What is an Autonomous SOC? Can You Build One Today?

Security operations centers (SOCs) are inundated with alerts, many of which require time-consuming investigations that turn out to be false positive/benign. The idea of an “Autonomous SOC” has gained traction, fueled by advances in Agentic AI promising full automation. As a result, AI-driven automation is frequently touted as the answer to many SecOps challenges, with some vendors claiming a fully autonomous SOC is imminent. However, the reality is more nuanced. 

An Autonomous SOC, in its truest sense, would need to handle everything from detection engineering to forensic analysis and coordinated incident response without human input. Today’s AI SOC Analysts can automate triage and investigation and some low-risk remediation action, but true autonomy remains an aspirational goal. Rather, organizations would benefit from assessing tools that can help streamline their operations and augment SOC needs, allowing humans to focus on the difficult questions.

The current state of SOC automation

Investigating alerts is just as much about volume as it’s about the time investment, with each case taking ~30 minutes to triage and investigate. This inefficiency drains resources, leads to analyst fatigue, and forces skilled professionals to focus on low-value, little-return tasks instead of real threats. Many organizations have turned to automation solutions like SOAR (Security Orchestration, Automation, and Response) and outsourced MDR/MSSPs (Managed Detection & Response, Managed Security Service Providers), but these come with trade-offs.

• SOAR tools: Automates enrichment workflows but requires heavy engineering investment to build and maintain complex playbooks and integrations.
• MDR/MSSPs: Offloads alert triage but introduces transparency issues, lacks coverage for custom detections, and can escalate too many false positives back to the customer.

Challenges in achieving full autonomy

AI-powered automation offers relief by handling high-volume tasks like alert triage and investigation. However, automation alone cannot fully replace human expertise. In practice, AI-driven SOC tools will struggle with tasks requiring deeper contextual awareness, such as:

• Detection engineering – AI can assist in rule creation but lacks the adaptability of experienced security engineers.
• Incident response coordination – While AI can suggest actions and even perform low-risk remediation, human oversight remains essential for containment and mitigation for more complex scenarios.
• Threat hunting – AI can identify patterns and even provide a natural language interface to make threat hunting easier for anyone, but a human must ask those questions of the AI. We're not at a point where AI can autonomously execute threat hunts.

There's also a concern with skills erosion and its impact on developing senior-level talent in the SOC, as L1 and L2 triage and investigation is fully automated.

Then there are challenges inherent to today's AI and SOC teams:

• Accuracy limitations – Hallucinations remain a concern, albeit much less so with the newer AI models and RAG architectures. A fully autonomous SOC would have to solve the problem of hallucinations. 
• Over-escalation – Low quality AI-driven investigations can actually increase the number of escalated alerts requiring human validation, shifting rather than eliminating workload.
• Integration complexity – Most enterprises operate a patchwork of security tools, many of which lack seamless interoperability, making full autonomy impractical.
• Adversarial adaptation – As AI-based defenses evolve, attackers develop new techniques to evade them. Automation must continuously adapt to remain effective.

The reality: AI SOC Analysts and human-in-the-loop validation

Instead of chasing full autonomy, the immediate opportunity is leveraging AI SOC Analysts to handle L1 and L2 investigations. This approach ensures that the vast majority of alerts are resolved without human intervention, with only a small fraction requiring validation.

Security teams should assess AI’s impact based on measurable operational improvements rather than vendor hype. Organizations should prioritize AI-driven automation for scaling security workflows, enhancing analyst productivity, and improving investigative depth, rather than attempting to replace human decision-making outright.

How Prophet AI is paving the way for an autonomous SOC

At Prophet Security, we believe in empowering, not replacing, SOC analysts. Prophet AI is designed to autonomously triage and investigate alerts, applying human-like reasoning to generate clear, evidence-backed insights. Unlike many AI tools, Prophet AI provides transparency, ensuring analysts understand why decisions were made and allowing for human oversight where necessary. 

Automation should make security teams more effective, not obsolete. By embracing AI as an augmentation tool, organizations can build a more resilient and adaptive security operation that leverages the speed of AI without sacrificing the depth of human expertise.

Request a demo > to see Prophet AI in action. 

‍