Digital Trust Management and Real-Time Deepfake Detection for Corporate Governance

Deepfakes erode digital trust and expose firms to fraud, market manipulation, and reputational loss, challenging the oversight duties of boards and executives. This paper proposes a Digital Trust Management (DTM) framework that aligns real-time deepfake detection with corporate governance, risk, and compliance (GRC) objectives. Technically, DTM fuses image forensics (spatiotemporal feature extraction and anomaly scoring) with cryptographic provenance—e.g., PKI-backed signatures and content authenticity standards—to verify media at ingestion and across the enterprise content lifecycle. Managerially, the framework operationalizes controls through risk-based triage, human-in-the-loop review, and incident playbooks aligned with the three lines of defense. We define control objectives and metrics—Mean Time to Detect (MTTD), Mean Time to Respond (MTTR), false-positive/false-negative rates, severity-weighted exposure—and embed them into service-level agreements and board-level dashboards for continuous assurance. The architecture generates audit-ready evidence trails to support internal audits and external assurance, and is designed to interoperate with enterprise IAM, DLP, and crisis-communication workflows. Using tabletop exercises and red-team simulations on representative media corpora, we illustrate how DTM reduces exposure windows, improves decision support during crises, and strengthens disclosure, record-keeping, and AI governance compliance. The result is a practical pathway for integrating real-time deep-fake defense into corporate governance to preserve stakeholder trust.