AI governance under strain: what modern platforms mean for data privacy (www.techradar.com)

Organizations are re-evaluating how artificial intelligence (AI) integrates into regulated environments, particularly focusing on operational risks rather than solely policy frameworks. This shift arises because AI does not operate in isolation; it interacts with existing systems that govern data access and changes. Poor governance in these foundational systems can lead to unexpected data exposures, revealing risks that may not have been apparent initially. The rapid evolution of platforms, especially cloud-based services and low-code environments, complicates this further, as data paths can shift quickly without proper visibility, leading to potential breaches of sensitive information. To achieve "AI readiness," organizations must prioritize risk awareness and operational discipline. This involves a gradual approach to AI deployment, starting with low-risk applications to build trust and refine processes. Essential practices like robust data hygiene, access controls, and ongoing scrutiny of AI vendors are critical to mitigating risk. By embedding secure behaviors within the culture of AI development, organizations can create an environment where AI can be deployed responsibly, thus reaping its benefits while safeguarding data integrity. Ultimately, trust in AI is cultivated through reliable operational practices, not just policies.