Continuous Vulnerability Management

Close the gaps between security assessments and significantly reduce risk.

Today’s processes involve different teams, using multiple point solutions, significantly adding complexity and time to the critical patching process. Discover, assess, and patch critical vulnerabilities in real-time and across your global hybrid-IT landscape all from a single app.

Multi-Layer Protection Against Ransomware

Ransomware is one of the biggest threats facing the cybersecurity industry today. The magnitude of its danger continues to increase by the day, leaving organizations around the country vulnerable. Adlumin is a Managed Security Services platform that protects against ransomware attacks through its new product, Total Ransomware Defense (TRD). TRD provides your network with multi-layer protection, eliminating the success of a ransomware attack at each layer.

Continuous Vulnerability Management Lifecycle

 Asset Management

Automated asset identification and categorization

Knowing what’s active in a global hybrid-IT environment is fundamental to security. Continuous Vulnerability Management enables customers to automatically discover and categorize known and unknown assets, continuously identify unmanaged assets, and create automated workflows to manage them effectively. After the data is collected, customers can instantly query assets and any attributes to get deep visibility into hardware, system configuration, applications, services, network information, and more.

Vulnerability Management

Real-time vulnerability and misconfiguration detection

Continuous Vulnerability Management enables customers to automatically detect vulnerabilities and critical misconfigurations per CIS benchmarks, broken out by asset. Misconfigurations, unlike vulnerabilities, do not have formal CVE IDs associated, which can leave assets out of compliance and vulnerable to attack. Continuous Vulnerability Management continuously identifies critical vulnerabilities and misconfigurations on the industry’s widest range of devices, operating systems, and applications.

 Threat Prioritization

Automated remediation prioritization with context 

Continuous Vulnerability Management uses real-time threat intelligence and machine learning models to automatically prioritize the vulnerabilities posing the most significant risk to your organization. Indicators, such as Exploitable, Actively Attacked, and High Lateral Movement, bubble up current vulnerabilities that are at risk while machine learning models highlight vulnerabilities most likely to become severe threats, providing multiple levels of prioritization. Further prioritize remediation by assigning a business impact to each asset, like devices that contain sensitive data, mission-critical applications, public-facing, accessible over the internet, etc.

 Patch Management 

Patching and remediation at your fingertips

After prioritizing vulnerabilities by risk, Continuous Vulnerability Management rapidly remediates targeted vulnerabilities, across any size environment, by deploying the most relevant superseding patch. Additionally, policy-based, automated recurring jobs keep systems up to date, providing proactive patch management for security and non-security patches. This significantly reduces the vulnerabilities the operations team must chase down as part of a remediation cycle.

Additional Resources

Continuous Vulnerability Management – Complexities of the Remediation Process

Managing and understanding vulnerabilities is a continuous activity, requiring the focus of time, attention, and dedicated resources.

The Importance of Proactive Security

Download this white paper to understand the difference between proactive and reactive security measures, discover the business benefits of being proactive, learn how to advance both your bottom line and your information security, and more.

Adlumin’s UEBA Features

No Data Limits

Adlumin delivers hyper-scalable patented architecture for ingesting streaming data and continuously training. The platform updates machine learning algorithms for anomaly detection across an infinite quantity of data streams to discover threats, malfunctions, IT operations failures, and other predictive analytics use cases.

Behavioral Patterns

Adlumin deploys autonomous parsing of streaming data using advanced algorithms that can detect anomalous events by efficiently learning baseline maps of normal messages, utilizing techniques that are especially scalable for cloud computing environments.

Full Network Visibility

Adlumin delivers end-to-end visibility into incoming data records through the detection process, giving the user control over the detection and surveillance capability via state-of-the-art and intuitive entry points.

Visualize Total Metrics

Adlumin visualizes usage, web, and project execution metrics from any system, including Jenkins, using isolation forest and other tree-based techniques within the umbrella of unsupervised machine learning, allowing AF users to direct investigative or analyze resources effectively.

Machine Learning as a Service (MLaaS)

Adlumin can build Machine Learning as a Service (MLaaS) applications covering a broad array of use cases, complementing the cloud computing resources already available within various F35 development environments by delivering turnkey solutions to numerous forecasting and estimation challenges.

Cluster Analysis

Adlumin automates cluster analysis for use cases that call for quick discovery of outlier instances or for finding coherent groups of observations within a large mass of data, using techniques including K-Nearest-Neighbors (KNN) and Cluster-Based Local Outlier Factor (CBLOF).

User Command Executions

Adlumin’s model user command executions using PowerShell profiling and codifying the statistical distribution of resulting metrics, leading to detections of abnormal executions without excessive false positives and without arbitrarily discarding analysis-worthy data.

Easy-to-Understand, Powerful Insights

Adlumin deploys machine learning techniques based on graph-theoretic metrics, such as Principal Components Analysis, to systematically reduce the complexity of the feature space and reveal sharp insights in the data to detect anomalies malfunctions.

Graph Theory

Adlumin analyzes user and entity resources using graph theory, giving AF security monitors powerful visuals of how different network elements are connected and aiding further investigation.

Ready to demo?

Schedule a briefing and live demo of Adlumin’s managed security services platform.

Adlumin’s UEBA Features

No Data Limits

Adlumin delivers hyper-scalable patented architecture for ingesting streaming data and continuously training. The platform updates machine learning algorithms for anomaly detection across an infinite quantity of data streams to discover malfunctions, IT operations failures, threats, and  other predictive analytics use cases.

Behavioral Patterns

Adlumin deploys autonomous parsing of streaming data using advanced algorithms that can detect anomalous events by efficiently learning baseline maps of normal messages, utilizing techniques that are especially scalable for cloud computing environments.

Full Network Visibility

Adlumin delivers end-to-end visibility into incoming data records through the detection process, giving the user control over the detection and surveillance capability via state-of-the-art and intuitive entry points.

Visualize Total Metrics

Adlumin visualizes usage, web, and project execution metrics from any system, including Jenkins, using isolation forest and other tree-based techniques within the umbrella of unsupervised machine learning, allowing AF users to direct investigative or analyze resources effectively.

Cluster Analysis

Adlumin automates cluster analysis for use cases that call for quick discovery of outlier instances or for finding coherent groups of observations within a large mass of data, using techniques including K-Nearest-Neighbors (KNN) and Cluster-Based Local Outlier Factor (CBLOF).

Graph Theory

Adlumin analyzes user and entity resources using graph theory, giving AF security monitors powerful visuals of how different network elements are connected and aiding further investigation.

User Command Executions

Adlumin’s model user command executions using PowerShell profiling and codifying the statistical distribution of resulting metrics, leading to detections of abnormal executions without excessive false positives and without arbitrarily discarding analysis-worthy data.

Easy-to-Understand, Powerful Insights

Adlumin deploys machine learning techniques based on graph-theoretic metrics, such as Principal Components Analysis, to systematically reduce the complexity of the feature space and reveal sharp insights in the data to detect anomalies malfunctions.

Machine Learning as a Service (MLaaS)

Adlumin can build Machine Learning as a Service (MLaaS) applications covering a broad array of use cases, complementing the cloud computing resources already available within various F35 development environments by delivering turnkey solutions to numerous forecasting and estimation challenges.