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Going Beyond CloudWatch: 5 Steps to Better Log Analytics & Analysis

Amazon CloudWatch is a great tool for DevOps engineers, developers, SREs, and other IT personnel who require basic Amazon Web Services (AWS) log processing and analytics for cloud services and applications deployed on AWS. However, most developer teams will ultimately need more logging functionality than a basic AWS log analyzer like Amazon Cloudwatch can provide. For example: That’s why, although CloudWatch may be one tool in your log analytics strategy, it probably should not be the only one.
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How to Detect Threats to AI Systems with MITRE ATLAS Framework

Cyber threats against AI systems are on the rise, and today's AI developers need a robust approach to securing AI applications that address the unique vulnerabilities and attack patterns associated with AI systems and ML models deployed in production environments. In this blog, we're taking a closer look at two specific tools that AI developers can use to help detect cyber threats against AI systems.
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From Legacy to Future-proof: Transforming Your Enterprise Data Architecture

Enterprise data and analytics is a fast-evolving field in enterprise IT, where new technologies and solutions are creating revolutionary ways to extract insights from data. To keep pace with these changes and drive value creation through data analytics initiatives, organizations must be willing to adopt innovative solutions, embrace new and emerging best practices, and move beyond obsolete or outdated methods that are no longer effective. Our blog post this week is all about transforming your enterprise data architecture to elevate your data management and analytics capabilities.

5 Ways to Approach Data Analytics Optimization for Your Data Lake

While data lakes make it easy to store and analyze a wide variety of data types, they can become data swamps without the proper documentation and governance. Until you solve the biggest data lake challenges — tackling exponential big data growth, costs, and management complexity — efficient and reliable data analytics will remain out of reach.
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How MDR Services Can Optimize Threat Intelligence

Managed Detection and Response (MDR) services play a critical role in cybersecurity. These technologies remotely monitor, detect, and respond to threats, blending threat intelligence with human expertise to hunt down and neutralize potential risks. However, one of the biggest challenges MDRs face is managing the sheer volume and variety of threat intelligence data they receive. This data comes from internal resources and the numerous security technologies their customers use, making it difficult to create a cohesive picture of the threat landscape.

How to Get Started with a Security Data Lake

Modern SecOps teams use Security Information and Event Management (SIEM) software to aggregate security logs, detect anomalies, hunt for threats, and enable rapid incident response. While SIEMs enable accurate, near real-time detection of threats, today's SIEM solutions were never designed to handle the volume of security data organizations generate daily. As daily log ingestion grows, so do the costs of data management.

6 Threat Detection Challenges for MDRs and How to Overcome Them

Managed Detection and Response (MDR) is a cybersecurity service offered by a Managed Security Services Provider (MSSP) that combines human security expertise with modern security tools to deliver managed threat detection, security monitoring, and incident response capabilities for both SMBs and enterprise clients.

The Power of Combining a Modular Security Data Lake with an XDR

The 2024 Global Digital Trust Insights survey from PwC reports that 36% of businesses have experienced a data breach that cost more than $1 million to remediate. Cyber threats are clearly on the rise and in today’s volatile threat environment, it is a matter of when - not if - a cybersecurity incident will occur. Digital adversaries are becoming more sophisticated and relying on weak links to exploit company applications and infrastructure.
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Improving Patch and Vulnerability Management with Proactive Security Analysis

Vulnerability management is the continuous process of identifying and addressing vulnerabilities in an organization's IT infrastructure, while patch management is the process of accessing, testing, and installing patches that fix bugs or address known security vulnerabilities in software applications. Vulnerability management and patch management are crucial SecOps processes that protect IT assets against cyber threats and prevent unauthorized access to secure systems. Effectiveness in patch management and vulnerability management depends on a proactive approach to cybersecurity where enterprise SecOps teams take steps to anticipate and prevent cyber attacks before they happen.

Understanding Security Log Analytics vs. SIEM for Midsized Companies Targeted by Cybercriminals

SecOps teams at midsize companies face a unique set of challenges when it comes to managing organizational cybersecurity. Midsize companies (those with 100-999 employees and $50 million-$1 billion in annual revenue, according to Gartner) possess significant financial resources and valuable data that may be targeted by digital adversaries.