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Each Monday, the Tenable Exposure Management Academy provides the practical, real-world guidance you need to shift from vulnerability management to exposure management. In this post, we feature the first Exposure Management Academy FAQ. We’ll run these FAQs from time to time to share some of the most common questions we receive about exposure management. You can read the entire Exposure Management Academy series here.By Team TenableHere at the Exposure Management Academy, we get questions all the time. So we’re inaugurating an occasional FAQ series this week with an up-close look at exposure management itself, the role of AI in exposure management and how cyber exposure management and cloud security work together. In future FAQs, we’ll cover a range of topics. Stay tuned. What is exposure management?It’s the essential question that always comes first: Just what is exposure management? In our first Exposure Management Academy post we covered what exposure management is and why it matters in depth. But for this FAQ, we’ll keep it short. Exposure management gives teams visibility and context across the modern attack surface so they can separate the actual exposures that can have a material impact on the business from all the noise. This means that your team can minimize churn and help prevent breaches by closing the exposures (or toxic risk combinations) attackers exploit before attacks get underway.As the natural evolution of vulnerability management, exposure management extends visibility to include all preventable risks across the attack surface: Common Vulnerabilities and Exposures, misconfigurations, excessive permissions and all asset types — multi-cloud, IT, OT, IoT, identities, applications, containers, as well as unseen and unmanaged assets. Unlike traditional security prioritization approaches, exposure management requires a mindset shift. Not all risk is created equal and not every risk needs to be addressed instantly. Instead, exposure management combines threat intelligence, such as accessibility and exploitability of risks, with technical and business context, including attack paths leading to crown jewels, to prioritize remediation of toxic risk that is most likely to have an impact on your organization.How does exposure management use AI?At the heart of exposure management is the need to unify visibility, insight and action across traditionally siloed tools, processes and staff. Solving this challenge requires more than just aggregation of data in a central repository. Artificial intelligence plays a critical role in exposure management by deduplicating, correlating and normalizing asset and risk data across typically siloed tools and technologies. It maps the complex data relationships needed to identify and visualize toxic risk combinations and attack paths, which prioritizes business-impacting exposures. Plus, it enriches decision making with additional context, such as threat intelligence and MITRE techniques, to provide the remediation guidance needed to quickly and effectively mobilize teams.Exposure management platforms typically put an array of AI flavors to work, including generative artificial intelligence, deep learning, AI and machine learning to fuel its capabilities. They help improve end-user productivity and enable preventive security in three ways:Help explain: AI can provide succinct guidance so you can better understand product findings.Conduct a search: AI can simplify searching across your asset inventory, which provides complete visibility.Take action: AI can proactively give you insights for actions that will have the most impact on your exposures.Exposure management platforms also offer a wide range of assessment methods that surface AI software packages, libraries and browser plugins. This capability helps you to see unauthorized AI usage, detect AI vulnerabilities and gain clarity on AI development occurring within your organization.For Tenable, AI is integral to the functionality of the Tenable One Exposure Management Platform. Below are some examples of how we put AI to work in the product to solve other complex challenges, such as:Identifying vulnerabilities that attackers are likely to exploit in the short term: Machine learning-based algorithms power our Vulnerability Priority Rating (VPR). By analyzing each vulnerability regularly to determine how likely it is that an exploit could be used against it, VPR provides a score you can use to prioritize your remediation efforts.Predicting the operating system (OS) of an unauthenticated asset: Machine learning-based algorithms enable Tenable to use host response to TCP packet data to predict the OS of an unauthenticated asset. This increases vulnerability assessment and inventory accuracy.Improving the efficiency and effectiveness of common processes: Generative AI-based research tools improve the efficiency and effectiveness of processes like reverse engineering, code debugging, web app security and visibility into cloud-based tools.Achieving a unified view of privileges: AI-based methods deliver a holistic view of all user identities and entitlement risks, including on-premises and cloud environments.Is exposure management cloud-based?Yes, you should expect an exposure management solution to be cloud-based for some very strategic reasons. First, exposure management requires continuous assessment of the threat landscape and dynamically changing environments, such as containers and Kubernetes. That calls for a highly scalable data platform with the storage and compute power necessary to process trillions of unique asset, identity, risk and threat data points.Exposure management platforms often collect data through API integrations with existing point security tools that are usually cloud-based, including cloud security posture management, external attack surface management, vulnerability management, identity and access management, endpoint detection and response/extended detection and response, configuration management database and cloud infrastructure and entitlement management. These integrations are far easier, faster and more robust when the platform itself is cloud-native and API-first.In addition, exposure management requires advanced relationship mapping and analysis, such as attack path modeling, machine learning for prioritization and AI-generated remediation guidance. These compute-heavy tasks are best handled in cloud environments built for data science and real-time inference.Organizations can deploy a SaaS-based exposure management platform in days rather than months and quickly deliver continuous improvements. It also enables continuous delivery of new capabilities, such as new risk models, threat intelligence and exposure logic.Have a question about exposure management you’d like us to tackle?We’re all ears. Share your question and maybe we’ll feature it in a future post. MktoForms2.loadForm("//info.tenable.com", "934-XQB-568", 14070);
Analysis Summary
# Best Practices: Implementing Comprehensive Exposure Management
## Overview
These practices focus on establishing an effective Exposure Management (EM) program. EM aims to gain comprehensive visibility across the entire attack surface (including IT, Cloud, OT, Identity, and vulnerabilities), focus remediation efforts to prevent likely attacks using advanced analysis (like attack path modeling), and accurately communicate cyber risk to support business objectives. The guidance emphasizes leveraging modern, cloud-native, and API-first platforms for efficiency and continuous improvement.
## Key Recommendations
### Immediate Actions
1. **Establish Attack Surface Visibility:** Immediately begin aggregating data streams from core security domains: Vulnerability Management (VM), Cloud Security (CSPM/CNAPP), Identity and Access Management (IAM), and Operational Technology (OT) security sources.
2. **Deploy/Integrate EM Platform:** If utilizing a dedicated EM platform, expedite the deployment process. Prioritize cloud-native, SaaS-based solutions for deployment in days, rather than months.
3. **Enable Foundational Analysis:** Activate initial Attack Path Analysis capabilities within the chosen platform to identify the highest-risk, interconnected exposure points that could lead to business impact *now*.
### Short-term Improvements (1-3 months)
1. **Prioritize Risk Using EM Metrics:** Shift remediation prioritization away from raw vulnerability counts to risk-based metrics provided by the EM platform (e.g., metrics factoring in exploitability, asset criticality, and lateral movement potential).
2. **Integrate Key Security Tools:** Ensure robust, bidirectional integration between the EM platform and existing security tools, specifically: configuration management database (CMDB), EDR/XDR systems, and patch management solutions.
3. **Implement Cloud Exposure Checks:** Integrate Cloud Security posture management (CSPM) and Cloud Infrastructure Entitlement Management (CIEM) data to understand and remediate improper entitlements and misconfigurations in cloud environments immediately.
### Long-term Strategy (3+ months)
1. **Develop AI/ML-Driven Remediation Workflow:** Leverage machine learning capabilities for faster prioritization and utilize AI-generated remediation guidance to reduce Mean Time To Remediate (MTTR) and operational overhead.
2. **Automate Identity Exposure Reduction:** Formally integrate Identity Exposure findings with existing IAM processes, focusing on reducing excessive privileges identified via CIEM tools.
3. **Establish Continuous Capability Delivery:** Ensure the chosen platform supports continuous delivery of new risk models, threat intelligence, and updated exposure logic, adapting rapidly to the evolving threat landscape.
4. **Formalize Reporting Cadence:** Institutionalize reporting on exposure reduction metrics to executive leadership and the board, translating technical findings into quantifiable business risk reduction.
## Implementation Guidance
### For Small Organizations
- Utilize SaaS-based, integrated Exposure Management platforms for rapid deployment and reduced infrastructure overhead.
- Focus initial efforts on integrating essential data sources: basic vulnerability scanning and cloud asset inventory (if applicable).
- Prioritize fixing critical path vulnerabilities on user-facing or production assets identified via simple attack path modeling.
### For Medium Organizations
- Mandate API-first integration strategies when connecting existing VM, EDR, and CMDB solutions to the EM platform.
- Dedicate security engineering staff time (e.g., 25% allocation) specifically to analyzing attack path visualizations to validate and refine remediation queues.
- Begin integrating OT/IoT security data sources if these assets are present within the business environment.
### For Large Enterprises
- Implement a centralized, cloud-native EM platform capable of handling massive data ingestion and compute-heavy tasks (like relationship mapping).
- Formalize data governance required for reliable CMDB integration and consistent asset criticality tagging across disparate business units.
- Leverage advanced capabilities like GenAI analytics for sophisticated threat modeling and custom risk orchestration across multiple security domains.
## Configuration Examples
*Note: Specific configuration syntax requires referencing the chosen platform documentation. The principle below focuses on necessary data integration endpoints.*
**API Integration Best Practice:**
Ensure all integrating security tools (VM scanner, CMDB, EDR) possess appropriately scoped credentials (read-only access for inventory/findings) and are configured to push data streams to the EM platform's ingestion APIs, utilizing formats compatible with standardized object definitions (e.g., JSON payloads).
**Attack Path Modeling Focus:**
Configure asset criticality labeling based first on business function, then service dependency, ensuring that the EM platform weights remediation based on the potential business impact of an exposure chain, not just technical severity alone.
## Compliance Alignment
- **NIST Cybersecurity Framework (CSF):** Directly supports Identify, Protect, and Detect functions by providing comprehensive visibility and risk-prioritized remediation reporting.
- **ISO 27001/27002:** Aligns with Annex A controls related to asset management, vulnerability management, and security monitoring.
- **CIS Critical Security Controls (CSC):** Supports foundational controls related to Inventory and Control of Software/Hardware Assets, and Vulnerability Management.
## Common Pitfalls to Avoid
- **Tool Sprawl Over Integration:** Do not attempt to manage exposure across siloed dashboards. Prioritize platforms that natively integrate and contextualize diverse data sources (VM, Cloud, Identity) rather than relying on manual correlation or separate point solutions.
- **Ignoring Asset Criticality:** Treating all vulnerabilities equally based on CVSS score alone will lead to resource paralysis. Always prioritize based on the asset's business role and potential for multi-stage compromise.
- **Stagnant Platform Strategy:** Avoid locking into on-premise solutions for EM. The complexity of modern attacks spanning IT/Cloud/OT requires the scalability and continuous capability updates offered by cloud-native, API-first platforms.
## Resources
- **Attack Path Analysis:** Utilize platform features that model the sequence of weaknesses leading to a high-value asset.
- **Cloud Infrastructure Entitlement Management (CIEM):** Engage relevant documentation or tools focused on auditing and right-sizing cloud identity permissions ("Identity Exposure").
- **Tenable One Exposure Management Platform:** Reference platform documentation for specific integration guides regarding CNAPP, Vulnerability Management, and OT Security capabilities. (Link to platform reference documentation would be provided here, defanged as per instruction).