Operation Foresight

Phase 0: Initialization & Scope Definition

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🛡️ Phase 0 Research Output

Project Scope Definition

Date: 2025-04-23

Research Context

This document represents the foundational scope definition for Operation Foresight, establishing boundaries, focus areas, and methodological approach for the entire research initiative.

Task ID: scope_001

Objective

Define the scope, boundaries, and methodology for a comprehensive one-time deep research report on emerging cybersecurity threats and AI governance risks, using Roo's logic primitive framework.

Context

Context ID: ai_cyber_report_2025

Operation Foresight is a structured deep research initiative designed to produce a comprehensive analysis of the intersection between emerging cybersecurity threats and AI governance risks. The project is built around the MCP logic primitives framework, enabling a systematic, traceable, and recursive approach to intelligence gathering, analysis, and synthesis.

Scope

Included

  • Emerging Cybersecurity Threats
    • Novel attack vectors targeting AI systems
    • LLM-specific vulnerabilities and exploits
    • Advanced persistent threats leveraging AI capabilities
    • Supply chain and infrastructure vulnerabilities
    • Quantum-resistant cryptography challenges
  • AI Governance Risks
    • Regulatory gaps and inconsistencies across jurisdictions
    • Public vs private governance control asymmetries
    • Governance failure case studies and patterns
    • Model deployment oversight mechanisms
    • AI safety standard implementation challenges
  • Temporal Scope
    • Current threats (2024-2025)
    • Near-term emerging threats (2025-2027)
    • Long-term potential threats (2028-2030)
  • Strategic Curiosity Mode (SCM) Exploration
    • Low-probability, high-impact scenarios
    • Potential black swan events
    • Unconventional threat vectors
    • Cross-domain governance failures

Excluded

  • Implementation of specific defensive measures
  • Technical code development for security tools
  • Detailed compliance guidelines for specific regulations
  • Commercially sensitive vulnerability disclosures
  • Country-specific policy recommendations

Methodology

The research will be conducted through a systematic phase-based approach using MCP logic primitives:

1

Observation & Signal Filtering

  • observe primitive to gather raw intelligence
  • distinguish primitive to separate signal from noise
2

Definition & Classification

  • define primitive to establish threat vector profiles
  • sequence primitive to map causal and dependency chains
3

Inference & Reflection

  • infer primitive to predict second-order effects
  • reflect primitive to evaluate framework gaps
4

Synthesis & Output

  • synthesize primitive to merge findings into coherent narrative
  • decide primitive to prioritize recommendations
5

Adaptation & Finalization

  • adapt primitive to revise based on new data or SCM feedback

Output Format

The final deliverable will be a comprehensive report consisting of:

  • Executive Summary
  • Methodology Documentation
  • Threat Matrix (cross-referencing threats vs. governance gaps)
  • Visual Maps (actors, timelines, dependency networks)
  • Strategic Curiosity Mode Insights
  • Governance Failure Modes Analysis
  • Future Research Directions

Dependencies

  • Access to research databases and sources
  • MCP logic primitives stack functionality
  • Strategic Curiosity Mode trigger criteria
  • Boomerang protocol for subtask tracking
  • Deep Research Agent capabilities

Constraints & Ethics Considerations

  • All research must adhere to Roo's ethics layer principles
  • Research will not include active penetration testing
  • No unauthorized access to systems will be performed
  • Potentially dangerous information will be properly contextualized
  • Uncertain claims will be clearly labeled as such

Next Actions

  1. Define AI threat typologies → threat_001
  2. Sequence research phases → phase_001
  3. Establish SCM trigger criteria → scm_001
  4. Begin observation phase → observe_001