What is human-in-the-loop?
Human in the loop (HITL) is a term used to describe systems, especially those involving artificial intelligence (AI) or security automation, where human oversight or intervention is an essential part of the process. Rather than leaving all decision-making to machines, HITL systems ensure people remain involved - either by supervising outputs, providing input, correcting errors, or making the final call when it matters most.
The "loop" can involve different levels of human interaction. In some cases, a person trains the systems by labeling data or giving feedback (think of teaching a chatbot to respond more accurately). In others, they monitor the AI's performance in real time and step in when the systems encounter uncertainty or ambiguity. Ultimately, HITL systems aim to combine the strengths of humans and machines to create smarter, safer, and more reliable outcomes.
How human-in-the-loop works in cybersecurity
In cybersecurity, HITL systems serve as a vital bridge between automated tooling and human expertise – especially in fast-paced environments where context and judgment are essential. While AI-driven systems and automation platforms can accelerate detection and response (D&R), HITL ensures skilled analysts remain engaged at key decision points. This approach is particularly critical in the operations of security orchestration, automation, and response (SOAR) platforms and managed detection and response (MDR) services.
Threat detection and triage
Automated detection engines – often integrated into SOAR and security information and event management (SIEM) platforms – generate alerts based on suspicious behavior, anomaly detection, or threat intelligence feeds. HITL ensures human analysts are looped in to validate and enrich these alerts.
Security operations and incident response
SOAR platforms can execute playbooks automatically – isolating endpoints, resetting credentials, or blocking IPs without manual intervention. But not every situation fits neatly into a pre-scripted response. HITL workflows are typically baked into daily operations, with analysts monitoring playbook execution, making judgment calls when automation reaches a decision boundary, and adjusting actions in real time.
Model training and adaptive defense
Whether tuning detection rules in a SOAR platform or feeding intelligence into an MDR system, human feedback is crucial for keeping security defenses relevant. Analysts contribute to training models by labeling phishing attempts, flagging missed detections, or identifying benign activity that was wrongly flagged.
Human-in-the-loop vs. fully automated systems
Fully automated systems are designed to operate independently – detecting threats, making decisions, and executing actions without any human involvement. While these systems are fast and scalable, they can also be rigid, context-blind, and vulnerable to making high-impact mistakes when faced with unfamiliar or ambiguous situations.
HITL systems, on the other hand, intentionally build human input into the decision-making process. The goal isn’t to slow things down – it’s to make sure automation is informed, contextual, and accountable. HITL ensures a human is involved at key points: validating alerts, approving responses, labeling training data, or overriding automation when necessary.
To better understand where HITL sits in the spectrum of automation, it's helpful to contrast it with two related models:
Human-on-the-loop
In a human on the loop setup, systems act autonomously, but a human oversees the process and can intervene if needed. The analyst isn’t directly involved in every decision but maintains situational awareness and can override automation in exceptional cases. Think of it as autopilot mode with a pilot in the cockpit.
Human-over-the-loop
In a human over the loop model, humans design, configure, and deploy automated systems but aren’t directly involved in day-to-day decisions or oversight. The system runs independently unless a major review or update is needed. Think of it as setting a course and stepping away until a system check is required.
Unlike its more hands-off counterparts, HITL keeps people closely engaged in active decision-making. In cybersecurity, where context can shift rapidly and the cost of a wrong call can be steep, HITL offers a more balanced approach.
Use cases in detection and response
D&R workflows are a prime arena for HITL systems. While automation helps security teams move faster, HITL ensures that critical thinking, domain expertise, and contextual awareness stay in the mix.
Alert triage and validation
Security teams are inundated with alerts – many of which turn out to be noise. HITL allows automation to handle initial detection and correlation, while human analysts step in to assess intent, impact, and priority.
Incident scoping and enrichment
Automation can flag suspicious behavior, but understanding the full scope of an incident often requires human intuition. HITL workflows enable analysts to dig deeper – correlating activity across systems, enriching events with business context, and identifying affected assets or users.
Response approval and override
Many organizations use SOAR platforms to automate response actions like isolating endpoints or disabling accounts. HITL adds a control layer by routing these actions through human review. Analysts can approve, modify, or override the automation based on contextual factors.
Phishing investigation and classification
Email security tools may quarantine suspicious messages, but determining whether they’re truly malicious often requires human judgment. Analysts in an HITL loop review quarantined messages, classify them for training models, and provide feedback that improves future detection accuracy.
Threat hunting and hypothesis testing
While automation helps surface anomalies, proactive threat hunting often starts with a human hypothesis. Analysts leverage automated queries and detection logic, but drive the process themselves – testing ideas, refining searches, and interpreting results. HITL enables this back-and-forth between machine-driven scale and human-driven strategy.
Human-in-the-loop benefits
HITL systems offer a powerful blend of speed, scale, and situational awareness. By combining automation with expert oversight, HITL workflows help security teams stay agile, accurate, and adaptable. Let’s take a look at some key benefits of incorporating HITL into detection and response.
- Reduced false positives: Analysts can quickly dismiss benign alerts that automation flags, reducing alert fatigue and helping teams focus on real threats.
- Faster time-to-resolution: HITL allows for rapid automation of common tasks, while still involving humans for judgement calls. This speeds up digital forensics investigations without compromising accuracy.
- Context-aware decision-making: Humans bring business context, institutional knowledge, and critical thinking that machines lack, helping ensure responses are proportional and well-informed.
- Continuous learning: Human review and feedback loops allow machine learning systems to evolve, improving both precision and recall in future detections.
- Risk mitigation: By involving humans before critical actions (like device isolation or user lockdown), organizations better adhere to compliance and regulatory standards as well as avoid unnecessary disruptions or reputational harm.
- Improved collaboration: HITL workflows encourage better communication between analysts, tools, and teams – leading to more cohesive and informed decision-making.
Human-in-the-loop challenges
While human-in-the-loop systems offer clear advantages, they also introduce operational and design challenges. Balancing automation with human input requires thoughtful workflows, skilled personnel, and clear boundaries of responsibility. Below are key challenges organizations face when implementing HITL in cybersecurity.
- Scalability limitations: HITL relies on human attention, which can become a bottleneck as alert volume – and perhaps analyst fatigue – grows.
- Inconsistent decision-making: Human judgement can vary between analysts or shifts, leading to uneven responses – unless playbooks and escalation criteria are well defined.
- Training and expertise requirements: Effective HITL depends on knowledgeable analysts who can recognize subtle threats, interpret automation outputs, and provide high-quality feedback.
- Maintaining feedback quality: For systems that rely on human input to train detection models, poor or inconsistent feedback – or delayed responses – can degrade detection quality instead of improving it.