Engineering Risk-Aware, Security-by-Design Frameworks for Assurance of Large-Scale Autonomous AI Models
This addresses security and assurance challenges for stakeholders in national security, open-source governance, and industrial automation, but it is incremental as it builds on existing threat metrics and hardening techniques.
The paper tackles the problem of ensuring safe and reliable operation of large-scale autonomous AI models by proposing an enterprise-level, risk-aware, security-by-design framework, resulting in measurable reductions in vulnerability and compliance overhead as demonstrated in case studies.
As AI models scale to billions of parameters and operate with increasing autonomy, ensuring their safe, reliable operation demands engineering-grade security and assurance frameworks. This paper presents an enterprise-level, risk-aware, security-by-design approach for large-scale autonomous AI systems, integrating standardized threat metrics, adversarial hardening techniques, and real-time anomaly detection into every phase of the development lifecycle. We detail a unified pipeline - from design-time risk assessments and secure training protocols to continuous monitoring and automated audit logging - that delivers provable guarantees of model behavior under adversarial and operational stress. Case studies in national security, open-source model governance, and industrial automation demonstrate measurable reductions in vulnerability and compliance overhead. Finally, we advocate cross-sector collaboration - uniting engineering teams, standards bodies, and regulatory agencies - to institutionalize these technical safeguards within a resilient, end-to-end assurance ecosystem for the next generation of AI.