AI Agentic Workflow for SOC Alert Enrichment and Triage

Raw SIEM alerts are enriched with context from CMDBs, threat intel feeds, and past incident databases before an analyst sees them. This eliminates 15-30 minutes of manual lookup per alert, allowing Tier 1 analysts to focus on validated threats. The workflow pulls entity risk scores, vulnerability status, and related tickets automatically, delivering an actionable summary in seconds.

Agents apply multi-step reasoning to correlate events, check against behavioral baselines, and score severity using business context (e.g., asset criticality). Low-fidelity alerts are automatically suppressed or downgraded, while high-severity incidents are prioritized and routed with recommended actions. This directly increases analyst throughput and job satisfaction by cutting noise.

The autonomous triage layer provides consistent, high-quality initial investigation during nights, weekends, and peak periods. This allows a fixed SOC team to manage a growing alert volume and complex infrastructure (5G core, cloud, IoT) without proportional staffing increases, creating significant labor leverage and improving shift economics.

Every automated triage decision is logged with the supporting evidence—threat intel matches, asset context, confidence scores—creating a defensible audit trail. This standardized, explainable output improves handoff quality to Tier 2/3, supports compliance reporting (NIST, ISO 27001), and reduces mean time to remediate (MTTR) by providing clear next steps.

The build cost is offset by reduced analyst burnout and turnover, lower reliance on expensive managed services, and the financial avoidance of prolonged breaches. By automating the repetitive, high-volume tier-1 workload, the SOC redirects skilled human capital to threat hunting and proactive defense, directly improving security posture and business risk.

The workflow is built on orchestration frameworks (LangGraph, Temporal) integrating with Splunk/QRadar/Sentinel, ServiceNow, and telecom-specific systems like HSS/HLR and BSS. It uses specialized agents for enrichment, correlation, and routing, with human-in-the-loop gates for critical decisions. This modular design allows it to adapt to new data sources and threat vectors without a full rebuild.

This primary agent pulls in real-time context for each raw SIEM alert. It queries the CMDB for asset criticality and ownership, checks internal threat intelligence platforms for known IOCs, and searches past incident databases for similar patterns. By appending business risk scores and historical data, it transforms low-fidelity alerts into high-intelligence incidents.

Using a framework like LangGraph, this component orchestrates logic to correlate related alerts across different sources (e.g., EDR, firewall, IDS). It applies scoring models that weigh factors like asset value, threat confidence, and attack progression to assign a dynamic severity and priority, routing only the most critical incidents for human review.

The workflow's backbone, built with platforms like Palo Alto XSOAR or custom Python orchestration, manages handoffs. It fetches data from ServiceNow for ticket status, pushes enriched alerts back to the SIEM (e.g., Splunk, Sentinel), and can trigger initial containment actions via firewall or EDR APIs based on pre-approved playbooks.

A centralized console presents the enriched alert with all aggregated context, a confidence score, and recommended next steps. Analysts can approve automated actions, request deeper investigation from a hunting agent, or escalate. All decisions are logged to ServiceNow with a full audit trail for compliance and process refinement.

Critical for long-term efficacy. Analyst overrides and incident post-mortems are fed back as ground truth. This data continuously retrains the scoring and correlation models, reducing false positives and adapting to new attacker TTPs. This closed-loop system ensures the workflow grows more accurate, protecting the ROI of the initial build.

Provides real-time metrics on workflow performance: alerts processed, auto-closure rates, MTTT reduction, and agent confidence scores. It also enforces governance by logging all agent decisions, data accesses, and model inferences, creating a defensible audit trail for internal security reviews and regulatory compliance (e.g., NIST, ISO 27001).