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This report reframes strategic red teaming as a vital governance tool for organizations making high-stakes decisions about AI systems, emphasizing the need to test underlying assumptions before operational exposure. It introduces a six-component model that includes an explicit assumption register and independence criteria, aimed at enhancing accountability and oversight in AI strategy. By treating red teaming as structured adversarial testing rather than traditional risk reviews, the report provides a conceptual framework for organizations to better manage strategic uncertainty in AI governance.
Strategic red teaming can transform how organizations govern AI by systematically exposing and testing the assumptions behind their decisions.
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.
