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:
Observation & Signal Filtering
observe
primitive to gather raw intelligencedistinguish
primitive to separate signal from noise
Definition & Classification
define
primitive to establish threat vector profilessequence
primitive to map causal and dependency chains
Inference & Reflection
infer
primitive to predict second-order effectsreflect
primitive to evaluate framework gaps
Synthesis & Output
synthesize
primitive to merge findings into coherent narrativedecide
primitive to prioritize recommendations
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
- Define AI threat typologies →
threat_001
- Sequence research phases →
phase_001
- Establish SCM trigger criteria →
scm_001
- Begin observation phase →
observe_001