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No Jitter Roll:
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Deloitte’s State of AI report shows that the top three risks organizations are most concerned about are data privacy and security (73%), legal and regulatory compliance (50%), and governance and oversight (46%).
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These concerns relate to how data can be misused or mismanaged, causing issues in visibility, compliance, governance and security for AI models and the data that goes into them.
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Organizations can mitigate these risks by ensuring proper controls, permissions and rules are put into place to protect data. This includes knowing where data came from, what organizations are allowed to use it for, and who/what has access to it.
No Jitter Insight:
It’s a new year, but organizations still have the same concerns about AI adoption that they did in 2025. According to Deloitte’s State of AI report, the top three AI risks that organizations are worried about are all affected by organizations’ best practices with data oversight and data management.
73% of respondents said data privacy and security is a top concern. How data is protected, accessed and stored – including how long it’s stored – plays a large part in ensuring data privacy. Organizations need to have access controls and permissions in place to make sure data is accessed by the correct people. Data storage needs to be encrypted to protect from data breaches. Organizations also need visibility over leaks and data breaches so they can prevent them from becoming widespread.
The second AI risk was legal, intellectual property and regulatory compliance (50%). This means that AI needs to follow rules regarding data usage according to legal and regulatory standards. Organizations need to know where data came from, why it was collected and what consents and rights they have when using this data. They also need to track each of these and show how data flows through systems to provide proof of compliance while maintaining visibility.
Governance and oversight is listed as the third AI risk at 46%. These are needed to show who approved models and owns datasets, how risks are monitored, what policies apply and what quality checks are in place. Having good governance and oversight allows organizations to maintain good data quality, labeling, retention and access standards.
This study is the latest to reiterate the risks of underinvesting in data management, especially as organizations rely more on AI in enterprise operations. An Avepoint report from October 2025 found that AI usage fuels data security incidents, with 75% of surveyed organizations reporting at least one data security incident where oversharing sensitive information negatively impacted them.
And in September 2025, No Jitter reported on the obstacles companies are currently struggling with when implementing sound data practices, with Omdia analyst Mila D’antonio saying, “The first step is to fix the urgent issues that block day-to-day operations, like broken data pipelines or critical quality gaps. But in parallel, companies need to design for long-term sustainability – things like governance frameworks and scalable architecture. I suggest balancing quick wins to build trust and momentum combined with a roadmap that addresses structural issues over time.”
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