M. Kumar
Guidehouse,
United States
Keywords: adversarial AI, AI governance, MLOps, risk management, defense, energy
Summary:
As artificial intelligence (AI) systems have become foundational to national security and defense, healthcare, and critical infrastructure, adversarial threats such as data poisoning, backdoors, supply chain compromise, and prompt injection have intensified in scale and impact, exposing the limitations of existing detection and defense frameworks. They represent urgent, real-world risks to operational trust, safety, and resilience. Developed by the AI Studio Research Team at Guidehouse, this work introduces a strategic, actionable framework for adversarial AI preparedness that integrates resilience engineering with governance and risk-management principles. The framework was developed through comprehensive review and analysis of research evidence, and insights from operational engagement with our clients and is designed for rapid adaptation to the energy sector and other critical domains where AI is now mission critical. The framework moves beyond traditional perimeter-based security models by embedding resilience, integrity, and continuous assurance throughout the machine learning operations (MLOps) lifecycle. It is structured around six complementary pillars: integrity, assurance, enforcement, containment, observation, and adaptation. This holistic approach enables organizations to proactively detect, mitigate, and recover from adversarial incidents—supporting mission assurance, regulatory compliance, and operational continuity. In our framework, integrity controls enforce cryptographic provenance and supply-chain transparency, while assurance mechanisms encompass continuous behavioral testing, red-teaming, and adversarial validation. Enforcement and containment operationalize policy-as-code and sandboxing principles, and observation and adaptation focus on real-time monitoring, telemetry analysis, and rapid response to emerging threats. Together, these pillars define a repeatable model for adversarial resilience that aligns with the National Institute of Standards and Technology AI Risk Management Framework (NIST AI RMF). What distinguishes this framework and aligns it with TechConnect’s mission of translational innovation is its emphasis on practical, cross-sector implementation. We provide a 30/60/90-day roadmap that organizations can use to establish foundational controls, scale monitoring and assurance functions, and achieve audit readiness. This phased implementation strategy is supported by sector-specific case studies, including defense applications such as adversarial attacks on autonomous vehicles and command-and-control systems, and energy-sector analogs addressing grid security and supply-chain integrity. Policy templates and operational playbooks are available to accelerate adoption and ensure measurable progress across all stakeholders. The framework is fully aligned with leading federal and industry standards, including the NIST AI RMF, Software Bill of Materials (SBOM), and Supply-chain Levels for Software Artifacts (SLSA). To extend SBOM practices into the AI domain, it incorporates an AI Bill of Materials (AIBOM) to document model lineage, datasets, and dependencies essential for provenance and assurance. By unifying resilience metrics, automated policy enforcement, and adaptive monitoring protocols, the framework closes the gap between AI capability and assurance. It enables organizations to systematically evaluate, govern, and sustain trustworthy AI—building a foundation for safe, responsible, and accelerated AI transformation across defense, energy, and other critical sectors.