Scaling L1 Alert Resolution with AI Agents

Client Overview

A Fortune 50 enterprise operating at global scale with thousands of international suppliers, vendors and distributors, faced increasing pressures on its third party risk management (TPRM) program. High false positive rates for sanctions screening, global reputational risk and adverse media monitoring overburdened the risk and compliance team.

The organization was processing more than 30,000 alerts annually, with several full-time analysts dedicated to L1 alert triage. Rising alert volume and high false positive rates were driving unnecessary review time and cost.

Challenge: Rising L1 Alert Rates

Within this large-scale compliance environment, three structural pressures emerged:

• Manual L1 Review Dominated Resources: A significant portion of analyst time was devoted to structured classification work rather than higher-risk investigative analysis.

• Operational Sensitivity to Volume Growth: Increased alert volumes would have required additional headcount to maintain service levels under the existing model.

• Strict Governance Expectations: Any efficiency gains needed to align with established risk appetite, preserve decision traceability and ensure human review remained central for consequential cases.

The objective was controlled efficiency within an enterprise compliance framework, not automation for automation’s sake.

Solution: AI Agents for Structured Resolution

The organization deployed Arbiter AI Agents to instantly review, investigate and triage sanctions, PEPs and adverse media alerts. Arbiter was integrated within the client’s existing TPRM system that onboards and monitors the organization’s global supply chain.

By embedding directly into the client’s existing risk and compliance workflow, Arbiter agents:

• Evaluate alerts using contextual logic across multiple decision factors

• Classify alerts at the L1 stage

• Escalate higher-risk or ambiguous cases to human reviewers

• Produce audit-ready, explainable decision narratives

Customization to Risk Appetite

Unlike static rules engines, Arbiter adapts to institutional policy inputs and applies structured reasoning, bridging manual review and automation. Arbiter was calibrated to reflect the organization’s existing escalation thresholds and decision logic.

Resolution thresholds were aligned with:

• Internal governance policies

• Escalation frameworks

• Oversight and audit expectations

This ensured that AI-assisted resolution reduced repetitive workload without compromising true positive handling.

Modular Deployment

Castellum.AI’s modular architecture allowed the enterprise to deploy Arbiter in a configuration aligned to its existing operations.

The organization implemented Arbiter as a plug-and-play AI resolution layer within its existing screening and case management systems, requiring minimal implementation effort and no infrastructure changes.