Forescout has introduced Rapid Insight Assessment, a new security evaluation service designed to identify unknown assets, exposed services, unmanaged devices, and segmentation issues in just a few days. The company links its launch to advancements in artificial intelligence models capable of locating vulnerabilities and developing increasingly complex exploitation chains, although the idea that any exposure could be automatically exploited “within minutes” should be understood as a risk scenario rather than a widespread reality.
The key features of Rapid Insight Assessment in 20 seconds
- Forescout combines open-source intelligence (OSINT) and passive network observation.
- Its Flyaway Kit allows analysis of IT, IoT, and OT environments without installing a permanent platform.
- Seeks to locate unknown devices, exposed administrative interfaces, and risky communications.
- Also correlates assets with known and exploited vulnerabilities.
- The rise of offensive AI increases pressure to reduce detection and remediation times.
This approach addresses a problem that predates current AI models: many organizations don’t exactly know which devices are connected, which are exposed to the Internet, or which systems are still communicating within their networks.
Forgotten servers, virtual machines created for temporary projects, IoT devices, industrial equipment, admin interfaces, and remote access services can remain active for years without properly appearing in the corporate inventory.
AI now adds another variable. The most advanced models are demonstrating growing capabilities to investigate vulnerabilities, write exploit code, and chain together different security actions. This doesn’t mean that any attacker can automatically compromise any network, but it does reduce some manual effort needed to analyze software and find attack paths.
An assessment that tries to uncover what the inventory doesn’t show
Rapid Insight Assessment combines two perspectives.
The first looks at the organization from outside through OSINT (Open Source Intelligence) analysis to locate publicly accessible assets and services. The goal is to approximate what an attacker might find when probing the external surface of a company.
The second analyzes the internal network using Forescout’s passive visibility capabilities.
For this, Flyaway Kit can be used—a portable, self-contained system that Forescout employs to bring discovery capabilities to facilities where deploying a permanent security platform may be challenging.
The key feature is that the observation is passive.
Instead of aggressive scans against each device, it analyzes traffic to identify what equipment exists, how they communicate, and what characteristics they have. This approach can be especially useful in Operational Technology (OT) networks, where certain industrial systems are sensitive to active testing typical in standard IT networks.
It covers traditional IT systems, IoT devices, OT infrastructure, and other equipment that may not be managed by the usual corporate tools.
Forescout claims to identify issues such as exposed remote access services, vulnerable protocols, accessible admin interfaces, previously unknown devices, and potentially dangerous communications between different network segments.
It also aims to identify devices associated with vulnerabilities listed in the Known Exploited Vulnerabilities (KEV) catalog maintained by the U.S. Cybersecurity and Infrastructure Security Agency (CISA).
Prioritization is especially aided by this approach.
Finding thousands of CVEs within a company doesn’t necessarily mean they all pose the same risk. CISA maintains KEV precisely as a catalog of vulnerabilities with evidence of real exploitation and recommends using it as part of vulnerability management prioritization.
AI is changing the speed of vulnerability research
Forescout presents its service around a compelling idea: organizations should find their exposures before AI does.
While there is an obvious commercial angle, the technological problem it describes is increasingly supported by independent evidence.
In May 2026, Anthropic released evaluation results using Claude Mythos Preview, observing a leap over previous generations in the ability to develop exploits. Their researchers confirmed that the model could find complex vulnerabilities, generate exploitation primitives, and chain them to build complete attack sequences within their testing environments.
An earlier case is even more illustrative. In March, Anthropic reported that Claude Opus 4.6 had found 22 vulnerabilities in Firefox over two weeks of collaboration with Mozilla and demonstrated how the model had developed an exploit for one. The company clarified that the exploit worked in a testing environment where some protections found in real browsers had been removed.
That last nuance is important.
Moving from “a model can develop an exploit under certain conditions” to “AI can automatically compromise any vulnerability within minutes” is a leap that current evidence does not support as a general statement.
However, the trend appears clear.
In February, OpenAI explained that models have evolved from completing small pieces of code to working autonomously for hours or days on complex tasks. The company views cybersecurity as one of the areas where this capability increase could benefit defenders, but also introduce new risks.
Anthropic has also documented real cases in 2026 of using language models in cybersecurity operations and has begun mapping these activities onto the MITRE ATT&CK framework.
The problem may lie in devices no one knew existed
This evolution partly shifts defensive priorities.
For years, vulnerability management has mainly followed a well-known sequence: inventory systems, scan them, find CVEs, prioritize, and patch.
The problem arises when the first step fails.
An organization might have an excellent vulnerability management system covering 95% of its assets, yet completely overlook that one server providing a gateway from the Internet.
Similarly, an old IP camera, an industrial system, an admin console, or a virtual machine that never entered the official inventory could be hiding unnoticed.
This is known as shadow IT, though in industrial networks and large organizations, the issue can go far beyond unauthorized applications.
The expansion of IoT, virtualization, cloud, containers, and remote work has significantly increased the number of assets a company must control.
Hence, exposure management is shifting from “what vulnerabilities exist?” to “what can an attacker actually reach?”
It’s not just semantics.
A properly isolated server with critical vulnerabilities may pose less immediate risk than a seemingly secondary device with weak credentials and an admin interface directly exposed to the Internet.
IT and OT make maintaining a reliable inventory especially challenging
Forescout’s approach is particularly relevant for organizations where corporate networks and industrial infrastructure coexist.
Deploying a security agent on Windows or macOS PCs in an office is straightforward. In a factory, hospital, automated warehouse, or energy facility, there may be PLCs, sensors, cameras, medical equipment, and proprietary systems where installing additional software is impossible or ill-advised.
Passive observation seeks to address this limitation.
It can also reveal relationships that a conventional inventory cannot show. Knowing that a device exists is useful; understanding which servers it communicates with, what protocols are used, and which segments it’s accessible from allows for a better attack surface assessment.
This context will become increasingly critical if AI-based tools succeed in automating much of the recognition and analysis currently performed by researchers and attackers alike.
This same technology can also work in favor of defenders. Anthropic has experimented with Claude alongside the Pacific Northwest National Laboratory to simulate attacks on a water treatment plant, reducing the time needed for certain red teaming tasks.
The challenge, therefore, is not only defending a network against AI-powered attackers.
It also involves leveraging automation and AI to reduce the time defenders need to discover, prioritize, and fix issues.
Rapid Insight Assessment begins at the most basic level: before deciding what to patch, an organization must know what it truly has connected and what parts of it an attacker could reach.
Frequently Asked Questions
What is Forescout Rapid Insight Assessment?
It’s an assessment service that combines external analysis and passive network visibility to locate unknown assets, public exposures, risky communications, and other security issues.
What is Forescout Flyaway Kit?
It’s a portable platform used to gain visibility into assets and communications across different environments, including IT, IoT, and OT, without requiring permanent deployment.
Can AI find and exploit vulnerabilities automatically?
Advanced models have already demonstrated the ability to identify vulnerabilities and develop exploits in certain evaluations. However, this does not mean that any vulnerability can be automatically exploited or that all attacks can occur without human intervention.
What are KEV vulnerabilities?
They are vulnerabilities included in CISA’s Known Exploited Vulnerabilities catalog because there is evidence they have been exploited in real-world scenarios. CISA recommends prioritizing these vulnerabilities for remediation.
via: forescout
