Security teams are shifting the debate around AI in the SOC away from the fundamental question and toward the targeted selection of which AI platform delivers the greatest benefit for a specific task — such as detection engineering or alert triage.
SOC practices for OT and ICS environments require specialized architectural models and organizational adaptations to meet regulatory requirements such as the NIS2 Directive while ensuring the availability of critical systems.
The classical endpoint detection model has failed because 79% of modern attacks are malware-free and require multi-layered detection systems with behavioral analysis.
Detection Engineering replaces generic vendor rules with tailored, behavior-based detection mechanisms that align with an organization’s specific infrastructure and threat landscape.
Autonomous AI agents are designed to integrate fragmented security infrastructures and reduce response times, requiring organizations to redefine their processes and automation boundaries.
CISOs must translate technical SOC insights into understandable business risks to anchor security measures as business priorities; only those who clearly articulate the impact on processes, finances, and reputation secure the necessary investments.
