Sign in to unlock valuable content and features from our AI-driven platform. Receive timely technology updates and the latest information from the solution providers who can help you realize your goals.
Start your journey by entering your name and email address below:
Please confirm your email address!
We are going to send a confirmation email to your email address to let you receive timely technology updates and the latest information from the solution providers who can help you realize your goals.
Is this you? Please confirm your name and email address below to receive the requested information.
Please check this box to confirm that you are opting-in to receive communications from Radix Solutions and the data sharing outlined in our privacy policy.
Initializing
Loading
AI security and Zero Trust
AI introduces new security challenges, from shadow AI use to data exfiltration. This whitepaper, "AI Security and Zero Trust," outlines how organizations can update their strategy to secure both data and applications in AI-driven environments. Download it to learn how to modernize your approach, align security with business goals, and mitigate emerging risks. Connect with Gert Labuschagne to discuss your path to resilient, AI-ready security.
Please enter your information below to access this content:
What is Zero Trust and why is it important for AI security?
Zero Trust is a modern security approach that focuses on verifying every access request, regardless of whether it originates from inside or outside the organization. It emphasizes protecting assets through explicit validation and least privilege access. This approach is crucial for AI security because traditional perimeter-based defenses are insufficient for safeguarding AI applications and the sensitive data they handle. By adopting Zero Trust principles, organizations can better manage the risks associated with AI, ensuring that data and applications remain secure in an increasingly complex digital landscape.
How does AI impact data security needs?
AI significantly amplifies the importance of data security for organizations. As AI technologies, particularly Generative AI, become integral to business operations, the value of data increases, making it a prime target for cyber attackers. Organizations must prioritize data classification and protection to mitigate risks associated with unauthorized access and data leaks. This shift necessitates a reevaluation of existing security strategies to ensure that data governance practices are robust and aligned with the evolving landscape of AI.
What is the shared responsibility model for AI security?
The shared responsibility model for AI security outlines the division of security responsibilities between organizations and their AI providers. Depending on the deployment type—Infrastructure as a Service (IaaS), Platform as a Service (PaaS), or Software as a Service (SaaS)—the responsibilities vary. Organizations are responsible for securing their data, applications, and usage, while providers manage the underlying infrastructure and platform security. This collaborative approach helps ensure that AI systems are resilient against evolving threats and that security investments are effectively aligned with organizational needs.
AI security and Zero Trust
published by Gert Labuschagne
ShareValue is Microsoft Solution Partner en SNA-gecertificeerd. Het SNA-keurmerk wordt al jaren aan ShareValue toegekend. Dit keurmerk is ontwikkeld om de risico’s te beperken omtrent keten- en inlenersaansprakelijkheid. Tweemaal per jaar worden wij door een geaccrediteerde inspectie-instelling gecontroleerd, met toekenning van het certificaat als gevolg.
Sign in with LinkedIn.jpg)