2026年AI智能体风险与行动报告：AI应用、创新与新兴风险格局

AI Adoption, Innovation, and theEmerging Risk Landscape Nightfall AI Table ofContents ExecutiveSummary Artificial intelligence has crossed a structural thresholdinside the enterprise. What began as isolated experimentationwith GenAI tools has evolved into something far moreconsequential: AI is now embedded across workflows,connected to core business systems, and increasinglyacting autonomously through agents. This report draws on anonymized, aggregated data from Nightfall’s customer base across technology, financial services,healthcare, and other industries, encompassing more than 35,000 distinct applications. The data provides a point-in-timeview into how AI is actually being adopted and used inside real enterprise environments — revealing patterns that are ofteninvisible to traditional security approaches. The core finding of this report is that AI risk is no longer defined by the tools themselves, but by the data connectivity theyenable. Every integration between an AI system and a SaaS platform, every agent connected to internal systems, and everyemerging protocol such as MCP introduces new pathways for sensitive data to flow. These pathways are dynamic,compound, and increasingly autonomous, making traditional approaches to data security insufficient. Legacy data loss prevention (DLP) technologies were not designed for this model. Built for an earlier era of email and filetransfers, they rely on static rules, generate high volumes of low-quality alerts, and lack visibility into the modern AIecosystem. Security leaders now face a different challenge. The question is no longer whether AI is being used, but how deeply it isembedded, what systems it can reach, and what data it can access. Managing this risk requires a shift from tool-centricgovernance to a data-centric model. AI is now part of how organizations operate. Risk has followed the same path. The enterprises that succeed in this nextphase will not be the ones that slow adoption, but the ones that build the controls to support it. In an AI-driven world,securing data—not just tools—is the only strategy that scales. Key Findings Nightfall discovered more than35,000distinct applications in useacross its customer base, including519 GenAI toolsand103 AIAgent tools. 01 02The median organization runs182 applicationsand11 AI tools,while the largest operates over8,000 applicationswith more than170 AI tools. 03Nearly half of all organizations(49%)are actively using orexperimenting with AI agents — autonomous systems thattakeaction, not just assist. 100× in the last 16 months10,850+,a new pathwayMCP servers grew— from ~100 atlaunch toeach onetoenterprise data. 04 0581%of GenAI tool usage occursoutside the top three providers,meaning the majority of risk accumulates inthe long tailoflesser-known tools. AI Is Now Core Infrastructure AI has reached a tipping point. It has moved beyond experimentation into daily operations. 35,000+ Apps Discovered GenAI Tools AI has reached a tipping point. It has moved beyond experimentation into daily operations.Organizations are no longer using a single AI tool — they are operating across dozens of AI-enabledsystems embedded throughout their workflows. The median organization in our dataset uses 182total applications and 11 AI tools. At the top end, organizations operate over 8,000 applications with170+ AI tools. AI is no longer a tool. It is becoming part of the operational fabricof modern organizations. A C T I O NP L A NACTION PLAN •Elevate AI governance to a core security priority,not an extension of IT policy. The scale ofexposure demands program-level ownership.•Maintain a continuous AI asset inventory— what tools are in use, who is using them, and what datathey can access.•Start scanning now— monitor where sensitive data is moving and whether it’s flowing intounsanctioned AI tools. Don’t wait for a classification project.•Map the connections between AI tools and core business systems.AI risk isn’t about the tools —it’s about what they can reach.•Elevate AI governance to a core security priority,not an extension of IT policy. The scale ofexposure demands program-level ownership.•Maintain a continuous AI asset inventory— what tools are in use, who is using them, and whatdata they can access.•Start scanning now— monitor where sensitive data is moving and whether it’s flowing intounsanctioned AI tools. Don’t wait for a classification project.•Map the connections between AI tools and core business systems.AI risk isn’t about the tools— it’s about what they can reach. 02 The AI Maturity Curve AI adoption is not evenly distributed. Organizations fall into distinct maturity segments. 11AI tools — median organization 170+ AI tools — largest organization AI adoption is not evenly distributed. Organizations fall into distinct maturity segments, each withdifferent risk profiles: The gap between the median and the top is enormous. AI-NativeLeaders operate 15-17x more AI tools than the medianorganiza