Noodle RAT Tech Unicorn Algorithm Theft - Planning Guide

Noodle RAT Tech Unicorn Algorithm Theft

Complete preparation guide for AI startup espionage scenario

Comprehensive facilitation guidance for Noodle RAT Tech Unicorn Algorithm Theft featuring memory-resident malware, breakthrough AI algorithm surveillance, pre-IPO intellectual property theft, and competitive intelligence threatening $5B valuation.

1. Quick Reference

Element	Details
Malmon	Noodle RAT (Psychic/Flying dual-type)
Difficulty Tier	Tier 3 (Expert)
Scenario Variant	APT: AI Unicorn Startup Pre-IPO
Organizational Context	DataFlow Technologies: 280 engineers, breakthrough AI, $5B pre-IPO valuation
Primary Stakes	Proprietary AI algorithms + $5B valuation + IPO success + Competitive advantage
Recommended Formats	Full Game / Advanced Challenge
Essential NPCs	Dr. Sarah Kim (CTO), Michael Foster (Security Engineer), Jennifer Martinez (Principal AI Scientist)
Optional NPCs	Robert Chen (IPO Coordinator), Investor Representative, Competitive AI Intelligence Analyst

Scenario Hook

DataFlow is preparing for IPO roadshow Monday when engineers notice development workstation performance indicators despite security scans finding no threats—fileless malware operates in memory, providing competitors invisible surveillance of breakthrough AI algorithms and $5B valuation intellectual property.

Victory Condition

Team identifies memory-resident AI surveillance through behavioral detection, protects proprietary machine learning models from continued theft, ensures investor confidence and IPO integrity, and addresses competitive espionage threatening unicorn valuation.

2-3. Configuration & Scenario Overview

Full Game (120-140 min): 3 rounds focusing on AI development anomaly detection, algorithm protection, IPO investor confidence

Opening: “It’s Friday morning at DataFlow Technologies. CTO Dr. Sarah Kim is finalizing AI algorithm demonstrations for Monday’s IPO roadshow—the $5B valuation depends on breakthrough machine learning technology. But Principal AI Scientist Jennifer Martinez reports development workstations showing subtle performance variations. Security scans find nothing. Yet she insists: ‘Our proprietary AI algorithms are being surveilled—someone is stealing our competitive advantage before IPO.’”

Initial Symptoms:

AI development workstations showing performance indicators during training runs despite “clean” security scans
Proprietary machine learning model files accessed at timestamps inconsistent with engineer workflows
Network monitoring detecting data transfers during off-hours engineers don’t recall initiating
Algorithm intellectual property files showing systematic access suggesting competitive intelligence
Model training performance degradation suggesting invisible surveillance resource consumption

Organizational Context:

DataFlow Technologies: AI unicorn startup, pre-IPO preparation, breakthrough machine learning technology
Key Assets: Proprietary AI algorithms, machine learning models, training datasets, pre-IPO strategic planning
Regulatory Environment: Investor disclosure requirements, IPO compliance, intellectual property protection
Cultural Factors: Startup velocity culture, IPO timeline pressure, competitive AI advantage protection

4. NPC Reference

Essential NPCs

Dr. Sarah Kim (CTO)

Position: Leading AI development and IPO technical preparation
Personality: Visionary technologist, protective of breakthrough AI, concerned about competitive theft before IPO
Agenda: Complete IPO roadshow with algorithm demonstrations while protecting intellectual property
Knowledge: AI algorithm details, competitive landscape, $5B valuation dependencies
Pressure Point: Unicorn valuation and IPO success depend on AI competitive advantage and investor confidence
IM Portrayal: Emphasizes innovation stakes and IPO timing pressure

Michael Foster (Security Engineer)

Position: Startup security, AI intellectual property protection
Personality: Technical pragmatist, resource-constrained, facing sophisticated threat
Agenda: Detect fileless surveillance while maintaining development velocity
Knowledge: Startup security challenges, limited resources, detection tool limitations
Pressure Point: Professional responsibility for protecting pre-IPO intellectual property
IM Portrayal: Under-resourced security facing nation-state-level threat

Jennifer Martinez (Principal AI Scientist)

Position: Developing breakthrough machine learning algorithms
Personality: Brilliant researcher, protective of her AI innovations, trusts technical instincts
Agenda: Protect proprietary algorithms and machine learning models from competitive theft
Knowledge: Algorithm architecture, file access patterns, training workflows
Pressure Point: Personal intellectual property and career tied to algorithm success
IM Portrayal: AI detective who noticed surveillance through model access patterns

5. Investigation Timeline

Round 1: Discovery

Automatic Reveals: Development workstation anomalies; AI algorithm file access discrepancies

Investigation Leads:

Detective: Memory dump reveals PowerShell code accessing machine learning models and training datasets
Protector: Proprietary AI algorithms and pre-IPO strategic planning compromised through memory surveillance
Tracker: Network traffic shows encrypted exfiltration of AI intellectual property to competitor infrastructure
Communicator: Engineers describe tech industry recruitment emails with sophisticated AI-targeted payloads
Crisis Manager: IPO roadshow Monday; $5B valuation at risk; investor disclosure obligations
Threat Hunter: Competitive AI espionage or nation-state technology acquisition targeting breakthrough algorithms

Rounds 2-3: [Investigation & Response - includes memory forensics, competitive/nation-state attribution, investor coordination, IPO decision]

6. Response Options

Most Effective:

Memory forensics with AI algorithm access analysis (DC 13)
PowerShell logging for fileless AI espionage detection (DC 14)
Behavioral monitoring for machine learning model protection (DC 15)

Moderately Effective:

Development environment rebuild preserving IPO timeline (DC 12, schedule impact)
Network isolation for AI intellectual property protection (DC 14)

Least Effective:

File-based detection on memory-only malware (DC 22)
Signature scanning for fileless operations (Automatic failure)

7-12. [Additional Sections - Structured Similarly]

Key Learning:

Fileless malware detection in AI development environments
Machine learning intellectual property protection under espionage
Pre-IPO cybersecurity incident disclosure and investor confidence
Competitive/nation-state AI technology acquisition threats
Memory forensics for startup algorithm protection

MITRE ATT&CK:

T1059.001 (PowerShell), T1055 (Process Injection), T1005 (Data from Local System - AI Algorithms)

Notes for IM Customization

What worked well:

What to modify next time:

Creative player solutions:

Timing adjustments: