One of the hottest cybersecurity buzzwords to emerge in 2024, which will undoubtedly be discussed at length at the upcoming 2025 RSA Conference, is the “autonomous security operations center (SOC).” Larger security vendors are racing to be the first to put a stake in the ground with product launches, making grand claims of being the industry’s first fully autonomous SOC that can detect threats faster than human security analysts.  

CISOs might be tempted to believe the hype and imagine a black box capability where high-priced humans are no longer required, but that’s hardly the case. A SOC is not the same as, say, a self-driving car that has only one function: To move a passenger from point A to point B. That kind of singular, hands-off approach to autonomy simply doesn’t translate to an autonomous SOC.  

The biggest myth of the autonomous SOC hype? That your SOC is, indeed, fully autonomous.

AI/Human Augmentation, Not Outright Human Replacement 

It’s true that advanced forms of AI, such as generative AI and machine learning, are transforming traditional SOCs, which benefits everyone. Security analysts are freed from manual, labor-intensive processes that take up too much of their time so they can focus on more complex problem-solving and investigations. AI helps tune out the noise of false positive security alerts so security analysts can dig into real ones, which means fewer threats slip through the cracks.  

Yet, even as these technologies evolve and take on more sophisticated tasks, human expertise will remain essential in the SOC. The term “Autonomous SOC” is misleading. The truth is that humans will always be required in the security operations center.

Real Humans, Real Problems 

Traditional SOCs (and particularly the humans that run them) have surfaced some common challenges, such as: 

• Alert fatigue: Analysts are overwhelmed with thousands of alerts per day, many of which are false positives. Clearing or investigating alerts leads to workload backlogs, inconsistent investigations and responses, missed threats and a lot of stress. 

• Response time: SOCs often struggle to proactively identify and address threats before they impact the organization, and new or zero-day attacks are difficult to manage. 

• Analyst burnout: The demanding nature of SOC work, coupled with the constant pressure to keep up with threats, leads to high turnover rates. 

• Skills gap and labor shortage: Mid-market companies often employ budget-friendly, lean security teams focused on lots of different security initiatives at once. As a result, personnel may lack certain technical, sophisticated skills required for configuring static rules for complex SIEM products. Further, attack vectors are evolving faster and increasingly complex, while security analysts have little time for additional education and on-the-job training. 

The concept of the autonomous SOC exists to address these challenges and much more.  

What is an “Autonomous SOC,” Really?

Let’s dispel yet another myth. Contrary to the name, an autonomous SOC (commonly referred to as “AI SOC” or “AI-powered SecOps environment”) was never meant to be a completely automatic model. Instead, it is a programmatic model for introducing strategic, critical layers of automation and streamlining into the security operations center, helping human security teams scale operations and rapidly secure their environment—without sacrificing personnel and costs, or weakening the overall security posture.

Integrating AI into an autonomous SOC helps address challenges by automating repetitive tasks, improving visibility into all aspects of the network, and improving anomaly detection.  

Humans in the Loop 

In the world of AI and ML design, there’s a common approach called human in the loop (HITL). This collaborative approach integrates human input and expertise into the lifecycle of AI systems. Humans are active participants in the training, evaluation, and operation of the model, providing valuable input, observation, guidance, feedback, and annotations. Using this approach has many benefits. Most importantly, HITL enhances the quality, accuracy, reliability, and adaptability of the AI system, harnessing the unique capabilities of both humans and machines.  

To ensure autonomous SOCs are truly a game-changing success and not just another cybersecurity buzzword, we still need human experts to: 

• Train LLMs and Set Policies: AI mechanisms rely on vast data models (LLMs), but humans are the ones who properly train them. We also need analysts to create and evolve the policies that guide AI decision-making.  

• Contextualize complex threats: AI can detect anomalies, but understanding the nuances of an attack and its potential impact requires human analysis and interpretation. 

• Make strategic decisions: Cybersecurity decisions must often consider risk tolerance, business impact, and ethics. These decisions require human judgment that AI can’t replicate. 

• Provide conversational feedback: The most important aspect of the autonomy journey will be the continuous conversational feedback the autonomous SOC will interact with analysts to ensure verification, validation and remediation actions.  

The Future of the Autonomous SOC: Human-AI Collaboration 

As we can see, the autonomous SOC will not be about replacing human analysts, but rather augmenting their capabilities. In the SOC of the future, AI will handle routine tasks (such as alert triage, threat data enrichment and basic incident responses), leaving human analysts to deliver complex threat analysis, strategic decision-making, and AI policy refinements. This collaborative approach will support SOCs that: 

• Reduce analyst alert fatigue and burnout. 

• Improve threat detection accuracy. 

• Respond to incidents more quickly and effectively. 

• Shift SOC activity from reacting to threats that have already happened to proactively guarding the infrastructure. 

Ultimately, the goal of the autonomous SOC is to create a more efficient and effective security environment where human analysts and AI work together to achieve a higher level of security than either could achieve alone. Working together, each improves the other.

In the future, an autonomous SOC could even help solve the cybersecurity skill shortage using adaptive technology (including AI), helping less sophisticated security teams or generalists achieve a similar outcome otherwise produced by complex, manual rule building.

Indeed, human security analysts will always be crucial to security operations, regardless of the level of autonomy introduced.