Tracks/Track 2 — Product Manager / CPO/2-17
Track 2 — Product Manager / CPO

2-17: Shadow AI & Data Extrusion

Preventing uncontrolled employee access to unauthorized LLMs and stopping IP leakage.

3 Lessons~45 min

🎯 What You'll Learn

  • Define Shadow AI
  • Establish Corporate GenAI Walls
  • Audit network endpoints
  • Create defensible AI policies
Free Preview — Lesson 1
1

Lesson 1: The Shadow AI Threat

Shadow AI is the unauthorized use of generative AI tools by employees to perform company work. It is Shadow IT on steroids. When engineers paste proprietary code into public models, or analysts upload financial spreadsheets to unsecured chat interfaces, you suffer immediate, irreversible Intellectual Property extrusion.

IP Extrusion

The permanent loss of proprietary data to public model training sets.

Action: Immediate blocklist of unauthorized endpoints
Copyright Contamination

Pasting AI-generated code from public tools into your repos.

Creates massive legal liabilities in M&A
Compliance Breach

Violating GDPR or HIPAA through unsecured AI prompts.

Fines can reach millions
📝 Exercise

Perform an audit of employee AI tool usage. How many unauthorized generative AI interfaces are being accessed daily?

2

Lesson 2: The Corporate GenAI Wall

You cannot stop employees from wanting to use AI to work faster. You must provide a secure, governed alternative. Establishing a "Corporate GenAI Wall" means deploying internal, enterprise-secured LLM interfaces that guarantee zero-retention policies.

Zero-Retention Contracts

Enterprise agreements that explicitly forbid model training on your data.

Mandatory for all AI vendors
Internal AI Portals

Providing secure chat interfaces that route to governed APIs.

Neutralizes the urge to use public tools
Endpoint DLP Integration

Updating Data Loss Prevention (DLP) tools to flag LLM traffic.

Action: Block pasting of sensitive data types
📝 Exercise

Design the architecture for an internal AI portal. What models will it use, and how will it ensure zero retention?

3

Lesson 3: Governing the Adoption Curve

Transitioning from Shadow AI to Governed AI requires cultural engineering. You must prove to employees that the secure internal tools are just as capable as the public tools they were using illicitly.

Adoption Metrics

Tracking the usage of internal governed AI tools vs. blocked attempts to external tools.

Target: > 90% internal adoption
Amnesty Programs

Allowing employees to report their Shadow AI usage without penalty.

Crucial for discovering hidden workflows
Continuous Monitoring

Regularly scanning the network for new, unauthorized AI endpoints.

The AI landscape changes weekly
📝 Exercise

Draft a company-wide communication announcing an Amnesty Program for Shadow AI usage and introducing the new Governed AI portal.

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Inference Architecture
01import { orchestrator } from '@exogram/core';
02
03const router = new AgentRouter({);
04strategy: 'COST_EFFICIENT_SLM',
05fallback: 'FRONTIER_MODEL'
06});
07
08await router.guardrail(payload);
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Module Syllabus

Lesson 1: Lesson 1: The Shadow AI Threat

Shadow AI is the unauthorized use of generative AI tools by employees to perform company work. It is Shadow IT on steroids. When engineers paste proprietary code into public models, or analysts upload financial spreadsheets to unsecured chat interfaces, you suffer immediate, irreversible Intellectual Property extrusion.

15 MIN

Lesson 2: Lesson 2: The Corporate GenAI Wall

You cannot stop employees from wanting to use AI to work faster. You must provide a secure, governed alternative. Establishing a "Corporate GenAI Wall" means deploying internal, enterprise-secured LLM interfaces that guarantee zero-retention policies.

20 MIN

Lesson 3: Lesson 3: Governing the Adoption Curve

Transitioning from Shadow AI to Governed AI requires cultural engineering. You must prove to employees that the secure internal tools are just as capable as the public tools they were using illicitly.

25 MIN
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