Brief

Despite advancements in AI for security operations centers (SOCs), many still struggle with high mean time to resolution (MTTR), analyst burnout, and missed attacks. Current AI deployments excel at correlating alerts and providing investigation starting points, reducing raw alert volume and false positives significantly. However, AI's effectiveness is limited by fragmented systems, data quality, and workflow integration, particularly in the post-detection phase where coordination and approvals cause significant delays. AI

Critical infrastructure is increasingly integrating AI, expanding its attack surface to include models, data, and ML pipelines. Traditional security measures and human-only Security Operations Centers (SOCs) are overwhelmed by the volume of data and the speed of AI-native attacks. To counter this, organizations must adopt AI SecOps, embedding continuous security checks into operational pipelines and using AI-driven tools to match the speed and reasoning of adversarial AI. AI