Dive Brief:
- More than half of companies have only partial visibility into their data amid rising concerns over data oversight and transparency, according to a Thales report conducted by the Cloud Security Alliance published last week. The report surveyed 210 IT and security professionals.
- While three-quarters of organizations say they’re confident in their ability to secure unstructured data, 68% say a majority of their data remains unprotected.
- In this climate, tech executives perceive AI as a solution as well as a threat. Nearly half of respondents view AI as a top future security risk against unstructured data, yet 40% see it as a key tool for managing it.
Dive Insight:
Unstructured data is making up a growing proportion of enterprise data, with nearly one-third of companies saying it accounted for more than half of their annual data growth.
The report also found governance practices are struggling to keep pace with the rate of unstructured data growth.
“The explosive growth of unstructured data — estimated by Gartner to account for between 70% and 90% of enterprise data—has become a defining characteristic of modern organizations,” said Hillary Baron, AVP of Research at CSA and lead author of the report, in a press release. “While it enables significant operational value, it also introduces substantial security risk.”
Fragmented tooling is compounding the problem. Nearly one-third of organizations use more than 11 tools or more to manage unstructured data, creating operational silos that hinder holistic governance practices. Many organizations still rely on manual processes for classification and monitoring, an approach that struggles to keep pace with the volume of data growth.
In this landscape, AI is emerging as both a potential remedy and a new source of risk.
On one hand, enterprises are looking at the tech to automate processes such as data discovery, classification and threat detection. On the other, scaling AI tools without proper oversight runs the risk of greater vulnerabilities later down the line. Without clear visibility, AI models risk being trained on incomplete or biased datasets.
With only 9% of organizations able to scan data in real time – and nearly a quarter unable to scan at all – AI systems risk amplifying existing blind spots rather than resolving them.
“Without addressing these gaps, AI, automation, and emerging technologies will only increase exposure,” said Todd Moore, VP at Thales.
AI is only as good as the data it’s trained on, with nearly 3 in 5 business leaders saying key decisions are made based on inaccurate or inconsistent data, according to a SoftServe report published last year. Moving forward without that foundation can lead to wasted resources, with many organizations stuck in the pilot phase as ROI lags and data challenges derail AI projects.
As businesses look to deploy AI agents more widely, AI ambitions hinge on a reliable data backbone, prompting organizations to prioritize data skills in upskilling efforts.
Foundational readiness will be the defining factor in whether unstructured data security efforts succeed, according to the Thales study. Organizations that invest in these areas — visibility, classification, governance, and scalable operations — will be better positioned to manage an increasingly complex data landscape.
