1. Concept Overview

What are AI-Built Decision-Support Models?

These are AI systems used to assist or automate complex decision-making in areas such as:

• Government policy decisions (tax, welfare, migration)
• Corporate strategy (pricing, investment, supply chain)
• Healthcare triage and diagnosis support
• Defense and intelligence analysis
• Banking risk scoring and credit decisions

These systems do not just process data—they:

• Predict outcomes
• Recommend actions
• Rank options
• Optimize decisions under uncertainty

2. What Are Trade Secrets in These AI Systems?

In AI decision-support models, trade secrets include:

(A) Model Intelligence

• Neural network architectures for decision ranking
• Reinforcement learning policies for optimization
• Predictive scoring systems

(B) Training Data

• Proprietary datasets (financial, health, government)
• Behavioral datasets used to train decision logic
• Feedback loops from real-world decisions

(C) Decision Logic

• Weighting of variables in decision-making
• Risk scoring formulas
• Bias correction mechanisms

(D) System Architecture

• API-based decision pipelines
• Cloud-based inference systems
• Human-AI hybrid decision frameworks

3. Trade Secret Governance Framework

(1) Identification Layer

Organizations must classify:

• Decision-support algorithms
• Model training pipelines
• Sensitive datasets
• Output decision logic

(2) Technical Governance Layer

• Encryption of model weights
• Secure model APIs with authentication
• Confidential computing (secure enclaves)
• Data masking + anonymization
• Version-controlled model access

(3) Organizational Governance Layer

• Role-based access control (RBAC)
• NDAs for data scientists and analysts
• Segmented AI development environments
• Ethics + compliance review boards

(4) Legal Governance Layer

• Trade secret litigation strategy
• Employment restriction agreements
• Non-disclosure + non-use clauses
• Cross-border data protection enforcement

4. Case Laws (Detailed Explanation)

CASE 1: Waymo LLC v. Uber Technologies (2017)

Facts:

• Former Waymo engineer downloaded thousands of confidential files
• Files included AI navigation + sensor fusion systems
• Used in autonomous vehicle development

Legal Issue:

Whether AI decision-making systems and datasets are trade secrets.

Judgment:

• Uber settled the case
• Paid significant equity compensation
• Implemented strict compliance controls

Relevance:

AI decision-support systems (like routing, logistics, policy AI) are:

• Highly protected trade secrets
• Dependent on proprietary training data
• Vulnerable to insider misuse

➡️ AI decision models = legally protected core assets.

CASE 2: PepsiCo Inc. v. Redmond (1995)

Facts:

• Executive left PepsiCo for competitor
• Had deep knowledge of strategic forecasting models
• Company argued inevitable disclosure

Legal Issue:

Doctrine of inevitable disclosure

Judgment:

• Court restricted employment temporarily
• Found that knowledge would inevitably influence decisions

Relevance:

AI decision-support experts:

• Cannot “unlearn” model logic
• Carry pricing/risk intelligence between employers
• May be restricted from joining competitors

➡️ AI governance depends heavily on controlling human mobility.

CASE 3: IBM v. Papermaster (2008)

Facts:

• Senior IBM engineer joined Apple
• IBM claimed exposure to sensitive architecture

Legal Issue:

Whether employment in similar AI/tech roles risks trade secret exposure.

Judgment:

• Employment allowed with restrictions
• Limited involvement in overlapping systems

Relevance:

• AI decision systems rely on architecture knowledge
• Engineers working on governance models carry sensitive logic
• Courts may impose role-based restrictions

➡️ Decision-support system architects are high-risk positions.

CASE 4: E.I. du Pont de Nemours v. Kolon Industries (2011)

Facts:

• Former employees allegedly stole Kevlar production process data
• Used to create competing product

Legal Issue:

Protection of industrial processes as trade secrets

Judgment:

• Massive damages awarded
• Strong recognition of process-based trade secrets

Relevance:

AI decision-support systems are also:

• Structured processes of decision optimization
• Industrial-scale computational workflows
• Proprietary optimization pipelines

➡️ Decision-making logic = protected industrial process.

CASE 5: Motorola Solutions v. Hytera Communications (2017–2020)

Facts:

• Engineers allegedly stole source code and system architecture
• Used in competing communication systems

Legal Issue:

Software-based trade secret theft

Judgment:

• Large damages awarded
• Strong protection for software systems

Relevance:

AI decision-support systems consist of:

• Software inference engines
• Algorithmic decision pipelines
• Cloud-based model orchestration systems

➡️ Entire AI stack is protected like source code.

CASE 6: United States v. Aleynikov (2012)

Facts:

• Programmer copied proprietary trading system code
• Uploaded it before leaving company

Legal Issue:

Digital copying of AI/algorithmic systems

Judgment:

• Confirmed principle that digital extraction = misappropriation
• Case involved complex appellate rulings but core principle stands

Relevance:

AI decision-support systems:

• Are often cloud-hosted
• Can be copied via model weights or APIs
• Data extraction is equivalent to theft

➡️ Model weight theft = trade secret violation.

CASE 7: DuPont v. Christopher (1970)

Facts:

• Industrial facility studied using aerial surveillance
• Competitor inferred manufacturing process layout

Legal Issue:

Whether indirect observation is trade secret misappropriation.

Judgment:

Court ruled:

• Improper acquisition = violation
• Trade secrets protected even if partially visible

Relevance:

AI systems today:

• Can be reverse-engineered via output analysis
• API probing can reveal decision logic
• Output pattern leakage can expose model behavior

➡️ Even indirect inference of AI logic is legally risky.

5. Key Legal Principles Derived

(1) AI Decision Models Are Trade Secrets

From Waymo + Motorola

(2) Data + Logic = Protected Asset

From IBM + Aleynikov

(3) Employee Knowledge Is Legally Controllable

From PepsiCo v Redmond

(4) System Design and Process Are Protected

From DuPont v Kolon

(5) Digital Copying or API Extraction = Liability

From Aleynikov + Motorola

(6) Indirect Reverse Engineering Can Be Misappropriation

From DuPont v Christopher

6. Application to AI Decision-Support Governance

High-Value Trade Secrets:

(A) Policy Decision AI

• Government welfare eligibility models
• Tax fraud detection systems

(B) Financial Decision AI

• Credit scoring models
• Investment risk engines

(C) Healthcare Decision AI

• Diagnostic recommendation systems
• Treatment prioritization models

(D) Corporate Strategy AI

• Pricing optimization systems
• Supply chain decision engines

7. Governance Framework (Practical Implementation)

Technical Governance:

• Encrypted model weights
• Secure inference APIs
• Differential privacy in training data
• Audit logs for every AI decision

Organizational Governance:

• Role-based AI access (analyst vs developer vs regulator)
• Ethical review boards
• Segmented model environments

Legal Governance:

• Strong NDAs + non-use clauses
• Employment restrictions for core AI engineers
• Cross-border enforcement agreements

8. Conclusion

AI-built decision-support systems are among the most legally sensitive trade secret assets, because they control:

• Strategic decision-making
• Financial and policy outcomes
• Risk prediction systems
• Automated governance frameworks

The case law clearly establishes that courts strongly protect:

• AI models and algorithms
• Decision-making logic and pipelines
• Employee knowledge mobility
• Digital extraction and reverse engineering
• System architecture and data