Navigating the New Security Data Frontier: The Synergy of Databahn.ai, Amazon Security Lake, and OCSF

In recent months, we've witnessed a paradigm shift where security teams are increasingly opting to build their own security data lakes. This trend isn't entirely new, as attempts have been made in the past with cloud storage systems and data warehouse solutions. Previously, the challenges of integrating data from disparate sources, normalizing it, and ensuring consistent usage through enterprise-wide security data models were significant barriers. However, the landscape is changing as more security teams embrace the idea of crafting their own data lakes. This isn't just about creating a repository for data; it's the beginning of a modular security operations stack that offers unprecedented flexibility. This new approach allows teams to integrate various tools into their stack seamlessly, without the complexities of data access, normalization, or the limitations imposed by incompatible data formats.

Driving Forces Behind the Shift

One pivotal factor propelling this shift is the development of the Open Cybersecurity Schema Framework (OCSF). Initiated in August 2022, OCSF aims to standardize security data across various platforms and tools and is now powered by a consortium of over 660 contributors from 197 enterprises. This framework strives to eliminate data silos and establish a unified language for security telemetry, promoting easier integration of products and fostering collaboration within the cybersecurity community. Achieving these benefits on a broad scale, however, requires ongoing cooperation among all stakeholders involved in cybersecurity.

The adoption of OCSF's structured data hierarchy significantly enhances security operations by enabling seamless communication through standardized data formats, which eliminates the need for extensive data normalization. This standardization also accelerates threat detection by facilitating quicker correlation and analysis of security events. Additionally, it improves overall security operations by streamlining data exchange, enhancing team collaboration, and simplifying the implementation of security orchestration, automation, and response (SOAR) strategies.

The Emergence of Amazon Security Lake

In tandem with the rise of OCSF, solutions like Amazon Security Lake have come to the forefront, offering specialized capabilities that address the limitations often encountered with traditional cloud SIEM vendors, such as data lock-in and restricted tool integration flexibility or traditional cloud data warehouses/data lakes that were often general purpose lacking the right foundations of managing security data. Amazon Security Lake acts as a central repository for security data from multiple sources—be it AWS environments, SaaS providers, on-premises data centers, or other cloud platforms. By consolidating this data into a dedicated data lake within the user’s AWS account, it enables a holistic view of security data across the organization.

Integrating Amazon Security Lake with OCSF facilitates the normalization and amalgamation of this data, crucial for consistent and efficient analysis and monitoring. One of the standout features of Amazon Security Lake is its ability to centralize vast amounts of data into Amazon S3 buckets, allowing security teams to utilize their chosen analytics tools freely. This capability not only circumvents vendor lock-in but also empowers organizations to adapt their analytics tools as security needs evolve and new technologies emerge.

The Rise of Security Data Fabrics - DataBahn.ai

DataBahn.ai plays a crucial role in this synergy, offering its Security Data Fabric platform. The platform enables AWS customers with the flexibility to select from an array of OCSF-enabled tools and services that best meet their needs, without the hassle of manually reformatting data. This capability enables teams to analyze security data from endpoints, networks, applications, and cloud sources in a standardized format. Quick identification and response to security events are facilitated, empowering organizations with enhanced access controls, cost-efficient data storage, and regulatory compliance.

DataBahn simplifies the process of enriching and shaping raw data from third-party sources to meet the specifications of Amazon Security Lake's Parquet schema. This transformation is facilitated by a repeatable process that minimizes the need for modifications, making data integration seamless and efficient.

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Through DataBahn’s Security Data Fabric, Amazon Security Lake users can:

• Simplify data collection and ingestion into Amazon Security Lake: DataBahn’s plug-and-play integrations and connectors, along with its native streaming integration, allow for hassle-free, real-time data ingestion into Amazon Security Lake without the need for manual reformatting or coding.
• Convert logs into insights: Utilizing volume reduction functions like aggregation and suppression, DataBahn helps convert noisy logs (e.g., network traffic/flow) into manageable insights, which are then loaded into Amazon Security Lake to reduce query execution times.
• Increase overall data governance and quality: DataBahn identifies and isolates sensitive data sets in transit, thereby limiting exposure.
• Get visibility into the health of telemetry generation: The dynamic device inventory generated by DataBahn tracks devices to identify those that have gone silent, log outages, and detect any other upstream telemetry blind spots.

The greatest advantage of all is that it's your data, in your lake, formatted in OCSF, which allows you to layer any additional tools on top of this stack. This flexibility empowers your teams to achieve more and enhance your security posture.

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Conclusion: A Unified Security Data Management Approach

This shift towards a more unified and flexible approach to security data management not only streamlines operations but also enables security teams to focus on strategic initiatives. With the combined capabilities of Databahn.ai, Amazon Security Lake, and OCSF, organizations are better positioned to enhance their security posture while maintaining the agility needed to respond to emerging threats. As the cybersecurity landscape continues to evolve, we are at the cusp of a new wave of Security operations powered by tools that will play a crucial role in shaping a more integrated, efficient, and adaptive security data management framework.

Scaling Security Operations using Data Orchestration

Lately, there has been a surge in discussions through numerous articles and blogs emphasizing the importance of disentangling the processes of data collection and ingestion from the conventional SIEM (Security Information and Event Management) systems. Leading detection engineering teams within the industry are already adapting to this transformation. They are moving away from the conventional approach of considering security data ingestion, analytics (detection), and storage as a single, monolithic task.

Instead, they have opted to separate the facets of data collection and ingestion from the SIEM, granting them the freedom to expand their detection and threat-hunting capabilities within the platforms of their choice. This approach not only enhances flexibility to bring the best-of-breed technologies but also proves to be cost-effective, as it empowers them to bring in the most pertinent data for their security operations.

Staying ahead of threats requires innovative solutions. One such advancement is the emergence of next-generation data-focused orchestration platforms.

So, what is Security Data Orchestration?

Security data orchestration is a process or technology that involves the collection, normalization, and organization of data related to cybersecurity and information security. It aims to streamline the handling of security data from various sources, making it more accessible in destinations where the data is actionable for security professionals.
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Why is Security Data Orchestration becoming a big deal now?

Not too long ago, security teams adhered to a philosophy of sending every bit of data everywhere. During that era, the allure of extensive on-premise infrastructure was irresistible, and organizations justified the sustained costs over time. However, in the subsequent years, a paradigm shift occurred as the entire industry began to shift its gaze towards the cloud.

This transformative shift meant that all the entities downstream from data sources—such as SIEM (Security Information and Event Management) systems, UEBA (User and Entity Behavior Analytics), and Data Warehouses—all made their migration to the cloud. This marked the inception of a new era defined by subscription and licensing models that held data as a paramount factor in their quest to maximize profit margins.

In the contemporary landscape, most downstream products, without exception, revolve around the notion of data as a pivotal element. It's all about the data you ingest, the data you process, the data you store, and, not to be overlooked, the data you search in your quest for security and insights.

This paradigm shift has left many security teams grappling to extract the full value they deserve from these downstream systems. They frequently find themselves constrained by the limitations of their SIEMs, struggling to accommodate additional valuable data. Moreover, they often face challenges related to storage capacity and data retention, hindering their ability to run complex hunting scenarios or retrospectively delve deeper into their data for enhanced visibility and insights.

It's quite amusing, but also concerning, to note the significant volume of redundant data that accumulates when companies simply opt for vendor default audit configurations. Take a moment to examine your data for outbound traffic to Office 365 applications, corporate intranets, or routine process executions like Teams.exe or Zoom.exe.
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Sample data redundancy illustration with logs collected by these product types in your SIEM Upon inspection, you'll likely discover that within your SIEM, at least three distinct sources are capturing identical information within their respective logs. This level of data redundancy often flies under the radar, and it's a noteworthy issue that warrants attention. And quite simply, this hinders the value that your teams expect to see from the investments made in your SIEM and data warehouse.

Conversely, many security teams amass extensive datasets, but only a fraction of this data finds utility in the realms of threat detection, hunting, and investigations. Here's a snapshot of Active Directory (AD) events, categorized by their event IDs and the daily volume within SIEMs across four distinct organizations.
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It is evident that, despite AD audit logs being a staple in SIEM implementations, no two organizations exhibit identical log profiles or event volume trends.

Adhering solely to vendor default audit configurations often leads to several noteworthy issues:

1. Overwhelming Log Collection: In certain cases, such as Org 3, organizations end up amassing an astronomical number of logs from event IDs like EID 4658 or 4690, despite their detection teams rarely leveraging these logs for meaningful analysis.
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2. Redundant Event Collection: Org 4, for example, inadvertently collects redundant events, such as EID 5156, which are also gathered by their firewalls and endpoint systems. This redundancy complicates data management and adds little value.
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3. Blind spots: Standard vendor configurations may result in the omission of critical events, thereby creating security blind spots. These unmonitored areas leave organizations vulnerable to potential threats

On the other hand, it's vital to recognize that in today's multifaceted landscape, no single platform can serve as the definitive, all-encompassing detection system. Although there are numerous purpose-built detection systems painstakingly crafted for specific log types, customers often find themselves grappling with the harsh reality that they can't readily incorporate a multitude of best-of-breed platforms.

The formidable challenges emerge from the intricate intricacies of data acquisition, system management, and the prevalent issue of the ingestion layer being tightly coupled with their SIEMs. Frequently, data cascades into various systems from the SIEM, further compounding the complexity of the situation. The overwhelming burden, both in terms of cost and operational intricacies, can make the pursuit of best-of-breed solutions an impractical endeavor for many organizations.

Today’s SOC teams do not have the strength or capacity to look at each source that is logging to weed out these redundancies or address blind spots or take only the right and relevant data to expensive downstream systems like the SIEM or analytics platforms or even manage multiple data pipelines for multiple platforms.

This underscores the growing necessity for Security Data Orchestration, with an even more vital emphasis on Context-Aware Security Data Orchestration. The rationale is clear: we want the Security Engineering team to focus on security, not get bogged down in data operations.

So, how do you go about Security Data Orchestration?

In its simplest form, envision this layer as a sandwich, positioned neatly between your data sources and their respective destinations.
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The foundational principles of a Security Data Orchestration platform are -

Centralize your log collection:-  Gather all your security-related logs and data from various sources through a centralized collection layer. This consolidation simplifies data management and analysis, making it easier for downstream platforms to consume the data effectively.

Decouple data ingestion:- Separate the processes of data collection and data ingestion from the downstream systems like SIEMs. This decoupling provides flexibility and scalability, allowing you to fine-tune data ingestion without disrupting your entire security infrastructure.

Filter to send only what is relevant to your downstream system:- Implement intelligent data orchestration to filter and direct only the most pertinent and actionable data to your downstream systems. This not only streamlines cost management but also optimizes the performance of your downstream systems with remarkable efficiency.

Enter DataBahn

At databahn.ai, our mission is clear: to forge the path toward the next-generation Data Orchestration platform. We're dedicated to empowering our customers to seize control of their data but without the burden of relying on communities or embarking on the arduous journey of constructing complex Kafka clusters and writing intricate code to track data changes.

We are purpose-built for Security, our platform captures telemetry once, improves its quality and usability, and then distributes it to multiple destinations - streamlining cybersecurity operations and data analytics.

DataBahn seamlessly ingests data from multiple feeds, aggregates compresses, reduces, and intelligently routes it. With advanced capabilities, it standardizes, enriches, correlates, and normalizes the data before transferring a comprehensive time-series dataset to your data lake, SIEM, UEBA, AI/ML, or any downstream platform.
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DataBahn offers continuous ML and AI-powered insights and recommendations on the data collected to unlock maximum visibility and ROI. Our platform natively comes with

• Out-of-the-box connectors and integrations:- DataBahn offers effortless integration and plug-and-play connectivity with a wide array of products and devices, allowing SOCs to swiftly adapt to new data sources.
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• Threat Research Enabled Filtering Rules:- Pre-configured filtering rules, underpinned by comprehensive threat research, guarantee a minimum volume reduction of 35%, enhancing data relevance for analysis.
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• Enrichment support against Multiple Contexts:- DataBahn enriches data against various contexts including Threat Intelligence, User, Asset, and Geo-location, providing a contextualized view of the data for precise threat identification.
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• Format Conversion and Schema Monitoring:- The platform supports seamless conversion into popular data formats like CIM, OCSF, CEF, and others, facilitating faster downstream onboarding. It intelligently monitors log schema changes for proactive adaptability.
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• Schema Drift Detection:- Detect changes to log schema intelligently for proactive adaptability.
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• Sensitive data detection:- Identify, isolate, and mask sensitive data ensuring data security and compliance.
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• Continuous Support for New Event Types:- DataBahn provides continuous support for new and unparsed event types, ensuring consistent data processing and adaptability to evolving data sources.
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Data orchestration revolutionizes the traditional cybersecurity data architecture by efficiently collecting, normalizing, and enriching data from diverse sources, ensuring that only relevant and purposeful data reaches detection and hunting platforms. Data Orchestration is the next big evolution in cybersecurity, that gives Security teams both control and flexibility simultaneously, with agility and cost-efficiency.

The Ultimate Guide to Microsoft Sentinel optimization for Enterprises

Are you struggling with inflating costs and increased time and effort in managing Microsoft Sentinel for your business? Is optimizing data ingestion cost, improving operational efficiency, and saving your team’s time and effort important for your business? With ~13% of the SIEM market according to industry sources, many enterprises across the world are looking for ways to unlock the full potential of this powerful platform.
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What is Microsoft Sentinel?

Microsoft Sentinel (formerly known as “Azure Sentinel”) is a popular and scalable cloud-native next-generation security information and event management (“SIEM”) solution and a security orchestration, automation, and response (“SOAR”) platform. It combines a graphical user interface, a comprehensive analytics package, and advanced ML-based functions that help security analysts detect, track, and resolve cybersecurity threats faster.

It delivers a real-time overview of your security information and data movement across your enterprise, providing enhanced cyberthreat detection, investigation, response, and proactive hunting capabilities. Microsoft Sentinel natively incorporates with Microsoft Azure services and is a popular SIEM solution deployed by enterprises using Microsoft Azure cloud solutions.

Find out how using DataBahn’s data orchestration can help your Sentinel deployment – download our solution brief here.         DOWNLOAD  

Text Microsoft Sentinel is deployed by companies to manage increasingly sophisticated attacks and threats, the rapid growth of data volumes in alerts, and the long timeframe for resolution.
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What is the Microsoft Sentinel advantage?

The four pillars of Microsoft Sentinel

Microsoft Sentinel is built around four pillars to protect your data and IT systems from threats: scalable data collection, enhanced threat detection, AI-based threat investigations, and rapid incident response.

Scalable data collection

Microsoft Sentinel enables multi-source data collection from devices, security sensors, and apps at cloud scale. It allows security teams to create per-user profiles to track and manage activity across the network with customizable policies, access, and app permissions. This enables single-point end-user management and can be used for end-user app testing or test environment with user-connected virtual devices.

Enhanced threat detection

Microsoft Sentinel leverages advanced ML algorithms to search the data going through your systems to identify and detect potential threats. It does this through “anomaly detection” to flag abnormal behavior across users, applications, or app activity patterns. With real-time analytics rules and queries being run every minute, and its “Fusion” correlation engine, it significantly reduces false positives and finds advanced and persistent threats that are otherwise very difficult to detect.

AI-based threat investigations

Microsoft Sentinel delivers a complete and comprehensive security incident investigation and management platform. It maintains a complete and constantly updated case file for every security threat, which are called “Incidents”. The Incidents page in Microsoft Sentinel increases the efficiency of security teams and offers automation rules to perform basic triage on new incidents and assign them to proper personnel, and syncs with Microsoft Defender XDR for simplified and consistent threat documentation.

Rapid incident response

The incident response feature in Microsoft Sentinel helps enterprises respond to incidents faster and increases their ability to investigate malicious activity by up to 50%. It creates advanced reports that make incident investigations easier, and also enables response automations in the form of Playbooks, which are collections of response and remediation actions and logics that are run from Sentinel as a routine.
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Benefits of Microsoft Sentinel

Implementing Microsoft Sentinel for your enterprise has the following benefits:

• Faster threat detection and remediation, reducing the mean time to respond (MTTR)
• Improved visibility into the origins of threats, and stronger capability for isolating and stopping threats
• Intelligent reporting that drives better and faster incident responses to improve outcomes
• Security automation through analytics rules and automations to allow faster data access
• Analytics and visualization tools to understand and analyze network data
• Flexible and scalable architecture
• Real-time incident management
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What is Microsoft Sentinel Optimization?

Microsoft Sentinel Optimization is the process of fine-tuning the powerful platform to reduce ingestion costs, improve operational efficiency, and enhancing the overall efficiency, cost-effectiveness, and efficacy of an organization’s cybersecurity team and operations. It addresses how you can manage the solution to ensure optimal performance and security effectiveness while reducing costs and enhancing data visibility, observance, and governance. It involves configuration changes, automated workflows, and use-case driven customizations that help businesses and enterprises get the most value out of the use of Microsoft Sentinel.
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Why Optimize your Microsoft Sentinel platform?

Despite the reduction in costs compared to legacy SIEM solutions, Microsoft Sentinel’s cost reduction in data ingestion is still subject to the incredible increase in security data and log volumes. With the volume of data being handled by enterprise security teams growing by more than 20% year-on-year, security and IT teams are finding it difficult to find critical data and information in their systems as mission-critical data is lost in the noise.

Additionally, the explosion in security data volumes also has an impact in terms of costs – SIEM API costs, storage costs, and the effort of managing and routing the data makes it difficult for security teams to allocate bandwidth and budgets to strategic projects.

With proper optimization, you can:

• Make it faster and easier for security analysts to detect and respond to threats in real-time
• Prioritize legitimate threats and incidents by reducing false positives
• Secure your data and systems from cyberattacks more effectively
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Benefits of using DataBahn for optimizing Sentinel

Using DataBahn’s Security Data Fabric enables you to improve Microsoft Sentinel ingest to ensure maximum value. Here’s what you can expect:

• Faster onboarding of sources: With effortless integration and plug-and-play connectivity with a wide array of products and services, SOCs can swiftly integrate with and adapt to new sources of data
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• Resilient Data Collection: Avoid single-point of failures, ensure reliable and consistent ingestion, and manage occasional data volume bursts with DataBahn’s secure mesh architecture
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• Text BoxReduced Costs: DataBahn enables your team to manage the overall costs of your Sentinel deployment by providing a library of purpose-built volume reduction rules that can weed out and less relevant logs.

Find out how DataBahn helped a US Cybersecurity firm save 38% of your SIEM licensing costs in just 2 weeks on their Sentinel deployment.   DOWNLOAD  

Why choose DataBahn for your Sentinel optimization?

Optimizing Microsoft Sentinel requires extensive time and effort from your infrastructure and security teams. Some aspects of the platform also ensure that there will continue to be a requirement to allocate additional bandwidth (integrating new sources, transforming data from different destinations, etc.).

By partnering with DataBahn, you can benefit from DataBahn’s Security Data Fabric platform to create a future-ready security stack that will ensure peak performance and complete optimization of cost while maximizing effectiveness.

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