Shadow AI Discovery & Governance Service
Map, detect, and govern unsanctioned LLM usage, API tokens, and sensitive data leakage across teams.
Program Overview
Safeguard public and institutional assets from unvetted technological exposure. The Shadow AI Discovery & Government Service Simulation trains personnel across public-sector agencies and government contractors to identify, govern, and remediate unauthorized artificial intelligence usage. Through controlled simulations mimicking official citizen service portals, document analysis workflows, and public-facing civic tooling, this program reveals invisible AI dependencies and reinforces secure data-handling mandates in real time.
Who It Covers
Public-sector employees, civil service personnel, defense/procurement contractors, agency leadership, and third-party IT service integrators.
Simulated Threat & Discovery Vectors:
Unsanctioned Consumer AI & LLM Tools: Employees pasting classified memos, citizen PII, or draft policies into unvetted public chatbots.
Spoofed Government Service Portals: Threat actors deploying rogue civic AI assistants, fraudulent benefits calculators, or compromised agency API integrations.
Browser Extension & Embedded AI Leaks: Unauthorized AI transcription plugins, automatic summarizers, and unapproved coding assistants operating on agency networks.
Data Pipeline Interceptions: Simulating the risks of public model retraining and prompt cache retention on unapproved platforms.
Network-wide Shadow AI audit baselines, interactive contextual simulation triggers, NIST/FedRAMP-aligned micro-learning, and compliance risk dashboards.
Key Deliverables
The problem statement
Driven by productivity pressures, over 70% of enterprise and public-sector knowledge workers regularly use unapproved, personal-tier AI tools. When applied to government operations—such as citizen record processing, judicial prep, or regulatory policy—this “Shadow AI” silently bypasses traditional endpoint security, data loss prevention (DLP), and sovereign data boundary controls. Because these consumer-tier platforms retain prompt logs, utilize input data for model retraining, and lack statutory audit trails, an unsanctioned copy-paste action can inadvertently trigger a major sovereign data compromise or regulatory violation before IT is even aware of the tool’s presence.
Data and risk exposure breakdown
When sensitive workflows leak through Shadow AI and compromised public service tools, an estimated 35% to 45% of all data processed through these unmanaged conduits is immediately exposed beyond recovery. In critical environments, this vulnerability directly places 70% to 80% of exposed citizen PII and national ID records at risk of regulatory breach and sovereign compliance violations, 50% to 60% of draft policy and regulatory intelligence vulnerable to external model extraction and prompt leaks, and 30% to 40% of public infrastructure source code and proprietary operational blueprints susceptible to third-party vendor compromise and permanent model memorization.
