Graph technology can help examine your data from a new perspective. Find out how graph technology may find previously unseen relationships in your data.
What Is Graph Technology?
Graph technology includes graph theory, graph analytics, and graph data management. Graph technology has a different perspective than most existing solutions by mapping complex datasets.
Graph technology is reaching an inflection point driven by modern data and data-driven business operations. This is primarily due to the volume, velocity, and complexity surrounding the countless structured and unstructured data emanating from disparate sources.
Graph technology makes it highly efficient to find crucial relationships and patterns in this ocean of diverse data.
What is Graph Theory? What Are Its Implications For The Cloud?
Graph technology is based on graph theory. Graph theory is simply a mathematical theory based on the properties, attributes, and applications of graphs. It is a network topology based on nodes and/or edges linked by branches.
Graph properties are immensely useful when it comes to deciphering and using the information they contain to gain insight into the network. In addition to helping find information, topological properties also help identify pertinent sub-structures within the network.
Recently, there has been a surge in interest regarding the analysis of networks, especially as it pertains to cloud infrastructures. This shouldn’t come as a surprise since when you think about an IT network, what most readily comes to mind is a graph.
Moreover, graph models are used to analyze and understand similar information and communication networks. The modeling of cloud systems based on graphs helps to highlight and clarify security problems with the aim of cloud risk management.
We have previously discussed how creating graphs is necessary for the cloud mapping process that assesses pertinent relationships between cloud assets. So, this graph-based approach has become highly relevant for today’s cloud security.
The Importance of Graph Technology To Cloud and Network Environments
The importance of graph technology has grown with the proliferation of numerical data in the modern age.
Graph technology positions companies to garner more in-depth information about their platforms through the insight generated by graph databases. It provides businesses with the ability to leverage the voluminous data at their disposal and use it to reveal patterns and deeper insights.
By leveraging the power of graph databases, graph technologies provide the ability to create and map relationships with dynamic assets, while visually depicting their interdependencies.
Graph technology use cases are highly pertinent to cloud security. Together with graph theory, it is used in cloud security to model attack vectors and the potential attack paths adversaries can use to exploit cybersecurity defenses.
Below are some other graph technology use cases that impact both computing and cloud environments.
Data and Graph Analytics
Graph technology is becoming increasingly important in data analytics because traditional methods have proven inadequate to address the complexity and growth in data volume. Moreover, with the copious amounts of data now available, business executives have been compelled to ask increasingly complex questions regarding the structured and unstructured data at their disposal.
However, although traditional relational database management systems (RDBMS) have grown in popularity, they weren’t designed to effectively cope with unstructured and disjointed data.
RDBMS use primary and foreign key relationships to map information in tabulated data. However, graph databases prioritize the relationships between data points. It uses nodes as primary entities that can represent a wide array of information while edges capture the relationships between nodes. As a result of these attributes, graph databases, unlike RDBMS, present us with an easy way to understand unstructured data.
In addition to its ability to store huge datasets, graph technology is uniquely suited to exploit the interlocking connections between unstructured data by creating a network of useful data by connecting unstructured data points.
Although traditional databases such as RDBMS won’t be wholly replaced, graph databases are poised to become the default database for enterprise applications, especially requiring scalable graph technology implementations.
Intelligent Graph Database to Power Graph Analytics
Analyzing data at scale requires native graph database like Neo4j to power intelligent applications. Such graph databases highlight correlations, outliers, and complicated relationships in social media, fraud detection, and AI that would otherwise have been impossible to discern with traditional databases.
Neo4j is equipped with a library of algorithms, most notably, the Neo4j Graph Data Science Library (GDS) library, which encompasses machine learning models for node classifications and other tree algorithms.
Artificial Intelligence and Machine Learning
Graph technologies improve the modeling speed and accuracy of AI solutions. This is because knowledge graph technologies are often modeled after AI and machine learning algorithms. As a result, knowledge graphs are increasingly powering artificial intelligence apps.
We see its application every day when executing keyword queries on search engines or intelligent devices such as recommendation engines or voice assistants. By using graph databases and AI algorithms, these apps combine disparate silos of data to facilitate intuitive searches.
Software Development and Data Management
Graph technology and data analytics have been shifting the landscape with regard to data governance and software management. Even with the emergence of DevOps methodology, graph technology allows development teams to focus on creating innovative applications instead of managing infrastructure.
Graph technology also makes augmented data management possible. This peculiar data management leverages ML and AI techniques to improve operations through its capability to handle large amounts of operational data. Graph technology is a driving force behind the ability of an augmented engine to fine-tune and optimize operations in configuration, security, and performance.
The complex nature of contemporary cloud environments requires nothing less than graph theory to capture its dynamic, fast-paced architectural changes.
The cybersecurity industry has continued to evolve moving towards paradigm shifts like zero-trust architecture in response to an escalation in attacks. However, while cybersecurity is rightfully focused on protecting valuable assets, security relationships have often been overlooked.
Incidentally, hackers take advantage of these connections. For instance, once hackers gain a foothold through spearphishing attacks, they capitalize on these connections to infiltrate and burrow deeper into the network.
Graph technology provides your InfoSec team with a deep understanding of all your interlinked assets so they can proactively prevent cyber threats and downgrade the capabilities of malicious actors.
How to Apply Knowledge Graphs to Public Cloud Security
Graphs are invaluable to security teams due to their ability to provide a detailed and three-dimensional overview of cybersecurity infrastructure and attack paths. In so doing, they illuminate security issues that exist and how these can impact the cloud network. The use of graph databases to create graphs of activity and behavior is especially useful to uncover anomalous behavioral patterns.
These are some of the ways that graph technology brings value to cloud security.
Quickly Identify Weakness in Deployments and Configurations
Graph technology is a significant resource for cybersecurity experts for various reasons, primarily due to its ability to discover weaknesses in cloud deployments spanning different platforms, technologies, and vendors.
Graphs make it possible to detect anomalies in real-time, for instance by connecting cloud security assessments with static code analysis. These cloud-based graph frameworks identify weaknesses in cloud deployments by contextualizing runtime information, for example, encryption settings, with data flows and an ontology of cloud resources.
Build a Conceptualized Security Graph
To be effective, cloud security needs to embrace a layered security approach. This is essential because understanding risks, especially in cloud environments, requires analyzing relationships. The security graph leverages graphs to depict assets that need protecting, but more importantly, the relationships between these assets including the roles they play within the organization.
Using graphs, the relationships between these layers can be modeled and analyzed to provide a well-rounded, holistic view of security while providing improved performance through contextual model optimization.
Graph technology makes it possible to take advantage of interlinked assets in your cloud environment to enhance security. As a data model, graphs can be applied to cloud security by virtue of their ability to map networks of cloud entities across multi-cloud infrastructures.
Because graph technology makes it possible to understand the complex relationships between these entities, DevSecOps are able to derive relevant knowledge from the diversity of security issues impacted by them.
Contextualize Knowledge in Cloud Networks
Through knowledge graphs and graph technology, DevSecOps develop a better cloud security context to evaluate risk. For instance, graphs can be contextualized with runtime information to boost their support for automated reasoning. What’s more, graphs also add more explainability to attack path modeling and neural networks.
Learn How Lightspin Uses Graph Technology to Map Cloud Architecture to Uncover Critical Attack Paths Across Multi-Cloud Environments
Attackers are becoming more agile and sophisticated, so it's a constant battle to prevent them from prevailing, especially in cloud environments which typically have a large surface of attack.
Lightspin uses a graph-based approach to boost cybersecurity through its competencies in contextualization and continuous mapping of complex cloud environments. Visibility is provided through a well-defined relationship explicitly highlighting linked assets in the cloud structure while comprehensive cloud risk management is achieved through the identification and mitigation of risks presented by critical attack paths.
To learn more, try our free demo today.