The modern enterprise Security Operations Center (SOC) faces a daunting challenge – handling an overwhelming number of alerts on a daily basis. On average, an enterprise SOC receives about 10,000 alerts each day, each of which requires between 20 to 40 minutes of investigation. However, even with fully staffed teams, only 22% of these alerts can be effectively addressed. This leaves a significant gap in the security monitoring process, with more than 60% of security teams admitting to ignoring critical alerts that were overlooked.
The traditional model of SOC operations is no longer sufficient to keep pace with the evolving threat landscape. Tier-1 analyst tasks such as alert triage, enrichment, and escalation are now being automated through software functions, with supervised AI agents taking on a more prominent role in handling the volume of alerts. Human analysts are shifting their focus to more strategic tasks such as investigation, review, and making critical decisions in complex scenarios. This shift has led to a reduction in response times and improved efficiency in threat detection and response.
However, the integration of human insight and intuition remains crucial in the SOC environment. According to Gartner, over 40% of agentic AI projects are projected to be canceled by the end of 2027, primarily due to unclear business value and inadequate governance. It is essential for organizations to get the change management process right and ensure that generative AI does not disrupt the SOC operations.
The need for change in the legacy SOC model is evident, as burnout among senior analysts is becoming increasingly prevalent. Legacy SOCs often rely on multiple systems that deliver conflicting alerts and lack interoperability, leading to inefficiencies and increased stress on security teams. The rapid evolution of cyber threats, with breakout times as fast as 51 seconds and a rise in malware-free intrusions, further emphasizes the need for a more agile and efficient SOC model.
To address these challenges, SOC deployments are adopting a model of bounded autonomy, where AI agents handle routine tasks such as triage and enrichment, while human analysts oversee critical decisions that carry operational risk. This division of labor enables SOC teams to process alerts at machine speed while maintaining human judgment in high-stakes scenarios.
Leading technology providers like ServiceNow and Ivanti are spearheading this shift towards agentic IT operations, with a focus on improving threat detection and response capabilities. Gartner predicts a significant increase in the adoption of multi-agent AI in threat detection, with ServiceNow investing billions in security acquisitions to enhance its capabilities. Ivanti has introduced agentic AI features for IT service management, bringing the bounded-autonomy model to the service desk and enabling organizations to streamline their operations.
In conclusion, the future of SOC operations lies in embracing AI-driven technologies while maintaining human oversight and governance. By implementing bounded autonomy and defining clear governance boundaries, organizations can enhance their threat detection capabilities and improve response times. Security leaders must prioritize workflows that are ripe for automation and validate the accuracy of AI-driven decisions to ensure resilience in a constantly evolving threat landscape.