AI-Powered Project Control Dashboards for Proactive Cybersecurity Event Response and Strategic Decision Support in Critical Infrastructure Programs

The protection of critical infrastructure programs ranging from energy grids to transportation and healthcare systems demands a proactive and intelligent approach to cybersecurity management, especially as threat landscapes grow more dynamic and interconnected.Traditional project control dashboards primarily serve cost, scope, and schedule tracking purposes, lacking the adaptability required to address emerging cyber threats.This paper proposes an integrated framework for AI-powered project control dashboards designed to facilitate proactive cybersecurity event response and strategic decision-making within large-scale infrastructure programs.Leveraging advances in machine learning, real-time anomaly detection, and natural language processing, the proposed dashboard architecture continuously ingests telemetry, threat intelligence feeds, system logs, and project status updates.These inputs are dynamically analyzed to flag early indicators of compromise, predict system vulnerabilities, and recommend mitigation paths.Embedded AI agents prioritize threat events based on risk scoring models, linking them directly to affected project milestones or critical paths.Beyond operational security, the dashboard provides strategic decision support by aligning cybersecurity insights with project governance KPIs enabling executives to evaluate trade-offs between security investments and delivery objectives.Scenario simulation tools allow for cyber-risk-adjusted forecasting of project outcomes, supporting resilient planning and stakeholder transparency.Case scenarios drawn from infrastructure security programs illustrate how AI-driven dashboards reduce incident response times, enhance coordination among project stakeholders, and shift cybersecurity from a reactive process to a governanceintegrated capability.The paper concludes with a roadmap for deploying such platforms in regulated sectors, emphasizing data provenance, model interpretability, and cross-disciplinary collaboration.