"Data integrity is no longer only about keeping data safe; it's also about data trust. Organizations are asking themselves, 'Can we trust our data?' In a new era shaped by AI-driven decisions, that question is difficult to answer, and it increasingly has operational significance."
"Data distortion, therefore, becomes a very clear and present integrity problem. While cybersecurity is about deploying security solutions to protect key systems, it's also about understanding that data is the driving force of any system."
"Curiosity ensures that people don't inherently assume their data is valid and trustworthy. This matters because modern threats don't focus on breaking systems alone, but on manipulating the data inputs these systems consume and leverage."
"Data integrity should be defined as what is normal and what is not. In modern environments, 'normal' is dynamic and evolving."
Organizations are increasingly questioning the trustworthiness of their data, especially in the context of AI-driven decisions. A small change in training data can lead to significant inaccuracies in AI outputs. Data integrity is now seen as a critical issue, as decision-making relies heavily on data. Understanding data flow and its transformations is essential, as data is interconnected across various systems. Curiosity about data validity is crucial, as modern threats focus on manipulating data inputs rather than just breaking systems. Data integrity must adapt to evolving definitions of what is considered normal.
Read at SecurityWeek
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