In order to integrate information from multiple sources and improve business outcomes, data lakes nowadays are very effective in this role, when combined with analysis and automation tools. To counter increasingly complex cyber threats, data lakes are essential to increase visibility, reduce workload, and provide business stakeholders with state-of-the-art solutions to thwart these threats.
Cloud data platforms enable multiple data consolidation
The growth in the use of the cloud and connected objects, along with the popularization of remote work, has increased the volume, diversity, and complexity of security data. It turns out that traditional security tools largely run on internal company data. However, even if organizations are equipped with solutions designed to integrate records from multiple sources, silos are inevitable. In fact, all too often, the cost of fully absorbing and maintaining this data is prohibitive. This fact only complicates the task of the security teams, already exhausted, which explains why they struggle to get the necessary results in terms of prevention, detection and response.
Fortunately, developments in cloud data systems are making data integration at scale much easier and more cost-effective. Realizing the potential of these solutions, many security teams have transformed their company’s central data platform into a place to consolidate all security data, regardless of size. This approach standardizes data records that are often in terabytes or petabytes and that come from multiple sources, such as firewalls, endpoint agents, and cloud infrastructure.
Data turns into useful information
In addition to storage, modern data platforms include languages such as SQL and Python that enable rapid analysis, transforming consolidated data into actionable insights, almost instantaneously. As a result, there are fewer and fewer false positives, which saves time. No more slow and error-prone manual investigations of potential violations. A powerful cycle has been launched: Security teams can easily add new data sets from your IT department and the rest of the business. Best of all, they help provide more context, further reducing false positives and workload.
Security data lakes, as implemented on modern cloud platforms, are well suited for using machine learning and any other advanced analytics tools. With security data hosted on the enterprise’s primary cloud platform, security and analytics teams can work side by side. Together, they can solve problems and develop solutions. They just need to apply the latest technology to better detect anomalies and automate processes. In this way, the security manager is completely aligned with the IT department, working from the same advanced data set. The benefits of this method are not limited to detecting threats. When data is standardized, shared, and accessed, risk management, compliance, identities, access, and vulnerabilities are also facilitated. Another strength is using existing business intelligence tools to generate reports that show executives how the cyber situation is improving. These documents can also be used to encourage stakeholders from other departments to take action.
The future of cybersecurity lies in data lakes
Innovative security vendors have understood all of these benefits. That’s why they support security teams whose data resides on the enterprise data platform. For example, Hunters, Panther Labs, and Securonix now offer cybersecurity solutions that connect to a customer’s security data lake, in a so-called “connected application” model. This is likely to become the norm. For security solutions and platforms that share a data-driven approach.
To turn the tide of the battle against cybercriminals, it is essential to standardize security data and maintain its unlimited availability. As the SolarWinds hack showed, responders must be equipped to investigate again for more than a year. In fact, in response to this and other breaches, the US government said it will require federal agencies to expand event retention and publish behavioral analyzes that can help mitigate future cyberattacks. This standard should be applied to all organizations concerned with security.
Thus, cloud-based security data lakes appear to be an efficient and profitable asset in the face of the increase in security data processing needs. As a result, complex analytics can be applied to ever-increasing volumes of data. In the face of the actions companies have taken for IT security, these platforms seem essential.