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Torq vs Tines: Security Automation HITL

A technical comparison of Torq and Tines for building security automation playbooks that require human approval before executing sensitive agent actions, focusing on SOC workflow integration, case management, and risk-based escalation.
Operations team reviewing AI workflow automation on laptop, workflow builder visible, casual office setup.
THE ANALYSIS

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.

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.

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.

HEAD-TO-HEAD COMPARISON

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.

MetricTorqTines

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

Torq vs Tines: Security Automation HITL

TL;DR Summary

A side-by-side look at the core strengths and trade-offs for embedding human approval gates into security automation playbooks.

01

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.

02

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.

03

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.

04

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.

CHOOSE YOUR PRIORITY

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.

THE ANALYSIS

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.

Prasad Kumkar

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.