Beyond Static SOAR: Scaling Security Automation for Modern Threats

Wipro is redefining security operations through the introduction of SMC-AI 2.0, focusing on agentic automation and orchestrated responses to address the limitations of traditional security frameworks. This development comes as human-driven workflows, static Security Orchestration, Automation, and Response (SOAR) playbooks, and scripted automations struggle to maintain pace with the increasing volume and complexity of modern cyber threats.

Traditional SOAR platforms were designed to combat alert fatigue by automating repetitive tasks and coordinating workflows across various security tools, such as SIEMs, firewalls, and threat intelligence feeds. These platforms typically rely on three core pillars: orchestration to connect disparate tools, automation to execute rule-based tasks like blocking IP addresses, and response to standardize how teams handle alerts.

The Limitations of Static Playbooks

Despite their initial utility, traditional SOAR models are reaching an operational ceiling. Industry data indicates that static playbook automation often caps coverage at 30% to 40%, as these systems cannot scale to handle the exponential growth of daily alert volumes, which can exceed 10,000 for large organizations.

Because static playbooks follow predefined logic, they are unable to reason through ambiguity or adapt to novel attack variations. This rigidity often results in a significant portion of alerts remaining uninvestigated, with some reports suggesting that 40% of total alert volume is never reviewed and 61% of SOC teams have missed real compromises.

Transition to Agentic and Autonomous SOCs

The shift toward agentic automation represents a move from executing predefined steps to reasoning about which steps are necessary. In an agentic SOC model, specialized AI agents investigate alerts, hunt for threats, and collaborate on responses without requiring a predefined playbook for every scenario.

This autonomous architecture differs from traditional SOAR by utilizing purpose-trained cybersecurity Large Language Models (LLMs). These models can autonomously investigate every alert by tracing attack paths and correlating data across the full technology stack to generate bespoke response playbooks at runtime.

The objective of this transition is to eliminate the capacity gap caused by flat analyst staffing levels in the face of rising threat volumes. By moving toward autonomy, organizations aim to increase alert coverage toward 100% and reduce triage time.

Hyperautomation as a Paradigm Shift

This evolution is part of a broader trend toward hyperautomation in cybersecurity. Hyperautomation is described as a fundamental paradigm shift rather than a simple upgrade to existing tools, promising to transform how organizations manage risk and operate their overall security posture.

While traditional SOAR served as a frontline defense by codifying response procedures, the current operating environment—characterized by AI-generated attack variations and rapidly shifting cloud architectures—requires a system that can adapt in real time. The move toward agentic automation allows security operations to handle the unpredictability of modern adversaries who can generate new attack patterns faster than human authors can update static playbook logic.