Threat Management

Security operations centers (SOCs) face difficulties such as separating real threats from noise, alert fatigue, workflow bottlenecks, and insufficient context for alert investigation

Threat management involves preventing cyberattacks, identifying threats, and addressing security incidents to protect organizations

Fragmented information and blind spots hinder security teams' ability to detect and respond to threats, especially with evolving risks like insider threats, APTs, and cloud vulnerabilities

The vision for SOCs is to operate autonomously, with human analysts acting as "SOC pilots" who intervene only in uncertain or critical situations

SOC analysts handle "0-day" vulnerabilities and other uncertainties by applying human judgment to neutralize emerging threats

While automation is widely used in SOCs, it requires context and confidence to function effectively, especially in open systems like SIEMs

AI and machine learning (ML) are already used for threat detection and classification, but limitations exist in automating complex security processes

IBM's multi-agentic AI framework enables autonomous SOC operations by recognizing context, collecting data, and making decisions without human intervention

The Autonomous Threat Operations Machine (ATOM) orchestrates tasks, engages with other AI agents to gather missing context, and creates tailored responses to alerts

ATOM mimics human reasoning by analyzing asset patterns (e.g., identifying workstations) and determining the best response path, such as SOAR or API calls

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