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ai security
What’s Powering Enterprise AI in 2025: ThreatLabz Report Sneak Peek
Zscaler ThreatLabz analyzed enterprise artificial intelligence (AI) usage patterns through November 2025, finding that OpenAI dominates large language model (LLM) transactions with 113.6 billion interactions, more than three times competitors like Codeium and Perplexity. Engineering departments drive the highest AI activity at 47.6% of transactions, followed by IT at 33.1%, with AI now embedded across productivity tools, coding platforms, and SaaS applications. Security teams are simultaneously blocking high-volume AI applications like GitHub Copilot and Grammarly that pose risks due to proximity to sensitive code and business communications, while threat actors increasingly operationalize autonomous attack workflows and exploit AI supply chain vulnerabilities.
Why it matters: Enterprise security practitioners must understand vendor concentration risks (OpenAI dependency), implement controls around AI tools accessing sensitive data in engineering and support workflows, and prepare defenses against AI-driven threats including agentic attacks and model supply chain exploits.
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ai security
What’s Powering Enterprise AI in 2025: ThreatLabz Report Sneak Peek
Zscaler ThreatLabz analyzed enterprise artificial intelligence (AI) usage patterns through November 2025, finding that OpenAI dominates large language model (LLM) transactions with 113.6 billion interactions, more than three times competitors like Codeium and Perplexity. Engineering departments drive the highest AI activity at 47.6% of transactions, followed by IT at 33.1%, with AI now embedded across productivity tools, coding platforms, and SaaS applications. Security teams are simultaneously blocking high-volume AI applications like GitHub Copilot and Grammarly that pose risks due to proximity to sensitive code and business communications, while threat actors increasingly operationalize autonomous attack workflows and exploit AI supply chain vulnerabilities.
Why it matters: Enterprise security practitioners must understand vendor concentration risks (OpenAI dependency), implement controls around AI tools accessing sensitive data in engineering and support workflows, and prepare defenses against AI-driven threats including agentic attacks and model supply chain exploits.
- Source published
- First seen by Cybersecurity Tracker