From Battlefield to Boardroom: Strategic Red Teaming as an Epistemic Governance Instrument in the Age of AI

Abstract

Organizations increasingly make strategic decisions about AI systems whose behaviour, failure modes, and institutional effects cannot be fully known at design time. This technical report reframes strategic red teaming as a board-level governance discipline for testing the assumptions under which AI-enabled strategies are approved, funded, and supervised. The report proposes a six-component model for strategic red teaming in AI governance: an explicit assumption register, an adversarial mandate, independence criteria, evidence grading, a board-facing decision record, and a follow-up mechanism for unresolved findings. The model is intended to make strategic uncertainty inspectable before it becomes operational exposure. It treats red teaming not as penetration testing, scenario theatre, or generic risk review, but as structured adversarial testing of the claims on which governance decisions depend. The contribution is conceptual and design-oriented. It does not claim empirical validation, regulatory endorsement, or legal sufficiency. Instead, it provides a candidate governance artefact for organizations that need to connect AI strategy, accountability, oversight, and evidence. The report also defines limitations and a minimum validation protocol for future empirical testing in organizational settings.