Proactively Analyzing Enterprise Configurations for Risk
Why is This Even Important?
In the ever-evolving landscape of the digital enterprise understanding your configuration state is paramount. According to Gartner, by 2025, 99% of cloud breaches will be the result of a misconfiguration or end-user mistake. Therefore, configuration-based risk analytics is not just a choice; it's a necessity for the modern enterprise seeking to ensure security, compliance, stability, and a smooth customer experience.
At its core, configuration-based risk analytics should entail a meticulous examination of your IT system's configuration settings and components. It's akin to a thorough and continuous health checkup for your digital infrastructure. By shining a spotlight on weak links, misconfigurations, and compliance deviations that could lead to catastrophic security breaches or operational disruptions, it enables you to apply corrective measures swiftly, reducing the window of vulnerability created by misconfigurations and end-user mistakes.
To provide more color - operating without intelligence of the configuration state of your IT infrastructure and potential risk is akin to a software development team working without version control like git or svn: it's a substantial blind spot.
For software development version control is essential because it tracks code changes, allowing developers to understand the history and intent behind code alterations - and to revert to a previous state if something goes wrong. Without it, developers would lack the context for changes made over time, leading to a higher chance of conflicting changes, and a cumbersome process to untangle them when issues arise, increasing the likelihood of introducing errors and complicating the debugging process.
Changes to your hybrid environment and today's complex infrastructure should be considered no differently. Without configuration-based risk analysis, or what we call configuration risk intelligence, an organization is navigating its IT environment without a clear view of its configuration state. This is a significant risk because configurations dictate how systems behave. And, in reality, a misconfiguration can go unnoticed - for days, months, or even years - festering until it causes a security breach or operational failure.
While tools like Splunk and Dynatrace monitor logs and performance, they don't provide the same in-depth analysis of configuration states. Without this insight into configuration risk, you're vulnerable to issues that these tools simply aren't designed to catch. Think of configuration risk intelligence as an insurance policy for business continuity and reputation. By addressing vulnerabilities before they manifest into crises, you ensure seamless operations and uphold your organization's credibility in an era where trust is paramount.
What Should Configuration Risk Analysis Entail?
Configuration-based risk analysis cannot be a manual task anymore; but must be a programmatic and almost fully automated strategic initiative that leverages modern technology that empowers you to protect, optimize, and propel your enterprise to new heights. In fact, configuration-based risk analytics has never been more important when considering the growing complexity of IT infrastructure and how to optimally protect your business.
Configuration-based risk analytics, in this context, refers to the process of using data science and analytical techniques to assess and manage the potential risks associated with an organization's end-to-end IT environment - while also weighing the costs and benefits of the practice to the business.
Here's a breakdown of what this entails:
1. **Deep Data Collection and Analysis**: Advanced technology to collect and continually analyze all configuration data related to the IT infrastructure, such as server settings, database schemas, kernel parameters, applications, configuration settings, etc., as shown in Figure 1 is essential. The included analysis would tune out irrelevant, redundant, or noisy data points to focus attention on prioritized risks.
Figure 1: Evolven's Deep Data Collection includes all environments and configuration types with granular details.
2. **Continuous Drift Detection**: Configurations are continuously monitored to promptly detect and assess new risks and vulnerabilities by comparing the current changed state with previous system states and similar baselines. In this fashion, drift, and consistency analysis becomes an ongoing capability for risk analysis. This AI-based technology also discriminates between unauthorized and authorized changes to systems, evaluating every granular change based on its correlation with expected changes.
3. **Identifying Vulnerabilities**: Quick identification of vulnerabilities and weaknesses in the IT infrastructure is critical. AI, coupled with freely available open-source vulnerability and exposure databases, can rapidly determine risky changes as they happen and predict future issues before they escalate to incidents requiring remediation.
4. **Compliance and Regulations**: Risk analytics must also be run against every configuration change to ensure compliance with industry regulations and standards such as GDPR, HIPAA, NIST, OCC, PCI, and more. By utilizing configuration risk analytics early detection of changes that cause out-of-policy or out-of-compliance issues can be identified early rather than during an audit.
5. **Risk Assessment & Scoring**: Risk-based AI analytics assess the likelihood and impact of various risks, including cybersecurity threats, stability risks, performance risks, compliance & regulatory risks -, and more. They use statistical models, historical data, and accumulated expert knowledge to quantify these risks.
In the case of Evolven, our AI Engine goes through over 100 assessments or frameworks to assess the risk of each and every configuration change to your IT Infrastructure. Evolven provides a score based on the potential to do harm, prioritizing each change based on the need to be addressed.
6. **Predictive Modeling**: Advanced risk analytics, involves predictive modeling to forecast potential future risks. Machine learning algorithms help in identifying trends and patterns of emerging threats on how your environments look compared to stable environments or what has caused problems in the past - enabling you to take action before a change actually manifests into an incident.
AI-based configuration risk analytics involves using data-driven insights to proactively manage and minimize potential risks. As an example, Evolven's predictive modeling estimates the risk of configuration changes before they reach production, by identifying cybersecurity risks, stability risks, or even potential human mistakes like anomalous and conflicting configuration values.
To find out how Evolven can help improve your security, compliance, and stability through greater visibility into your configuration state - contact us today and learn more about our Configuration Risk Intelligence Platform.