Advancing Operational Security Through System-of-Systems Engineering
Over the past several months, I have been conducting a research and development effort examining Operational Security (OPSEC), Systems Engineering, Artificial Intelligence, and System-of-Systems (SoS) resilience from an enterprise perspective.
The objective has been to explore how traditional OPSEC methodologies can be strengthened by combining them with modern systems engineering, digital engineering, risk management, and AI governance practices.
The work followed a structured analytical progression covering:
- Critical Information Identification
- Threat Analysis
- Vulnerability Analysis
- Enterprise Risk Assessment
- Countermeasure Development
- OPSEC Program Integration
- Verification Planning
- System-Shock Exercise Design
- After Action Review (AAR)
Rather than treating cybersecurity, infrastructure, logistics, energy, AI, communications, and governance as independent domains, the assessment examined how they interact within increasingly interconnected operational environments.
One of the central observations from the research is that operational risk often emerges from interdependencies rather than isolated failures. Individual systems may perform as designed while the combined system experiences degraded performance due to cascading effects across multiple domains.
Another area of investigation focused on the rapid growth of AI-enabled workloads. As AI systems become increasingly integrated into enterprise operations, infrastructure planning must consider not only user-generated requests but also the expanding volume of supporting activities such as retrieval, orchestration, validation, monitoring, and autonomous workflows. These factors have implications for compute infrastructure, networking, power, cooling, observability, and governance.
To support future analysis, the project introduced the engineering concept of Creeping Current Mismatch—a hypothesis describing conditions in which operational demand grows faster than one or more supporting systems, such as computing capacity, electrical infrastructure, monitoring capability, or organizational decision processes. This concept is intended as a framework for resilience analysis and should be evaluated through measurement, modeling, and controlled experimentation.
The work also emphasizes the importance of maintaining a clear distinction between:
- observed operational data;
- engineering estimates;
- analytical models;
- planning assumptions; and
- validated conclusions.
Maintaining that distinction is essential for evidence-based decision support and responsible engineering.
The overall effort has reinforced the value of integrating OPSEC with Systems Engineering, Risk Management, AI Governance, Digital Engineering, and Mission Engineering as complementary disciplines. This integrated approach supports more comprehensive resilience planning and helps identify potential vulnerabilities before they become operational issues.
Future work will focus on quantitative validation, Digital Twin development, executable System-of-Systems models, AI workload characterization, and controlled resilience exercises to further improve engineering confidence and operational decision support.
I welcome constructive discussion with colleagues working in systems engineering, operational security, AI governance, digital engineering, mission engineering, enterprise architecture, cybersecurity, and critical infrastructure resilience. Cross-disciplinary collaboration will be increasingly important as complex systems continue to evolve.
Keywords: Operational Security (OPSEC), Systems Engineering, System-of-Systems Engineering, Digital Engineering, Artificial Intelligence, AI Governance, Enterprise Architecture, Risk Management, Mission Engineering, Critical Infrastructure, Digital Twin, Resilience Engineering, Research & Development.
