AIsaac and AWS Security Lake: when data lakes meet AI

Navigating the sea of security data: why organizations need data lakes

Imagine you’re a fisherman, casting your net into the vast ocean in search of fish. You never know what you’ll catch, but you hope to find something valuable that will help sustain you and your community. Managing security data is a bit like fishing – organizations cast their nets far and wide, hoping to catch valuable insights that will help them protect their systems and data.

But the sea of security data is vast and unpredictable. Organizations generate vast amounts of data from multiple sources, including servers, networks, applications, and devices. This data can be incredibly valuable for identifying security threats and vulnerabilities, but it can also be overwhelming and difficult to manage.

One of the biggest challenges of managing security data is the sheer volume of information generated. It’s like trying to catch fish in a vast ocean with a small net – there’s so much data coming in from so many sources that it can be difficult to identify and prioritize the most critical security events. In addition, security data is often spread across multiple systems and platforms, making it hard to get a comprehensive view of an organization’s security posture.

That’s where data lakes come in. A data lake is like a massive fishing pond that allows organizations to store, manage, and analyze large volumes of data from multiple sources. Instead of casting a small net into the vast ocean, organizations can cast a wide net into a single, centralized repository. With a data lake, organizations can centralize their security data and apply advanced analytics and machine learning algorithms to identify potential threats and vulnerabilities.

The big catch: what Amazon Security Lake brings to the security game

Security data is often spread across multiple platforms and services. Therefore, organizations are turning to data lakes, but how can they manage this in environments like AWS?

AWS launched on November 2022 the preview of its Amazon Security Lake, a powerful tool for centralizing security data from multiple sources into a purpose-built data lake that’s stored in the organization’s account. By aggregating data from cloud, on-premises, and custom sources, the Amazon Security Lake provides a more complete understanding of the organization’s security posture, making it easier to identify and respond to potential threats.

But to fully leverage this ocean of security data, it is key to apply advanced analytics and machine learning algorithms to the data. Only this way, will it be possible to identify potential threats and vulnerabilities before they become serious security breaches.

One key advantage of Amazon Security Lake is that it’s based on the Open Cybersecurity Schema Framework (OCSF), an open standard that allows for normalization and combination of security data from AWS and other enterprise security data sources. This means that Security Lake can work with a broad range of security data sources, providing a more comprehensive view of your security landscape.

Casting a net for security threats: the concrete application with Eviden AIsaac

Let’s take a closer look at five concrete use cases that we address with AIsaac, Eviden next-generation artificial intelligence (AI) platform, that leverages the Amazon Security Lake:

• Threat Intelligence: With Amazon Security Lake, we are able to ingest security log data from multiple sources, and provide a central location for threat intelligence feeds. By analyzing this data in real-time, we can help identify emerging threats and take proactive steps to prevent them from becoming serious security breaches.

• Security Monitoring: Our platform leverages Amazon Security Lake to perform advanced security analytics on endpoints, user behavior, applications, and networks. By centralizing this data into one repository, we’re able to provide deeper insights into potential security threats, helping organizations respond more quickly and effectively to security incidents.
• Threat Hunting: Our automated and manual threat hunting capabilities leverage the data in Amazon Security Lake to proactively search for in-progress attacks that may have evaded initial detection. By analyzing security data from multiple sources in real-time, we’re able to identify potential threats and take action to prevent them from causing serious harm to your organization.

• Incident Analysis: With Amazon Security Lake, we’re able to thoroughly investigate and decode security incidents to define all compromised assets and determine how to stop attacks at their root. By centralizing security data from multiple sources, we can provide a more complete understanding of the scope and impact of security incidents, helping organizations respond more quickly and effectively to minimize the damage caused by security breaches.

• Security Automation: AI and machine learning can be used to automate security tasks such as vulnerability scanning, patching, and threat response, leveraging the data in Amazon Security Lake. By automating routine tasks, we free up security teams to focus on more complex tasks, helping organizations stay ahead of emerging threats and better protect their assets.

Building a stronger security posture for the future

Eviden is committed to supporting a growing number of use cases on the AIsaac platform. Our goal is to provide organizations worldwide with exceptional value by integrating AIsaac with Amazon Security Lake to deliver Managed Detection and Response (MDR)/Extended Detection and Response (XDR) services.

By leveraging the power of AIsaac and Amazon Security Lake, organizations can take advantage of real-time threat detection and response capabilities, enabling them to stay ahead of evolving cyber threats. We remain dedicated to advancing our technology and providing world-class security solutions that meet the evolving needs of organizations.