Eavesdropping, or passive surveillance, is a clandestine method that involves intercepting and monitoring communications without the knowledge or consent of those involved. This silent threat can pose significant risks to businesses, particularly those with confidential data and mission-critical operations.
In today’s digital age, voice assistants have become integral to our daily business. These virtual assistants offer convenience and efficiency, from controlling smart homes to providing information and entertainment. However, as with any technology, voice assistants have vulnerabilities. Cybercriminals have recognised the potential of exploiting voice assistants to target high-profile individuals, including C-Suite executives, for financial gain, reputational damage, and competitive advantage.
Voice assistant exploitation refers to cyberattacks targeting voice-activated virtual assistants, Amazon Alexa, Google Home, or Apple Siri.
In today’s cyber age, biometric authentication has emerged as an easy way to verify ID. By leveraging unique human characteristics such as Iris, palm prints, and facial patterns, it can provide a more authentic alternative to traditional passphrase-based authentication. However, as biometric technology advances, so do its associated threats. One of the biometric systems’ most significant challenges is the risk of spoofing or presentation attacks.
Biometric or presentation spoofing involves using fake biometric details to double-cross the authentication systems. By presenting a counterfeit biometric sample, an attacker can evade security measures and gain unauthorised access to sensitive information or resources. The prevalence of biometric spoofing has increased in recent years, making it a critical concern for organisations of all sizes, particularly those that rely heavily on biometric technology for security.
In today’s data-driven world, machine learning (ML) has become an indispensable tech for businesses across various industries. From fraud detection to customer segmentation, ML algorithms extract valuable insights and make informed decisions. However, the increasing reliance on ML systems has also made them a prime target for malicious actors. Adversarial machine learning attacks exploit the vulnerabilities of ML models to compromise their integrity and functionality. This blog article will delve into the intricacies of adversarial machine learning attacks, exploring their various types, real-world implications, and effective mitigation strategies. We will adopt a C-suite-centric perspective, focusing on the business impact, ROI, and risk mitigation associated with these attacks.
In cyber warfare, where the lines between offence and defence constantly blur, a particularly insidious threat looms large: the logic bomb. These malicious code snippets, embedded within legitimate applications, scripts, or systems, are designed to unleash destructive payloads under specific conditions or triggers. For C-level executives responsible for their organisation’s security and reputation, understanding the nature, implications, and countermeasures of logic bombs is paramount.
A logic bomb is a time bomb waiting to go off within a computer system. Code remains dormant until a predetermined condition matches, such as a specific date, time, event, or data input. Once the trigger is pulled, the bomb explodes, executing its malicious payload, which can range from data deletion or corruption to system shutdown or network sabotage.