Scaling Cyber Effects Personnel for Enhanced Force Projection Using Large and Small Language Models

Technology Areas of Interest: 1. Artificial Intelligence, 3. Cybersecurity   Note: No Prior, Current or Pending Support of Similar Proposals or Awards. Recent breakthroughs in Reinforcement Learning (RL) and distillation techniques demonstrate that highly-performant, fast, and locally deployed Small Language Models (SLMs) can be optimized for cyberspace-specific requirements and limited discretionary budgets of mission owners. Our solution delivers a real-time, tactical AI copilot to support two key cyber effects user groups: 1. Cyberspace Operators: Provides automated generation of capability and operational security guidance based on technical data from target environments, including capability deconfliction and equity risk assessment.2. Exploitation Analysts: Delivers real-time automated analysis of host characterization, context-specific linguistic translation, and assessments of intelligence value from hosts and networks. The technical approach encompasses four core objectives: 1. Holistic Constraint Integration: Uniquely combines and weights environmental technical data, capability knowledge, OPSEC constraints, and mission objectives to ensure recommendations are technically sound, compliant, and mission-aligned.2. Dynamic Contextual Reasoning: Continuously reasons about changes in target environments and adapts recommended actions accordingly, reconciling capability behaviors against known detections.3. Adaptive Capability Pairing: Encodes friendly capabilities to propose tailored, mission-appropriate, and OPSEC-safe actions tied directly to available tools and methods.4. Cyber Effects Model Benchmarking: Develops tailored metrics specifically adapted for cyber effects use cases, enabling frequent model upgrades as new models emerge. Our implementation leverages recent advances in:- RL and distillation techniques for creating domain-specific "narrow savant" models- Inference-time scaling for synthetic data generation- Fine-tuning, retrieval-augmented generation (RAG), and prompt programming for precise conditioning on multiple data streams- Realistic testbeds for iterative validation- Guided policy alignment through continuous SME engagement The architecture employs microservices including real-time input processing, knowledge graph maintenance, multi-stage ML pipeline for recommendations, and operational policy enforcement. The solution prioritizes open source models for transparency, security, and customization while anticipating deployment in secure, isolated infrastructure. Phase I will validate technology selection through prototyping and establish Cyber Effects Benchmarks.  Phase II will demonstrate the system on a USN/USMC-relevant cyber range, showing dynamic scenario adaptation and OPSEC-compliant recommendations. This capability will enable cyber effects personnel to conduct increasingly parallelized operations with shortened planning cycles, verified compliance, and improved adaptability against evolving challenges.