Tenable One Cloud Exposure Vulnerability Management deploys agentless scanning to assess cloud workloads and containers without installing agents on ephemeral assets. The platform uses a 1:1 license consumption ratio, which prevents the rapid depletion of license pools when dealing with short-lived infrastructure that spins up and down in minutes. This model allows teams to extend vulnerability management best practices into hybrid-cloud environments while maintaining unified visibility across on-premises and cloud assets. The solution is purpose-built for leaders who need cloud workload protection coverage without the complexity of a full cloud-native application protection platform. It correlates cloud workload risks with container vulnerabilities to drive exposure-driven prioritization, ensuring that remediation efforts focus on what truly increases risk to the business.
Deploying Agentless Scanning
Start by establishing a comprehensive inventory of all assets across hybrid and multi-cloud environments, including containers, virtual machines, serverless functions, and APIs. You cannot secure what you cannot see, so ensure the discovery process covers every layer from development to production. Adopt agentless tools that enable continuous monitoring and assessment, providing real-time insights into potential vulnerabilities rather than relying on periodic scans. The transient nature of cloud-native components demands this shift because resources are often spun up and down in minutes, rendering traditional periodic approaches ineffective. Integrate security controls directly into CI/CD workflows to detect and address vulnerabilities early in the development lifecycle, reducing risk before deployment. Deploy automated testing tools to identify vulnerabilities in code, container images, and Infrastructure-as-Code templates before they reach production. This approach addresses the expanded attack surface created by microservices and APIs, which require granular and continuous vulnerability assessments.
| Feature | Specification |
|---|---|
| Scanning Method | API-based, agentless |
| License Ratio | 1:1 consumption |
| Coverage | Workloads, containers, hybrid-cloud |
| Prioritization Engine | VPR (AI-driven exploit prediction) |
The licensing model simplifies adoption for customers dealing with highly ephemeral assets, as it does not consume a high number of licenses from the pool. Teams should run short trials of these agentless technologies and assess them against organizational requirements aligned with their specific risk tolerance. Consult with development teams to define cloud vulnerability management requirements that match your operational reality. If you skip the initial asset discovery phase, you will miss critical exposures in shadow AI or misconfigured entitlements. Does the current integration fully cover your third-party data sources?
Prioritizing with Exposure Data
Prioritize remediation faster using Tenable’s vulnerability data, powered by Tenable Research and AI-driven exploit prediction known as VPR. Integrate cloud vulnerability findings with those from on-premises and other environments for a unified view of risk to determine what to remediate first. Tenable Hexa AI agentic orchestration accelerates remediation and shortens the time from detection to resolution, eliminating exposures before they become breaches. Built on the Exposure Data Fabric, this system understands how risk moves through the environment and prioritizes precisely based on pathways for exploitation and impact. Use risk-based prioritization techniques such as attack path management and toxic combinations to focus on high-impact threats to critical assets. Standard policies across cloud platforms via policy-as-code maintain consistent security enforcement, ensuring scalability across diverse environments.
The platform reveals "toxic access" and flags anomalies to get organizations closer to least privilege, cutting cloud risks faster without requiring deep expertise. However, the reliance on external intelligence means that if the underlying data set lacks context for a specific niche stack, the prioritization may lag behind manual analysis. Security teams must still verify that the automated remediation workflows align with their specific risk appetite before executing changes. Start scanning for known vulnerabilities in image and container open-source components first, then mature across multi-cloud by adopting the full CNAPP solution. Iterate on the process to incorporate and refine the exposure management program continuously.
