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.

Cloud security refers to technologies, best practices, and safety guidelines that help to protect your data from human errors, insider and security threats. Therefore, it naturally covers a wide range of procedures, which are aimed at securing systems from data breaches, data loss, unauthorized access, and other cybersecurity-related risks that are growing from year to year.

In today’s hyper-connected digital landscape, APIs are the lifeblood of innovation, powering everything from customer experiences to internal operations. However, with this growing reliance on APIs comes a dark side—zombie and shadow APIs. These hidden, forgotten, or undocumented endpoints present significant security risks that traditional approaches simply can’t address.

We are lucky enough to be living through the golden age of podcasts. Whatever subject you’re into, you can bet there’s someone out there talking into a microphone about it, and cybersecurity is no exception.

In early 2022, a Yahoo employee, Qian Sang, exploited his access to confidential information, and stole the company’s AdLearn product minutes after receiving a job offer from a competitor. By the time the breach was discovered, the damage was extensive, costing the company millions in fines, legal fees, and lost business . This incident is not an isolated case.

While external cyberattacks often make the headlines, internal security risks (aka insider risks) present a significant danger that is sometimes underestimated. These risks can arise from disgruntled employees, negligent insiders, or malicious actors with privileged access. The repercussions of such breaches can be severe, resulting in data loss, financial harm, legal fines, and harm to reputation.

Estimating the potential impact of a successful cyber attack may seem impossible, especially given the rapid expansion of organizations’ digital footprint (and, consequently, their attack surface). One example are attacks which pertain to the contact points between businesses and clients, such as websites and mobile apps. In particular, these assets can be cloned and used for phishing attacks.

A new strain of Android malware has emerged, targeting victims' card details and utilizing near-field communication (NFC) technology to facilitate unauthorized ATM withdrawals. This sophisticated crimeware, active since March 2024, has already impacted customers of three major Czech banks.

Enterprises invest heavily in cybersecurity measures to protect their critical assets and sensitive data. According to the Worldwide Security Spending Guide published by International Data Corporation (IDC), European security spending will grow by 12.3% in 2024, similar trajectory to the US and Asia Pacific. Despite these investments, crippling vulnerabilities continue to wreak havoc, and the costs of cyber attacks continue to soar.

As language models (LLMs) in enterprise applications continue to grow, ensuring data privacy while maintaining response accuracy becomes crucial. One of the primary methods for protecting sensitive information is data masking. However, this process can lead to significant information loss, potentially rendering responses from LLMs less accurate. How can this loss be measured?