In cybersecurity, it’s a big challenge to handle diverse data formats across various platforms. The Open Cybersecurity Schema Framework (OCSF) aims to address this by standardizing data security formats and simplifying the process of threat hunting. Major players like IBM, AWS and others are working together to standardize data with this open-source project, emphasizing its importance.

Establishing a proactive security posture involves a data-driven approach to threat detection, investigation, and response. In the past, this was challenging because there wasn’t a centralized way to collect and analyze security data across sources, but with Amazon Security Lake it is much simpler.

Developing a proactive security strategy can potentially save an organization millions of dollars per year. According to IBM, the average cost of a data breach in 2023 added up to a staggering $4.45 million, up 15% over the last three years. This is especially true for cloud-native environments, which face unique security challenges due to their dynamic nature.

Establishing an effective cyber threat hunting program is among the top priorities of enterprise security leaders seeking a proactive approach to detecting and counteracting potential threats. Furthermore, implementing a proactive threat hunting program, security teams that leverage formalized frameworks or threat hunting methodologies are far more likely to detect vulnerabilities or in-process malicious activities in their environments than those that do not.

Today’s enterprise networks are diverse and complex. Rather than the simple network perimeter of old, bad actors can attack through multiple entry points, including cloud-based applications. Not to mention, these networks generate massive amounts of transactional data. Because enterprise networks have become larger, they’re more difficult to secure and manage.

A SIEM system (Security Information and Event Management) is often used by security operations centers (SOCs) for real-time detection of suspicious activity and security events. While some teams choose to adopt a purpose-built SIEM, others rely on the same DevOps tools they are already using for tasks like troubleshooting and operational log data analysis.

Data retention policy will become a major focus for CIOs in 2021. Here’s why: First, enterprise organizations are producing larger volumes of data than ever before and utilizing enterprise data across a wider range of business processes and applications. To maximize its value, this data must be managed effectively throughout its entire life cycle - from collection and storage, through to usage, archiving, and eventually deletion.

This year, Amazon Web Services (AWS), a leading cloud services provider, announced a comprehensive security solution called Amazon Security Lake. In this blog post, we will explore what Amazon Security Lake is, how it works, the benefits for organizations, and partners you can leverage alongside it to enhance security analytics and quickly respond to security events. Image source: Amazon.

Amazon Security Lake is a new service from Amazon Web Services (AWS) that is designed to help organizations improve their security posture by automating the collection, normalization, and consolidation of security-related log and event data from integrated AWS services and third-party services (Source Partners). By centralizing all the security data in a single location, organizations can gain greater visibility and identify potential threats more quickly.

As fraud continues to grow in prevalence, SecOps teams are increasingly investing in fraud prevention capabilities to protect themselves and their customers. One approach that’s proved reliable is the use of log analytics and telemetry data for fraud prevention. By collecting and analyzing data from various sources, including server logs, network traffic, and user behavior, enterprise SecOps teams can identify patterns and anomalies in real time that may indicate fraudulent activity.