Automated Threat Hunting: Integrating SOAR and AI to Proactively Find Hidden Adversaries

What could an adversary accomplish with 20 days inside your network? The unfortunate reality is that the average adversary dwell time remains stubbornly high, giving them more than enough time to map your environment, escalate privileges, and find your most critical data. While your SIEM and EDR are essential for catching known threats, they often leave you in a reactive posture. You’re waiting for an alarm to go off. It’s time to stop waiting and start hunting. The good news is that we now have the tools to do this at scale. Effective automated threat hunting is no longer a theoretical concept. By integrating Security Orchestration, Automation, and Response (SOAR) with Artificial Intelligence (AI), we can build a proactive defense that actively seeks out adversaries before they complete their mission.

This isn’t about replacing your skilled analysts. It’s about empowering them. It’s about automating the repetitive, time-consuming tasks so your human experts can focus on complex investigations and strategic defense. Overwhelmed security teams struggling with alert fatigue simply don’t have the bandwidth for manual, proactive hunting. The adversary knows this and exploits it. We need to fight automation with automation.

How SOAR Supercharges Automated Threat Hunting

Think of your security tools: your SIEM, EDR, firewalls, and threat intelligence feeds, as individual specialists. They are powerful but often don’t talk to each other efficiently. A SOAR platform acts as the central coordinator, the operational hub that gets these tools working together in a unified, automated workflow. For automated threat hunting, SOAR is the engine that executes your hunting playbooks at machine speed, 24/7.

So how does this work in practice? Instead of an analyst manually running queries, cross-referencing IP addresses, and pulling user data, a SOAR playbook can do it in seconds. This directly answers the question of how to automate repetitive hunting tasks. A playbook is a pre-defined set of actions that triggers based on a specific hypothesis or a low-fidelity indicator. For example, a hypothesis might be: “An adversary is using a common administrative tool like PowerShell for malicious purposes.”

A manual hunt for this might take an analyst hours. An automated SOAR playbook executes instantly:

1. Trigger: The EDR detects a PowerShell process launched by a non-standard application like Microsoft Word.
2. Enrichment: The SOAR platform automatically queries your Active Directory to get the user’s role and privileges. It queries your threat intelligence platform to check the reputation of any outbound network connections. It queries your asset management database to determine the criticality of the endpoint.
3. Triage: Based on the enriched data, the playbook can make an initial decision. Is this a system administrator performing a legitimate task, or is it a standard user on a finance workstation exhibiting highly anomalous behavior?

This level of automation is a game-changer. Organizations that properly implement SOAR for automated threat hunting can investigate three times more hypotheses than teams stuck with manual processes. It allows you to scale your hunting efforts without proportionally scaling your headcount, turning your defense from a series of disconnected actions into a cohesive, automated system.

The Role of AI and Machine Learning in Finding the Unseen

If SOAR is the engine for automation, AI is the intelligence that guides it. Traditional security tools are great at finding threats we already know about through signatures, rules, and known indicators of compromise (IOCs). But what about the novel attacks or the subtle techniques that don’t match any known pattern? This is where AI and machine learning (ML) become critical.

AI-driven threat detection excels at establishing a baseline of normal activity in your unique environment. It learns what ‘right’ looks like for your network traffic, your endpoint processes, and your user behavior. It understands which users typically access which servers, what processes normally run on a developer’s machine, and the typical data flow from your financial systems. Once this baseline is established, the AI can spot subtle deviations that would be nearly impossible for a human analyst to find in a sea of data.

This directly addresses the core challenge of finding hidden threats. An adversary trying to blend in might use legitimate credentials and system tools. A signature-based system won’t see anything wrong. But an AI model might detect a combination of barely-off-normal events:

• A user who normally works 9-to-5 logs in at 3 AM.
• They access a server they’ve never touched before.
• They use a standard administrative tool to exfiltrate a small amount of data, just under the threshold of a normal data loss prevention (DLP) rule.

Individually, each of these events might be a low-priority alert that gets ignored. But the AI model, understanding the context and the chain of events, can identify this pattern as a high-confidence indicator of a compromise. This is the power of AI-driven analysis. It finds the quiet, methodical adversary who is trying to live off the land.

Practical, High-Value Automated Hunting Playbooks

Theory is great, but practical application is what solves problems. Let’s look at a concrete example of an automated hunting playbook you can implement to find adversaries using living-off-the-land (LotL) techniques. These attacks are difficult to detect because they use legitimate tools already present on your systems.

Hypothesis: An adversary is using rundll32.exe to execute malicious code from a script or network share, a common LotL technique.

Automated SOAR/AI Playbook:

1. Data Collection & Trigger: The EDR system continuously monitors process execution. The trigger for this playbook is any instance of rundll32.exe being launched with unusual command-line arguments or by a parent process like winword.exe or outlook.exe.

2. Automated Enrichment (SOAR):

   • The SOAR platform ingests the alert.
   • It pulls the full process tree and command-line arguments from the EDR.
   • It queries threat intelligence feeds with any file hashes or domains found in the command line.
   • It retrieves the user and host information from internal systems.

3. AI-Powered Analysis (ML Model):

   • The enriched data is fed into an ML model trained to spot suspicious rundll32.exe usage.
   • The model analyzes features like the parent process, the presence of network connections, and whether the command is trying to execute code directly from memory.
   • It generates a risk score. A low score might indicate a legitimate, if unusual, software installer. A high score indicates a likely threat.

4. Tiered Automated Response (SOAR):

   • High Risk Score: The SOAR playbook automatically executes a pre-approved response. It can isolate the host from the network to stop any potential lateral movement and create a critical-priority ticket in your ITSM platform, assigning it directly to a senior analyst. All the enriched data is included in the ticket.
   • Medium Risk Score: The playbook creates a medium-priority ticket for investigation but takes no immediate containment action, allowing an analyst to review the activity before acting.

This single playbook automates the entire discovery and initial response process. It allows your team to hunt for a specific, high-impact TTP across your entire enterprise without requiring a single minute of manual analyst time until a credible threat is found.

Your security team is likely stretched thin, but the threats aren’t slowing down. Adversaries are using their own forms of automation to attack at scale. A defense that relies solely on manual processes and reacting to high-fidelity alerts is no longer sufficient. By integrating the orchestration power of SOAR with the pattern-recognition capabilities of AI, you can fundamentally shift your security posture. You can build a system for automated threat hunting that tirelessly searches for the threats you don’t yet know exist. The future of defense is proactive, and the technology to get there is available today.

Shift your security posture from reactive to proactive. Let’s design and implement automated threat hunting workflows tailored to your environment.