Shadow AI is a Major Concern Across Enterprise IT

Komprise, Inc. announces the results of new industry research: Komprise IT Survey: AI, Data & Enterprise Risk.

The survey showed IT organizations are concerned about shadow AI, with nearly half stating that they are ‘extremely worried’ about the security and compliance impact of unauthorized and unsanctioned use of AI tools.

Beyond concern, enterprises are seeing real-world impact from shadow AI. Nearly 80% of IT leaders say their organization has experienced negative outcomes from employee use of GenAI, including false or inaccurate results from queries (46%) and leaking of sensitive data into AI (44%). Notably, 13% say that these poor outcomes have also resulted in financial, customer or reputational damage.

To help combat the downside of shadow AI, most (75%) plan to use data management technologies to address risks from shadow AI, followed closely by AI discovery and monitoring tools (74%).

Komprise surveyed 200 IT directors and executives at US enterprise organizations of 1,000 employees and larger. The purpose of the survey was to discover how IT teams are preparing their unstructured data for AI and the challenges they face. A 3rd party conducted the survey in April.

Key Statistics

Shadow AI Risks

The vast majority (90%) are concerned about shadow AI from a privacy and security standpoint, with 46% reporting that they are ‘extremely’
Most (79%) of IT leaders report that their organization has experienced negative outcomes from sending corporate data to AI, including PII data leakage and inaccurate or false results.
Most (75%) are planning to use data management technologies to address risks from shadow AI, followed closely by AI discovery and monitoring tools (74%).

Preparing Unstructured Data for AI

The greatest challenge in preparing unstructured data for AI is finding and moving the right data to locations for AI ingestion (54%) followed by a lack of visibility into data across storage to identify risks (40%).
The top tactic for preparing data for AI is classifying sensitive data and using workflow automation to prevent its improper use with AI (73%).
Nearly all (96.5%) are classifying and tagging unstructured data for AI, with a mix of manual and automated methods for doing so.
More than half (56%) say that IT is moving data to AI processes for users manually, or with free tools, with 40% saying that users are manually copying data to AI on their own.

IT Infrastructure Priorities

Supporting AI initiatives is the top priority for IT infrastructure (68%), followed by 16% saying it is equally important as cost optimization, cybersecurity and core IT upgrades.
Most IT leaders (45%) express a multi-faced strategy for investing in storage for AI, with equal priority to acquiring AI-ready storage, increasing capacity of existing storage and acquiring data management capabilities for AI.

“As enterprises are starting to get real about AI as part of their business strategy, the cracks are starting to show,” says Krishna Subramanian, COO and co-founder, Komprise. “With most reporting that they have experienced negative and even damaging consequences from using corporate data with AI, it’s time to create the right AI data governance strategy.  Unstructured data management will play a central role by giving users automated tools for data classification, sensitive data management, data workflows and AI data ingestion.“

Read the report: Komprise IT Survey: AI, Data & Enterprise Risk (registration required)