As artificial intelligence (AI) grows, AI guardrails ensure safety, accuracy, and ethical use. These guardrails are a set of protocols and best practices designed to mitigate risks associated with AI, such as bias, misinformation, and security threats. They are vital in shaping how AI systems, particularly generative AI, are developed and deployed.

Privileged Access Management (PAM) is a comprehensive methodology for managing and securing privileged accounts—those that possess elevated permissions to perform critical functions within an organisation’s IT infrastructure. These accounts enable access to sensitive data and systems, making them highly attractive to cybercriminals. The core objective of PAM is to ensure that only authorised personnel have access to these accounts, under strict monitoring and control.

How Generative AI Can Help Identify Malware? Spambrella explains how AI models add value: Generative AI models can identify malware by learning the patterns and structures typical of malicious code versus benign software. Code Generation and Analysis – By generating variations of known malware, these models can simulate potential new forms of malware, helping cybersecurity teams anticipate and defend against unseen threats.

As large language models (LLMs) become more advanced and are granted additional capabilities by developers, security risks increase dramatically. Manipulated LLMs are no longer just a risk of ethical policy violations; they have become a , potentially aiding in the compromise of the systems they’re integrated into.

In today’s rapidly evolving digital landscape, IT leaders, whether CIOs, CISOs, or VPs of IT, are responsible for driving a range of initiatives that enable business growth and success. Projects like cloud migration, hybrid workforce enablement, and SaaS adoption are now essential. However, these initiatives carry inherent risks that need to be carefully managed, especially when it comes to performance, security, and user experience.

In 2024, healthcare organizations face heightened security challenges, mainly as they increasingly rely on Application Programming Interfaces (APIs) to support critical functions. APIs have become indispensable in driving digital transformation and improving operational efficiencies across healthcare systems. However, the rising complexity and volume of APIs, alongside insufficient security practices, have created a vulnerable environment ripe for exploitation.

False positives in API security are a serious problem, often resulting in wasted results and time, missing real threats, alert fatigue, and operational disruption. Fortunately, however, emerging technologies like machine learning (ML) can help organizations minimize false positives and streamline the protection of their APIs. Let's examine how.