Data Management Glossary
Shadow AI
Shadow AI is the unknown, unauthorized or unmanaged use of AI tools, models, or services within an organization, outside the visibility and governance of enterprise IT, security, and compliance teams.
It’s an evolution of Shadow IT, but with significantly higher data risk, regulatory exposure, and ethical complexity, especially in the age of GenAI and LLMs. Read the white paper Unstructured Data Management In the Age of Generative AI.
Shadow AI is a Growing Problem
The 2025 Komprise IT Survey: AI, Data & Enterprise Risk 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. Data-heavy enterprises are especially vulnerable to the risks created of shadow AI due to:
- Vast amounts of unstructured data in shared drives, clouds, and personal folders
- Employees feeding sensitive data into tools like ChatGPT, GitHub Copilot, Claude, or private AI apps
- Business units or developers deploying AI models on local or cloud environments without centralized oversight
Proliferating shadow AI can lead to:
- Data leakage or IP exposure
- Regulatory non-compliance (e.g., GDPR, HIPAA, SEC)
- Model misuse or bias
- Security and privacy blindspots
- Inaccurate, false or suboptimal results due to lack of standards and guidelines for proper use of AI
Strategies to Prevent or Manage Shadow AI
Here are some strategies enterprise IT organizations are implementing to address shadow AI:
Data Management & Security Technology
- Most enterprise IT leaders (75%) are planning to use data management technologies to address risks from shadow AI, followed closely by AI discovery and monitoring tools (74%), according to the 2025 Komprise IT Survey: AI, Data & Enterprise Risk.
- Organizations are also focusing on classifying sensitive data and using workflow automation to prevent its improper use with AI (73%), according to the survey.
Policy + Education
- Develop an AI Acceptable Use Policy (AUP) and disseminate it broadly with training
- Run awareness campaigns about the risks of unauthorized AI usage
- Provide sanctioned alternatives to consumer AI tools (e.g., Azure OpenAI with enterprise governance)
Centralized AI Data Governance
- Establish a cross-functional AI governance committee (IT, Legal, Compliance, Security, Data Science)
- Define AI model approval, lifecycle, and audit requirements
- Use ML model registries and access control platforms (e.g., MLflow, ModelDB)
- Adopt data governance tools and standards for AI
Establish Security Controls
- Use AI discovery tools and/or CASBs (Cloud Access Security Brokers) to detect unauthorized API access or AI tool usage
- Implement data loss prevention (DLP) controls to detect uploads of sensitive data to AI tools
- Monitor file movement into AI-related apps or platforms using data governance and auditing tools
- Leverage unstructured data management solutions to create sensitive data management processes
Audit and Monitor Data Sources
- Know what data is being accessed and by whom—especially large, unstructured datasets
- Enforce least-privilege access and activity logging for data lakes, cloud buckets, NAS, etc.
How Komprise & Unstructured Data Management Can Help Address Shadow AI in the Enterprise
Unmanaged unstructured data is where Shadow AI risks often begin. Komprise can play a critical preventative and monitoring role:
Data Visibility: See Across Storage Silos
Komprise scans and catalogs all unstructured data (across NAS, cloud, object storage) so that IT knows what data exists, where, and how it’s being accessed. Shadow AI often feeds on “dark data.” Komprise helps shine light on it.
Unstructured Data Classification & Data Tagging
Komprise supports tagging files by sensitivity, business unit, or keywords. IT and end users with the right data access controls can label regulated or high-risk data (e.g., PII, R&D, contracts) and restrict its use in AI training or prompts.
Access Pattern Analysis
Komprise analyzes who is accessing which data, how often, and from where. Unusual access spikes or transfers of large files to unauthorized cloud platforms can be flagged as potential Shadow AI indicators.
Policy-Driven Data Management
Komprise Intelligent Data Management automatically tiers or archive data so that non-essential files are less accessible. With Komprise Smart Data Workflows you can enforce policies that restrict copying, moving, or exporting sensitive unstructured data.
Audit Trails and Forensics
The Komprise metadata catalog, or metadabase, plus historical logs offer evidence in case of AI-related data leaks. This visibility helps with post-incident investigations and compliance reporting
The Real Problem of Shadow AI
Shadow AI is a serious and growing risk, especially for enterprises with large volumes of unstructured data. The solution is not to block tools our outlaw AI, which can backfire and is largely ineffective. Instead, IT leaders can deliver broad visibility, governance and responsible use of AI.
Unstructured data management platforms like Komprise provide essential foundations for:
- Discovering hidden data risks
- Tagging and restricting AI-sensitive data
- Monitoring usage patterns that could signal Shadow AI activity
