We posted recently on the unique composition and actionability of UncommonX Relative Risk Ratings (R3). As discussed, these ratings are not an academic exercise. They are a precise business tool to optimize security and improve efficiency.
In this post, we will dive deeper into how AI evaluates the elements of the R3 score and how this provides an accurate and customer-specific view of risk and remediation.
For those new to our Relative Risk Ratings, R3 consists of two main factors: your target score and your risk score. Your target score represents your cybersecurity investment relative to the NIST Cybersecurity Framework, based on our complete visibility into your security technology stack. Your risk score is a measure of volatility based on real-time snapshots of your environment's risk profile.
The collection of the data and the scoring are standard parts of our system functionality. The application of NIST CSF provides foundational assessment of controls in place. The use of vulnerability data, telemetry, threat intelligence, and business context are critical to the complete visibility that is core to our Exposure Management platform.
Your 20 highest-risk hosts are evaluated in real time across five factors: Priority (the function and importance of the host), Vulnerability (whether it possesses critical or exploitable vulnerabilities), Telemetry (whether the host is classified correctly based on its services and traffic), Controls (whether it is appropriately managed and protected), and Detections (whether there are currently open cases against it). Each of these factors contributes to a dynamic, evidence-based risk score that changes as your environment changes.
As we have discussed throughout this series, AI works best when it has complete and accurate data to work with. In our system, AI works on real data from the customer network to enhance every layer of the R3 scoring process.
AI enhances the risk ratings with a broader set of variables and combinations of factors. Rather than evaluating each risk signal in isolation, AI identifies patterns and correlations across data sources that a manual review would miss. This means the risk score reflects not just individual vulnerabilities, but the compound effect of multiple factors acting together.
AI models more accurate outcomes based on multiple data sources and client-specific weightings. Every organization has a different risk profile, different priorities, and different infrastructure. AI allows our platform to tailor the scoring and recommendations to each customer's actual environment rather than applying a one-size-fits-all model.
AI learns from continuous use of new and updated system, vulnerability, and threat data. As the threat landscape evolves and as a customer's environment changes, the models improve. This creates an always-improving method for sustained risk management that gets smarter over time.
AI enables our patented discovery to not only categorize all devices and connections on a network. It also helps build a more complete contextual profile that generates comprehensive network intelligence essential to IT, Security, and Operations.
AI leverages our proprietary analytics, including R3 scores, vulnerability management scores, business context, and threat ratings to generate real-time insights and actions to be addressed by SOC, IT, Security, and Operations staff. This provides efficiency and effectiveness by enabling users to focus on real risks in real time, applying their resources to what matters most.
The application of AI in our system moves from typical probabilistic modeling to a more complex intermix of variables and potential outcomes. This provides our customers with a truly differentiated capability that addresses the most critical risk factors to secure their environment. AI processes countless scenarios to assess risk and provide explicit, customer-specific instructions for remediation. The result is not a generic list of recommendations. It is a prioritized action plan built on the reality of each customer's network.
For CIOs and security teams, AI-enhanced R3 scoring translates directly into better decision-making. Instead of sifting through alerts and trying to determine what is most urgent, the platform surfaces the highest-impact risks with clear instructions on how to address them. Teams spend less time investigating and more time remediating. And as risks are addressed, the gap between your risk score and your target score decreases, providing measurable evidence that your security posture is improving.
In summary, our system applies AI to customer-specific data to evaluate parameters, probabilities, and potentials; identify the highest risks and provide instructions to build resiliency; and create an always-improving method for sustained risk management.
Our next post will share information on applying our AI capabilities to generate product-specific instruction sets for use by clients to better secure customer environments.
Contact us today to learn how AI-powered risk intelligence and our Exposure Management platform can help your organization turn complete visibility into smarter, faster action.