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MNPI SurveillanceShield

AI-powered InfoBarriers monitors and prevents MNPI leaks across enterprise communications.

Product details

Overview

Shield’s InfoBarriers is an AI-driven capability within the Shield eComms compliance platform that continuously monitors communications to detect and prevent sharing of material non-public information (MNPI) and other sensitive data across email, chat, voice, video and file channels. It integrates with HR and list-management systems to define who should (and should not) see specific information, applies multilayered AI to surface likely breaches, automatically backfills historical data when lists change, and routes incidents into control-room workflows so compliance teams can rapidly investigate and remediate risk.

Features and Capabilities

  • Detection & Monitoring: Continuous, multilayered AI scans every channel for MNPI and sensitive information, reducing false positives while surfacing high-risk interactions.
  • List & Identity Integration: Syncs with HR and list-management systems to enforce access boundaries and automatically re-check historical communications when lists update.
  • Backfill & Retrospective Analysis: When personnel or lists change, InfoBarriers retrospectively analyses past messages to identify previously missed breaches.
  • Control-Room Workflows: Matches detections to control-room procedures so compliance teams can triage, escalate and remediate incidents with audit-ready evidence and case traceability.
  • Regulatory Alignment: Built to support surveillance and compliance use cases relevant to financial regulators (examples include SEC and FERC-related scenarios).
  • Scalable Coverage: Designed to be added to Shield’s platform stack with minimal IT overhaul — scales across channels and data volumes common in large financial firms.
  • Visualization & Search Analytics: Integrates with enhanced search analytics and visualization tools inside the platform to help surface trends, prioritize channels and accelerate investigations.
  • Automated Detection & Noise Reduction: Uses layered models (lexicons, behavior context, ML) to lower noise and ensure first-line supervisors focus on meaningful alerts.
  • Auditability & Data Integrity: Produces audit-ready records and case workspaces for eDiscovery and regulator responses.