What is Shadow AI and why is it a risk?

Shadow AI emerges when developers, researchers, or business teams independently adopt AI tools without involving security or compliance teams. This includes using:

• Public generative AI models like ChatGPT or open-source LLMs
• Unapproved MLOps platforms or notebooks
• Personal accounts on AI infrastructure
• Unvetted datasets or pretrained model weights
• Agent frameworks or AI APIs embedded into applications

Often well-intentioned, Shadow AI bypasses standard procurement, security reviews, and policy enforcement—leading to significant blind spots in enterprise environments. Risks include:

• Data leakage: Uploading confidential or regulated data to public models
• Licensing issues: Use of copyrighted or improperly licensed datasets and weights
• Security exposure: Integration with AI tools that lack authentication, logging, or controls
• Compliance gaps: Missing documentation, audit trails, or policy alignment
• Operational risk: Lack of monitoring or update tracking for unregistered AI assets

As AI adoption accelerates across business units, Shadow AI is becoming more common—and harder to detect—especially in fast-paced, cloud-first environments. Unlike traditional shadow IT, these risks often live inside code, model repositories, or MLOps pipelines, making them invisible to standard IT tools.

How PointGuard AI Helps:
PointGuard AI Discovery is purpose-built to uncover and manage Shadow AI across enterprise environments. It integrates with leading platforms like Databricks, AWS, Azure, and GCP to automatically detect:

• Unknown or unapproved models, datasets, agents, and pipelines
• Model usage tied to unauthorized users, accounts, or apps
• Orphaned endpoints or APIs exposing sensitive functions
• Disconnected tools running outside approved governance workflows

Security and GRC teams gain full visibility into AI assets—even those not officially onboarded—along with automated workflows for approval, remediation, or risk classification.

PointGuard also generates complete AI Bills of Materials (AI-BOMs) and maintains live inventories across the entire AI stack. Shadow assets can be flagged, scored for risk, and integrated into compliance dashboards—ensuring that no AI deployment goes ungoverned.

By eliminating Shadow AI, organizations improve security, reduce liability, and bring all AI initiatives into alignment with internal policy and regulatory frameworks.

Learn more at: https://www.pointguardai.com/ai-discovery