An IT organization can invest months making sure new systems have the security controls they need to hit their compliance goals. And while they’re doing that, a business user discovers they can solve that same problem in minutes with generative AI.
This is shadow AI—and it’s becoming one of the fastest-growing challenges in cybersecurity governance.
Shadow AI occurs when employees deploy AI tools and large language models (LLMs) without security review, compliance checks, or data governance. Unlike traditional shadow IT, shadow AI can process vast amounts of sensitive data in seconds. A single prompt might expose intellectual property, customer information, or confidential strategy to unauthorized systems.
The rise of agentic AI—autonomous AI agents that can take actions independently—takes the risk to a new level, especially when people with no coding skills discover they can use AI to write and deploy those agents.
The Risks Are Significant
- Data leakage: Sensitive information fed into public LLMs may become training data
- Compliance gaps: GDPR, HIPAA, and other regulations apply to AI deployments
- Inconsistent outputs: Teams getting conflicting AI-generated recommendations
- Agentic unpredictability: Autonomous AI systems acting without human oversight
Building Effective AI Governance
Solving this with AI risk management isn’t about blocking innovation. It’s creating AI governance frameworks that enable productivity while managing cybersecurity risk.
Key components include:
- Vetted AI tools with proper data controls
- Clear AI security policies
- Training on responsible AI use
- Monitoring AI compliance without stifling teams
Organizations that proactively build AI governance frameworks position themselves to leverage AI’s benefits while managing its risks.
Key Takeaways
- Shadow AI enables employees to deploy powerful tools in minutes, bypassing traditional security review processes
- Agentic AI—autonomous agents that act independently—introduces new risks that require governance frameworks
- Data leakage, compliance gaps, and unpredictable outputs are key concerns with unmanaged AI deployments
- Effective AI governance enables productivity while maintaining security controls and regulatory compliance
- Proactive AI risk management frameworks help organizations capture innovation benefits while managing emerging threats