William Marks in HBR: AI Safeguards May Train Competition

This piece by William Marks, published in HBR on Sep 08, 2026, underscores a growing concern: that the very AI systems companies deploy to gain an edge might inadvertently reveal strategies or proprietary information. The article suggests that conventional protective measures, while standard, might not be robust enough against emerging forms of data leakage or model inversion.

The context here is the increasing reliance on AI for competitive advantage. As firms feed their AI with internal data, there is a lurking risk that this data could be extracted or inferred by adversaries. The article's warning is a call for business leaders to scrutinize their AI governance frameworks, ensuring they go beyond baseline compliance.

The stakes are high: if safeguards fail, a company's core strengths could become known to rivals, eroding its market position. Whether current protections hold may depend on how quickly organizations adapt to these overlooked vulnerabilities, potentially requiring a rethink of AI deployment strategies. The outcome hinges on proactive vigilance rather than reactive fixes.