Enterprise security leaders need practical frameworks for AI data loss, not perfect solutions
Fasoo AI presented a data loss prevention strategy at the Gartner Security & Risk Management Summit 2026 that rejects the premise of eliminating all AI-related data risks. Instead, the company argued that organizations should focus on selecting the right combination of controls that match their risk tolerance and operational constraints.
"No enterprise can eliminate every AI-related data risk," said Jason Sohn, Executive Managing Director at Fasoo AI. "The practical question is which combination of controls best fits your organization's risk appetite, operational realities, and governance requirements."
Where AI data exposure happens
Security leaders visiting Fasoo AI's booth at the summit focused on three core challenges: controlling data exposure as AI systems expand, securing unstructured data used by AI, and maintaining visibility as data moves across cloud services, collaboration platforms, and external partners.
Ron Arden, EVP, CTO & COO at Fasoo AI, presented a session on June 1 examining how AI adoption reshapes enterprise data risks across different access channels. He outlined that no single control-whether network firewalls, proxies, enterprise browsers, or local agents-is sufficient on its own.
A framework for selection, not elimination
Arden presented a practical approach combining five elements: data discovery, classification, persistent encryption, activity monitoring, and adaptive policy enforcement across the AI data lifecycle.
The framework prioritizes supporting AI innovation while maintaining governance, compliance, and control over sensitive data. Organizations can build AI-ready infrastructure without creating friction for employees and external partners.
Fasoo AI demonstrated persistent file-level protection, AI-powered sensitive data detection, AI usage visibility, and secure collaboration capabilities through June 3 at booth 329.
For executives developing AI adoption strategies, the implication is straightforward: effective data loss prevention requires matching controls to specific organizational constraints, not pursuing impossible perfection.
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