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 Tenable
Here 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.