AI security is fragmenting. One protection tool monitors prompts, another cloud configurations, another AI identities, and still another endpoints.Individually, these point solutions are useful. Collectively, they leave security teams blind to how weaknesses can be chained across the full AI attack surface.AI risk does not exist in isolation. An approved chatbot, an overprivileged agent, or an exposed endpoint each look manageable separately, but together they can create a viable compromise path.Tenable calls this the "AI exposure management gap," defining it as the inability to see where AI is used, where it runs and how interconnected vulnerabilities lead to material risk.
The final step is especially important. Huber explains that a medium-severity AI misconfiguration could become critical when connected to an exposed LLM, excessive agent entitlements and sensitive business systems."It's about understanding how toxic combinations of risk coalesce to create business exposure," he writes.AI security cannot be reduced to securing individual models, prompts or agents. The enterprise attack surface spans applications, identities, infrastructure, data and human behavior. Minor weaknesses may become major risks if they can be chained together.The answer is not to purchase and implement yet more point tools. Instead, the tools' findings must be merged into one risk picture.By continuously discovering AI instances, mapping relationships, assessing usage and prioritizing attack paths, exposure management can turn fragmented AI protections into a coordinated security program, giving organizations the visibility needed to secure AI without slowing its adoption.
Why fragmented AI-protection tools fail to deliver full security
Traditional controls examine AI-related risks by domain: cloud posture here, identity permissions there, application risk somewhere else. But attackers don't respect those boundaries."We often can't isolate AI risk to a single asset," explains Tenable CSO Robert Huber in a recent blog post. "Rather, it emerges from a string of interconnected elements (such as applications, infrastructure, identities, and data) that in aggregate create exposure."For example, an employee might use an approved chatbot connected to Amazon Bedrock agents with privileged access to enterprise-resource-planning and customer-resource-management systems. That sounds harmless, but if an attacker compromises the employee’s laptop in some way, the otherwise safe AI workflow becomes a pathway to exfiltrate sensitive data."Protecting data in today's AI-assisted work environments becomes exponentially more difficult because each one of the myriad interactions with AI assets (e.g., every prompt, file upload, generated response, integration, and configuration) can put intellectual property, customer information, and confidential plans at risk," writes Huber.What can go wrong if AI security is not strong enough
The scale is already substantial. In another blog post, Tenable CPO Eric Doerr reports that during one 30-day period, Tenable detected 457 million AI-related security issues across more than 7,000 organizations.That averages out to about 62,000 exposures per organization. Many of these issues involved misconfigurations and unmanaged dependencies rather than conventional CVEs.The widespread use of shadow AI magnifies the danger. In one example Doree cites, a contractor installed 12 unauthorized OpenClaw agents on cloud workloads, gave them access to source code and API keys, and managed them remotely through Telegram, itself another unsanctioned service."If we didn’t already know that shadow AI was a problem," writes Doerr, "data like this makes it clear every organization needs to visualize, map, assess, and protect with a comprehensive exposure management program."How exposure management can secure your AI deployment
A properly configured exposure-management program continuously discovers sanctioned and shadow AI across endpoints, cloud workloads, APIs, and externally exposed services. It maps relationships among models, infrastructure, storage, networking, identities and data to show where real exposure exists.This contextual view enables environment-wide prioritization. Instead of treating an overprivileged agent, a cloud misconfiguration or a vulnerable laptop as separate tickets, security teams can identify combinations that create attack paths to critical assets.Tenable One AI Exposure, built to protect AI implementations as part of a larger exposure-management platform, provides visibility into prompts, uploads and employee or agent interactions, helping identify sensitive-data exposure, prompt injection and jailbreak attempts."To keep your organization secure in the age of AI," writes Doerr, "you need to assess all security threats, including identity flaws and misconfigurations, across your IT, operational technology (OT), AI, IoT, and cloud assets and see how they combine to create attack paths leading to your organization’s most sensitive systems and data."Five steps to secure your AI deployment
Huber recommends a five-step framework for any organization using AI:- Establish AI governance and acceptable-use policies, detailing approved tools and approved use cases, plus what's off limits and what happens if rules are broken.
- Continuously discover AI instances across the attack surface, including abandoned deployments, browser extensions, and embedded tools.
- Secure AI workloads and agents by fixing misconfigurations, patching vulnerabilities, instituting least-privilege access and quickly isolating compromised AI agents.
- Assess AI usage and interactions, such as by establishing who's using AI and why, monitoring what kind of data is being shared, and detecting jailbreaking attempts and potentially malicious prompts.
- Analyze AI risks in the context of all other enterprise exposures to get a holistic view of potential threats, or, as Huber puts it, "understanding how toxic combinations of risk coalesce to create business exposure."
