AI Agentic Workflow for Detecting and Taking Down Fake Tower Sites (IMSI Catchers)

Rogue cell sites, or IMSI catchers, are a critical operational threat, enabling subscriber tracking, interception, and fraud. Manual detection is slow and unscalable across vast RAN footprints. A custom agentic workflow automates this by continuously analyzing RAN telemetry, UE reports, and signaling data for anomalies like unexpected cell IDs or signal parameter mismatches. This reduces the dwell time of active interceptors from days to minutes, directly protecting subscriber data and mitigating regulatory and reputational risk. The architecture must integrate with RAN Intelligent Controllers (RIC), SON platforms, and security information systems.

Implementation requires a multi-agent system where specialized agents handle detection, geolocation fusion (triangulating RF data with external feeds), and regulatory coordination. The orchestrator, built on frameworks like LangGraph, manages state and handoffs, ensuring auditability. Critical controls include confidence scoring for automated alerts, mandatory human-in-the-loop approval for law enforcement engagement, and integration with SOAR platforms for playbook execution. This workflow must be deployed with phased rollouts, starting in high-risk urban areas, and requires continuous model retraining on new IMSI catcher signatures to maintain efficacy.

The workflow automates the detection and neutralization of IMSI catchers (Stingrays), which are unauthorized cell sites used for subscriber location tracking and interception. It eliminates the manual bottleneck of correlating RAN telemetry, subscriber device reports (MDT), and external threat feeds, directly reducing the dwell time of these surveillance devices from days to minutes. Operational upside comes from protecting subscriber privacy, preventing fraud, and maintaining regulatory compliance by automating a response that was previously slow and labor-intensive for SOC and network security teams.

Implementation integrates with RAN Intelligent Controllers (RIC), packet core systems, and SOAR platforms like Palo Alto XSOAR. The orchestrator, built on LangGraph, sequences specialized agents for detection, geolocation, and coordination. Critical controls include confidence scoring gates, mandatory human review for low-confidence cases, and immutable audit trails logged to the SOAR system for compliance. The final coordination agent interfaces with regulatory bodies via secure APIs and updates network policies to block the rogue site, creating a verifiable, closed-loop containment action.

Phase 1 establishes the core detection pipeline, ingesting RAN telemetry and UE reports into a data lake. We deploy initial ML models for anomaly scoring and integrate with the Security Operations Center (SOC) SIEM for alerting. This MVP delivers immediate detection value while building the data foundation. The focus is on high-confidence, low-volume alerts to validate the model's precision and begin reducing the manual monitoring burden on network security teams.

Subsequent phases introduce the agentic orchestration layer using LangGraph or a custom state machine. Specialized agents handle geolocation refinement, evidence compilation, and case routing to internal security or external authorities via secure APIs. Each phase includes defined approval gates, rollback procedures, and performance monitoring against key metrics like mean time to detect (MTTD) and containment rate. Final governance phases lock in audit trails and continuous model retraining.

This workflow automates the detection and neutralization of fake cell towers (IMSI catchers) that intercept subscriber traffic. It eliminates the manual, reactive analysis of RAN telemetry and device reports, turning a high-latency investigation into a continuous, automated defense. The operational upside is measured in reduced dwell time for surveillance attacks, lower risk of data exfiltration, and preserved customer trust, directly impacting security SLAs and regulatory compliance posture for national carriers.

Implementation integrates detection agents with RAN Intelligent Controllers (RIC), SOAR platforms like Palo Alto XSOAR, and national regulator systems via secure APIs. The architecture requires robust controls: confidence scoring to manage false positives, mandatory human-in-the-loop approval for takedown actions, and immutable audit trails for legal defensibility. Deployment is phased, starting with non-critical network segments, using a digital twin for simulation, and incorporating feedback loops to retrain detection models from confirmed incidents.

This workflow's governance layer ensures automated actions remain within legal and operational policy. A central Policy Orchestrator validates every high-confidence detection from the AI agents against a rules engine, checking jurisdiction, law enforcement engagement status, and asset criticality. Exceptions or borderline cases are automatically routed to a human-in-the-loop console in the Security Operations Center (SOC), preserving mandatory review for high-stakes physical interventions. This control structure mitigates the risk of erroneous takedown actions that could disrupt legitimate services or violate regulatory mandates.

Phased rollout is critical for managing risk and building operational trust. Phase 1 deploys detection and alerting only, feeding high-fidelity leads to the existing manual SOC process for validation and action. Phase 2 introduces automated geolocation and evidence packaging, with the Policy Orchestrator enforcing mandatory SOC approval for all takedown requests. Phase 3, enabled after proven accuracy and established protocols with law enforcement, activates conditional autonomous takedown for pre-authorized scenarios, such as repeat offenders in designated high-security zones. Each phase is gated by performance metrics on false-positive rates and mean time to acknowledge.