Are you struggling to balance the productivity gains delivered by Generative AI with security, data privacy and compliance concerns? In this webinar, Tines and AWS share how you can develop effective strategies to mitigate these risks while providing models with enough contextual information to allow them to solve problems accurately and effectively. You’ll hear from industry leaders who created a secure-by-design approach to building AI features and will learn.
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.
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.
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 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.
Mobile Breakthrough Awards has named Forward Networks the winner of its “2024 Enterprise Cloud Computing Software of the Year” award. This is Forward Networks’ fifth consecutive award win for Enterprise Cloud Computing from Mobile Breakthrough’s Award program.