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Home / Technology / Shadow AI: Enterprise's Costly Blind Spot

Shadow AI: Enterprise's Costly Blind Spot

2 Jan

•

Summary

  • Most firms lack advanced AI security strategies.
  • Rogue AI lawsuits could target executives in 2026.
  • Visibility gap hinders AI security and incident response.
Shadow AI: Enterprise's Costly Blind Spot

Four in 10 enterprise applications will feature AI agents this year, yet only 6% of organizations possess robust AI security strategies, according to Stanford research. Palo Alto Networks forecasts that 2026 will usher in the first major lawsuits holding executives personally accountable for AI actions. A pervasive "visibility gap" regarding LLM usage and modification hinders effective AI security, transforming incident response into guesswork.

A survey revealed 62% of security practitioners cannot identify where LLMs are deployed within their organizations. This lack of transparency exacerbates risks like prompt injection (76%), vulnerable LLM code (66%), and jailbreaking (65%). Traditional security tools struggle with adaptive AI models, leading to "shadow AI" incidents costing an average of $670,000 more than standard breaches.

While standards like AI-BOMs are emerging, adoption lags significantly. NIST's AI Risk Management Framework calls for AI-BOMs, but current tooling faces challenges due to AI models' dynamic nature. Experts emphasize that operational urgency, not a lack of tools, is needed to address the expanding AI attack surface and secure AI supply chains before breaches occur.

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Disclaimer: This story has been auto-aggregated and auto-summarised by a computer program. This story has not been edited or created by the Feedzop team.
The biggest AI security risk is the "visibility gap" in tracking LLM usage, hindering effective security and incident response.
Palo Alto Networks predicts 2026 will see the first major lawsuits holding executives personally liable for rogue AI actions.
AI-BOMs are crucial for tracking runtime model dependencies, unlike traditional SBOMs which focus on fixed software dependencies resolved at build time.

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