Verv IAM Identity Federation

Whether users are local or spread across the world, they must connect quickly and reliably.

The challenge is to secure the resources from the network point-of-presence to the end user locally or globally over the public internet, without exposing any private data. Verv IAM Cybersecurity Capabilities.

Verv IAM offers a range of cyber security monitoring solutions for internet access, work from home, multi-cloud network connections to and from enterprise data centers - custom solutions integrating existing logging and monitoring. Automated Logging and Monitoring Services include: - Network Layer Access, Authentication Gateway, Web Application Firewall, Developer and Administrator Access, Anomaly Reporting, Alerts and Alarms. Best practice Security Reference Architecture contains specifications for the key deliverables. Verv IAM Cloud Infrastructure Security Architecture

Today, public cloud service networks provide optimised performance to access protected resources via global private networks. These advances enable a new level of automation and artificial intelligence of transport, agribusiness, manufacturing and energy distribution to name a few, at a fraction of past private network costs. However, the last mile from the private network to the end user is still either a porous VPN or the public internet. And this is where the data breaches are occurring.

Security Monitoring, Logging, SIEM, Endpoint Prevention and Detection, and SOAR (Security Orchestration Automated Response) technology dashboards are based on machine learning and AI capabilities from collected data to identify potential and actual attacks. AI models use analysis of past events as training for detection and prevention models. As collected data accelerates, major refinements will become available over the coming decade. Operational logs have to be carefully analyzed for insight capabilities for both streaming and time series data. Verv IAM offers semi automated CSMS with the potential for training anomaly detection over time with sufficient data from particular Customer Use Cases.

SOAR Capabilities

1. Anomaly Detection: - Statistical Techniques: SOAR platforms often use statistical methods such as time-series analysis, clustering, and probability distributions to detect anomalies in network traffic, system logs, user behavior, and other security-related data. These techniques help identify deviations from normal patterns that may indicate potential security threats. - Neural Techniques: Neural network models, including recurrent neural networks (RNNs), convolutional neural networks (CNNs), and autoencoders, can be trained on large volumes of data to learn normal behavior and detect anomalies. These models can capture complex patterns and relationships in data, making them effective for anomaly detection in cybersecurity.
2. Threat Intelligence Analysis: - Statistical Techniques: Statistical analysis techniques, such as frequency analysis, correlation analysis, and trend analysis, are used to analyze threat intelligence feeds, security alerts, and incident data to identify trends, patterns, and correlations indicative of cyber threats. - Neural Techniques: Neural network models can be trained on threat intelligence data to identify emerging threats, classify malicious URLs, analyze malware characteristics, and categorize threat actor behaviors. Deep learning models, such as recurrent neural networks (RNNs) and transformers, are particularly effective for processing unstructured threat data, such as text and images.
3. Behavioral Analysis: - Statistical Techniques: Behavioral analysis techniques, such as clustering, sequence analysis, and Markov models, are used to analyze user behavior, network traffic, and system activity to detect anomalous or malicious behavior. - Neural Techniques: Deep learning models, such as recurrent neural networks (RNNs) and long short-term memory networks (LSTMs), are commonly used for sequence modeling and time-series analysis in cybersecurity. These models can capture temporal dependencies and identify subtle patterns indicative of malicious behavior.
4. Natural Language Processing (NLP): - Statistical Techniques: NLP techniques, such as sentiment analysis, named entity recognition, and topic modeling, are used to analyze unstructured text data from sources such as security incident reports, threat intelligence feeds, and social media platforms. - Neural Techniques: Deep learning models, such as transformers and recurrent neural networks (RNNs), are used for various NLP tasks in cybersecurity, including document classification, information extraction, and threat hunting. Pre-trained language models like BERT and GPT are often fine-tuned on cybersecurity data for specific tasks.
5. Predictive Analytics: - Statistical Techniques: Predictive analytics techniques, such as regression analysis, time-series forecasting, and decision trees, are used to predict future security events, identify potential vulnerabilities, and prioritize security incidents. - Neural Techniques: Neural network models, such as recurrent neural networks (RNNs) and deep neural networks (DNNs), can be trained on historical security data to make predictions about future security events, such as cyber attacks, data breaches, and system compromises.