Governing the hidden risks of generative AI in the enterprise
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Generative AI is being rapidly adopted in enterprises, but governance structures are often lagging behind, leaving organizations vulnerable to operational, security, and reputational risks. As AI becomes embedded in critical workflows, governance, security, and human oversight must evolve to address these risks.
Generative AI is being used in businesses to improve productivity and unlock new services. However, this rapid adoption has led to a gap in governance structures, with fewer than a quarter of business leaders having an AI governance program in place. Generative AI systems introduce new security challenges, including prompt injection and the potential to automate phishing campaigns. To address these risks, organizations are adopting secure-by-design approaches that embed safeguards throughout the lifecycle of AI systems. Data governance plays a critical role in this process, with many organizations relying on high-level data classification frameworks that were not designed with AI systems in mind. Maintaining human oversight and systematic monitoring is essential to prevent small errors from cascading into larger failures.
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