive agent actions.
| Metric | Torq | Tines |
|---|---|---|
Native Case Management | ||
SOC Playbook Templates (Pre-built) | 400+ | 50+ |
Approval Step Types | Multi-user, SLA-driven, Risk-based | Single-user, Timeout-based |
Avg. Playbook Execution Latency | < 2 sec | < 1 sec |
SIEM/SOAR Integration Depth | Bi-directional (API + Webhook) | Webhook-dominant |
Audit Trail Granularity | Step-level with payload diff | Story-level with input/output |
RBAC for Playbook Editors | ||
On-premise Deployment Option |
TL;DR Summary
A side-by-side look at the core strengths and trade-offs for embedding human approval gates into security automation playbooks.
Torq: Hyperautomation for the Enterprise SOC
Native case management: Torq embeds a full case management system directly into its automation fabric. This matters for mature SOC teams that need to link human decisions to specific alerts, artifacts, and timelines without switching to a separate ITSM tool.
Enterprise IAM depth: Offers pre-built steps for complex identity workflows (e.g., Okta group management, Just-in-Time access). This matters for zero-trust architectures where agent actions must be tightly scoped to ephemeral permissions.
Trade-off: The platform's breadth can introduce a steeper learning curve and higher cost, making it potentially over-engineered for teams that only need simple yes/no approval gates.
Tines: Composable Clarity for Rapid Response
Unmatched transparency: Tines' visual storyboard makes every agent action and human decision point instantly auditable. This matters for lean security teams that need to prove exactly what an agent did and why a human approved it during an incident post-mortem.
Lightweight HITL triggers: Sending an approval email or Slack message is a native, first-class action, not a plugin. This matters for DevSecOps pipelines where you need to insert a manual gating step into a CI/CD process in minutes, not hours.
Trade-off: Lacks a native case management backbone. For complex, long-running investigations that require persistent evidence lockers, you'll need to integrate an external system, adding architectural complexity.
Choose Torq for Case-Centric SOC Workflows
Best fit when: The human approval is part of a formal investigation. If your playbook requires an analyst to collect artifacts, write a summary, and then approve a containment action all within a single, auditable case file, Torq's integrated case management is the stronger architectural choice. It reduces the "swivel chair" between automation and ticketing systems.
Choose Tines for High-Velocity, Atomic Approvals
Best fit when: You need to build and change approval gates rapidly. If your primary need is to pause a playbook, send a "Deny/Approve" prompt to a Slack channel, and resume based on the response, Tines' simplicity is a strategic advantage. It allows non-developer analysts to safely wire human judgment into automated response loops without coding overhead.
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When to Choose Torq vs Tines
Torq for SOC Analysts
Strengths: Torq's hyperautomation platform is purpose-built for security operations, offering deep case management integration, pre-built SOC playbooks, and a rich UI for triage. Analysts can review agent-suggested actions within a unified case context, making it ideal for high-volume alert handling.
Verdict: Choose Torq when your primary goal is to accelerate Tier-1 SOC workflows with a platform that natively understands security context, evidence collection, and incident timelines.
Tines for SOC Analysts
Strengths: Tines offers a no-code, story-based approach that allows analysts to build custom automation quickly without heavy engineering support. Its strength lies in flexibility and rapid prototyping of response actions.
Verdict: Choose Tines when you need a lightweight, highly customizable automation layer that empowers analysts to build their own HITL workflows without waiting for a dedicated SOAR platform deployment.
Verdict
A final trade-off analysis to guide CTOs in choosing between Torq's case-management depth and Tines' composable story-building for security automation HITL.
Torq excels at building complex, case-centric security workflows because its architecture treats every investigation as a persistent, collaborative case. For example, a SOC analyst can pause an automated phishing playbook, enrich the alert with threat intelligence, and loop in a senior responder via a dedicated war room—all without losing the context of the original alert. This results in a 40% reduction in mean time to resolution (MTTR) for incidents requiring multi-analyst collaboration, as the platform natively integrates case management with the automation engine.
Tines takes a different approach by prioritizing composable, story-driven automation. Its strength lies in the simplicity of chaining actions into 'stories' that can be modified by any team member without specialized coding skills. While it offers human-in-the-loop (HITL) gates through its human_input action, the approval experience is more transactional. An analyst receives a prompt, makes a decision, and the story continues. This results in a faster automation build time—often measured in hours—but lacks the deep, persistent case context that Torq provides for long-running investigations.
The key trade-off: If your priority is a unified SOC workflow where case management, evidence collection, and multi-analyst collaboration are inseparable from the automation itself, choose Torq. If you prioritize rapid, democratized automation building where HITL is a lightweight gate within a broader, composable workflow, choose Tines. For enterprises needing a full-fledged security orchestration, automation, and response (SOAR) platform with embedded case management, Torq is the stronger fit. For teams seeking to augment existing tools with flexible, story-based automations that include simple approval steps, Tines provides a faster time-to-value.

About the author
Prasad Kumkar
CEO & MD, Inference Systems
Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.
His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.
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