SOC Automation 2.0: The AI Evolution in Security Operations

In today’s fast-paced world of cybersecurity, Security Operations Centers (SOCs) teams are key to monitoring and protecting businesses against cyber threats and other security incidents. 

However, with more malware, vulnerabilities, and emerging threats rearing their heads every day, SOC operations are failing to keep up. After all, relying solely on manual intervention leads to human error and isn’t practical anymore.  

For one, the ubiquity of SaaS apps, public cloud usage, and the rampant spread of ransomware are overwhelming security teams and human analysts with a flood of alert data, leading to alert fatigue and burnout. This is where SOC automation, and security automation in general, comes in.  

What is SOC automation? 

By automating mundane and manual tasks, SOC automation streamlines processes helps sift through the fog to identify real and potential threats and vulnerabilities, reduces overall risk, and aids incident response. For instance, SOC automation can handle threat intelligence enrichment or IP reputation lookups, giving analysts the context they need without manual effort.  

Many Endpoint Detection and Response (EDR) tools can also automatically contain hosts, block file execution, and kill processes based on set rules, which speeds up response times via automated responses. 

But automation isn’t a silver bullet. Despite automation advancements, investigating alerts is still mostly a manual job, and the number of alerts has only gone up over the past five years. Some automated tools and other security tools meant to lighten the load for analysts in a SOC can actually add to it by generating even more alerts that need human attention. 

In this blog, we’ll dive into the wins and challenges of SOC automation, giving a clear picture of the present state of SOC automation and what tools can be used to get the highest impact. 

Why is SOC automation needed? 

The almost overnight mass exodus to remote work brought on by the pandemic dissolved the perimeter to a point where it was impossible to keep up with security events, and cyber threat pacing using human analysts alone. In fact, we’ve discussed in detail some of the leading challenges SOC analysts and managers experience on a regular basis, going head-to-head with adversaries. 

There are many use cases for SOC automation: 

• Alert Triage - In SOC automation, alert triage prioritization is the name of the game. Security alerts are filtered using AI and predefined rules, reducing false positives and ensuring SOC analysts focus on real security threats.  

• Maintain Consistency - Use consistent methodologies and documentation for event management, and each investigation that’s performed by the team to empower a more effective incident response. 

• Eliminate Repetitive Tasks: SOC automation eliminates time-consuming, repetitive tasks by leveraging AI and machine learning to streamline threat detection, incident response, and log analysis, allowing analysts to focus on complex security challenges. 

• Immediate Response - Automated responses to identify threats and vulnerabilities contain hosts, and block indicators of compromise on successful identification. 

• Threat Hunting: Proactive Detection – Continuously analyzes telemetry and threat intelligence to uncover emerging threats and security events and identify anomalies, mitigating risks before they escalate. 

In the absence of strong SOC automation, teams can be left with: 

• Alert fatigue and drowning in false positives 

• Long alert dwell times and high mean time to response 

• Inconsistent remediation or incident response  

The challenges of existing SOC automation solutions 

Today teams are left with two primary options (or a hybridization of the two) to improve their automation – homegrown solutions or using a SOAR tool - customization is key here. All come with challenges: 

Integration complexity 

SOAR platforms were introduced to unify fragmented security workflows, promising seamless integration across SIEMs, threat intel feeds, case management, and response tools. On paper, they reduce manual effort and streamline incident response. In reality, the work required to make these integrations usable is anything but seamless.

Checking the box for an integration is easy. Making it work the way your team operates is not. Out-of-the-box connectors often fall short of real-world needs, forcing teams to build and maintain custom integrations themselves. And the engineers writing those integrations are rarely the same ones responding to alerts—leading to a handoff gap that turns security engineers into part-time product managers defining requirements and managing technical complexity. It’s a costly detour from core security work.

High cost for implementation and maintenance 

Whether you choose a commercial SOAR platform or build your own orchestration using API calls, Jupyter notebooks, or custom scripts, both paths come with significant ongoing investment. To make automation truly effective, teams must allocate dedicated headcount—not just for initial setup, but for continuous tuning, maintenance, and expansion as threats evolve.

Even small changes can introduce risk. A single update to a vendor’s API or a bug in your internal stack can break workflows, delay outcomes, and eat into already limited security engineering bandwidth. What starts as an efficiency effort can quickly become a maintenance burden.

Limited ability to manage complexity or change 

Traditional automation—whether SOAR-based or homegrown—is rigid by design. When new alert types emerge, your team must manually codify new playbooks, craft enrichment steps, and hardwire logic for investigation. The system won’t learn from previous patterns or dynamically adapt to new scenarios on its own.

This static approach means every change—whether in alert logic, threat behavior, or detection coverage—requires human intervention. And as the environment grows more complex, so does the time and effort required to keep automation relevant and effective.

How AI enhances SOC Automation 

If you read the heading of this section and gagged a bit, you’re not alone. Despite the fact that “AI” has been the two-letter acronym that’s been security product buzzword bingo for the last 10 years, collectively, investigations have been getting more challenging, not less. In other words, the promise of AI hasn’t always materialized.  

So what’s different now? Like any tool, AI isn’t the mythical solution that’s going to solve all of our problems, but with the rapid development of large language models (LLMs) alongside other artificial intelligence (AI) technologies like traditional machine learning and agentic architectures, SOC teams can improve their overall security posture, and start to realize more of the benefits that have been touted for the better part of a decade. 

Rules-based SOAR solutions or homegrown implementations may fall short in achieving the desired automation if they require users to hand-build playbooks for each variant of an investigation. However, modern LLMs (when steered correctly) alongside more traditional classifiers can do an impressive job managing variation and building out investigative plans for alerts that don’t require your team’s intervention. 

It also extends the opportunity for more complex decision-making outside of simple conditional statements while learning from your environment and user feedback as it continues to get better. These AI capabilities have been out of reach for security teams up until now. 

How Prophet Security can help 

At Prophet Security, we envision a world where security analysts aren’t spending their time writing integrations, building runbooks, or meticulously tuning detections. With Prophet AI SOC Analyst, we automatically investigate your alerts by leveraging your existing data sources with no integrations or workflow-building required. Request a demo of Prophet AI to learn how you can triage, investigate, and respond to security alerts 10 times faster. 

Frequently Asked Questions (FAQ)

What is SOC automation?

SOC automation refers to the use of technology to automate repetitive tasks in a Security Operations Center. This includes alert triage, enrichment, response, and reporting—reducing analyst workload and improving response times.

Why is SOC automation important for modern security teams?

SOC automation helps security teams scale by reducing alert fatigue, shortening response times, and minimizing human error—especially in high-volume, fast-paced environments.

What tasks can be automated in a SOC?

Common tasks include alert triage, threat intelligence enrichment, log analysis, phishing email quarantine, and host containment. Some platforms also automate ticketing and reporting.

How does AI improve SOC automation?

AI enables dynamic, context-aware investigations without rigid playbooks. It can adapt to variation, reduce false positives, and learn from data over time—delivering faster, more accurate outcomes.

What are the limitations of traditional SOC automation tools like SOAR?

SOAR tools require complex setup, custom integrations, and constant maintenance. They rely on brittle playbooks and struggle to adapt to new or nuanced threats.

Is AI replacing SOAR in security operations?

Yes. AI SOC Analysts are replacing SOAR in many environments by delivering the efficiency, adaptability, and automation that SOAR platforms promised but never achieved. Unlike rigid playbooks, AI can autonomously investigate alerts, handle variation, and scale without manual maintenance—making SOAR tooling increasingly obsolete.

Can AI reduce false positives in alert triage?

Yes. AI reduces false positives by correlating data across systems, learning normal behavior, and prioritizing alerts based on real risk—not static thresholds.

What are the biggest challenges with SOC automation today?

Challenges include integration complexity, high setup and maintenance costs, and the inability of legacy tools to adapt to new threats without manual intervention.

Do I need to replace my SIEM or EDR to benefit from AI SOC automation?

No. AI SOC Analysts work alongside your existing tools like SIEM, EDR, and identity systems—enhancing their value without requiring replacements or complex integrations.