Detecting, prioritizing, and remediating high-criticality findings is a daunting task. Managing such a large amount of findings can be overwhelming, and acquiring more resources to handle the task can be expensive and not scalable in the long run. The demands of the digital customer often outpace the security team’s ability to keep up with software and application development.
Furthermore, the gap in cybersecurity skills and the need for more training in the industry to meet advanced security and compliance needs further compound this problem.
AI-driven risk assessment, prioritization, and automation technologies can scale to meet the size of the ‘findings overload’ problem. Risk prioritization allows the security and remediation teams to focus on the risks that matter. AI-based engines can be configured to mitigate specific simple issues automatically, allowing security team resources to tackle the more complicated problems.
Two different applications may have different business impact to the enterprise. Therefore, the same finding in two different applications may represent two different levels of risk to the enterprise. It’s important to be able to quickly locate where the biggest risks are in the enterprise and to attribute the mitigation of that risk to the appropriate application risk managers. Application awareness is the key to risk attribution. Risk attribution is the key to prioritization and action. The Caveonix platform enables instantaneous and continuous application awareness by associating every cloud asset and all its findings with the application to which it belongs. Overall risk can be “rolled up” from application to organization and compared across organizations.
The Pareto Principle, named after economist Vilfredo Pareto, specifies that 80% of consequences come from 20% of the causes, asserting an unequal relationship between inputs and outputs. This principle serves as a general reminder that the relationship between inputs and outputs is not balanced. At Caveonix, we apply Pareto optimality to the prioritization of risk. All risk scores are quantitative and transformed from their base score by a variety of factors such as Business Impact Analysis, location within the enterprise, attack path, compensatory controls etc. The result of our analysis is an action plan: if you have 100 remediations to do, we identify for you the first 20 that give you the most “bang for the buck” and tell you by how much it will reduce your security risk or improve your compliance posture.
Enterprises today typically receive the results of a regular vulnerability/configuration scan in the form of a spreadsheet. Beyond the fact that they have no application awareness or risk prioritization, these spreadsheets must be continually revalidated for the status of findings. For example, are we dealing with the same false positive again? Security personnel may receive the scan on Monday. But, they’re not ready to take action until Friday after they have re-validated the findings. Caveonix Findings Management enables managers to classify findings by false positives, exemptions, compensatory controls, or those you need to act on. Every finding receives a unique identifier, which maintains state across scans. No time is wasted re-validating alerts. You can immediately get to those that require action and send them to Jira, ServiceNow, or your SOAR solution. With two-way integration, when you close out the alert in your IT Service Management platform, it automatically closes out in Caveonix.
AI powers Caveonix’s DefenseBot™ technology which utilizes Robotic Process Automation (RPA). It can automatically fix configuration settings, mitigate findings, and help avoid compliance drift. This significantly reduces Mean Time to Remediation (MTTR), with exposure windows often less than 30 seconds to prevent the exploitation of critical risks. DefenseBot™ seamlessly integrates with CI/CD pipelines for real-time detection and auto-remediation during pre-deployment.