Introduction

Data fabrics are cohesive data layers that bridge data sources with data consumers, including analytics platforms such as SIEMs. They automate tasks like data ingestion, integration, and curation across diverse data sources, improving the agility and responsiveness of data ecosystems. More specifically, a security data fabric adds additional capabilities, including governance and compliance, security enrichment, and the integration of security events. 

In this blog, we will look at the most common features of security data fabrics and show how Observo AI’s telemetry pipeline for security data can handle all of these. Using the unique capabilities of the AI-powered telemetry pipeline for security data can be a great alternative for organizations looking to implement a security data fabric.

‍

Major Elements of a Security Data Fabric

Data Ingestion: Data ingestion in a security data fabric involves the automated collection and streaming of security event data from various sources such as servers, cloud environments, networks, applications, and agents. It ensures the real-time or batch processing of raw security data, which is crucial for detecting threats and managing security incidents efficiently. Observo AI can ingest and process data from a wide range of data sources including all of those mentioned above. Observo AI can also add new or custom data sources like custom application logs very easily using our large language machine learning models which can be trained to interpret new data sources very quickly. We create custom transforms that can translate all of these diverse sources into the schema of any of the multiple data consumers you may need. These transforms are automated, so you don’t have to painstakingly write a long list of rules that may soon become out-of-date.

Data Integration: Data integration refers to the ability of the security data fabric to unify disparate security data streams into a cohesive, centralized framework. This capability allows for seamless correlation of data from different security tools, providing comprehensive visibility into an organization's security posture. Observo AI helps customers create a security data lake by converting all sources into a common format, storing it in inexpensive cloud object storage, like AWS S3, Azure Blob, or Google Cloud Storage. Observo AI transforms log data into Parquet format, a highly compressible data format that allows search using natural language queries through tools like Athena. Using this method to create a security data lake ensures all downstream data consumers can analyze data on-demand. With Observo AI, you can query this unified source of security data with natural language, collect what you need, and route it to the best analytics tool in the right format.

Data Curation: Data curation involves the transformation, filtering, and normalization of security data within the fabric. By organizing and refining raw data, security teams can improve the quality and relevance of the information before it’s analyzed, reducing noise and enhancing the accuracy of insights for threat detection. Over 80% of all security data is noise and provides nothing of value from an analytical perspective. This noise includes null values, header fields, duplicate data, and just plain normal data that doesn’t tell your security team anything interesting.  Observo AI curates a much smaller set of data that is relevant to the types of analysis your team conducts to secure your enterprise. We do this by eliminating all of the low-value data, and summarizing all of the normal data, such as firewall events where a transaction was allowed by the firewall. Summarization aggregates many of these boring, “normal” events into a single event for massive data reduction. Observo AI further curates security data by tagging events with positive or negative sentiment. We’ll cover this in more detail in the next section.

Enrichment of Security Data: Enrichment involves adding context to raw security event data by incorporating external sources. This allows security teams to prioritize and respond more effectively to threats by providing deeper insights into the nature of security incidents. Observo AI can enrich security events in a couple of ways. The first is by enriching each log with third-party data from sources like threat intelligence databases or Geo-IP which can give more context security events. Observo AI also performs anomaly detection in the data stream to determine if an event has positive or negative sentiment. Events with negative sentiment may resemble those known to be associated with a threat or may be so different from the other events that they warrant special attention. Shifting AI left into the data stream allows your SecOps team to prioritize the most important events. Our customers report a 40% reduction in the time to resolve critical incidents - by focusing on critical items first, your team can solve issues before they spiral into costly problems.

Governance and Compliance: Governance and compliance in a security data fabric ensure that security data is managed in line with regulatory and policy requirements. This capability includes enforcing data privacy standards, access controls, and auditability, enabling organizations to maintain compliance with laws like GDPR, HIPAA, or PCI-DSS. Observo AI detects sensitive data allowing you to secure it through obfuscation or hashing. Unlike static tools that set rules for what is sensitive data, Observo’s ML models use pattern recognition to discover all sensitive data, even if it’s in an unexpected field or metric. Observo AI automates compliance with privacy regulations like GDPR, CCPA, and PCI. Observo AI helps you keep all sensitive data safe and protected. Many industries, such as banking, require security data to be archived for as many as seven years. As we discussed earlier, by creating a security data lake, in a unified format, your security archive is a natural query away and satisfies most of these requirements. Observo AI enables you to create a security data lake that is inexpensive enough to keep data as long as you require.

Wrapping up

Implementing a security data fabric is essential for organizations looking to streamline their security data management processes and enhance their security posture. By automating tasks like data ingestion, integration, curation, enrichment, and ensuring governance and compliance, a security data fabric empowers security teams with more efficient, actionable insights. Observo AI's telemetry pipeline for security data stands out as a robust solution, offering advanced capabilities like machine learning-based data ingestion, natural language query support, automated enrichment, and compliance automation.

By leveraging Observo AI, organizations can reduce noise, enrich data, and gain meaningful insights faster, ultimately improving threat detection and incident response times. For businesses seeking an all-encompassing solution to manage their security data pipeline, Observo AI’s unique approach can be a game-changer in building a highly responsive and secure data fabric that aligns with today’s demanding security requirements.

‍Schedule your demo today to learn how Observo AI can help secure your enterprise.