The rapid proliferation of artificial intelligence (AI) models within enterprises has brought about a concerning rise in security incidents and privacy breaches. According to a recent survey conducted by Gartner, two out of five organizations reported experiencing AI-related breaches or security incidents, with a quarter of them being malicious attacks. It is evident that conventional security controls are no longer sufficient to protect these valuable assets. In this blog, we will discuss the significance of Secure Enclaves for AI LLM model governance, where code, model, and data encryption are implemented during the flow of federation.
Learn more about how you can secure Generative AI with Secure Enclave in our previous blog — https://safelishare.com/blog/secure-generative-ai-llm-adoption-using-confidential-computing/
As AI technology becomes increasingly pervasive, organizations are deploying hundreds or even thousands of AI models across various applications. Gartner’s survey revealed that over 70% of enterprises have already deployed numerous AI models. Unfortunately, this widespread adoption has also led to a growth in attack surfaces. With such a vast attack surface, it is no surprise that compromises and malicious attacks against AI models are common.
Source: Gartner Blog
The survey results showcased that 41% of organizations surveyed experienced an AI privacy breach or security incident. Among these incidents, 60% were data compromises by internal parties, while 27% were malicious attacks targeting the organization’s AI infrastructure. These figures shed light on the magnitude of the problem, emphasizing the urgent need for robust security measures, as some incidents may go undetected.
It is evident that traditional security controls are insufficient to protect AI models effectively. Conventional measures fail to account for the unique risks associated with AI and its potential impact on organizations. To address these challenges, new forms of AI security and risk framework are necessary.
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Secure Enclaves offer a viable solution to enhance AI model security and governance. By encrypting the code, model, and data during federation, organizations can ensure the confidentiality and integrity of their AI assets. Secure Enclaves create a trusted execution environment, protecting sensitive information from unauthorized access or tampering.
Despite the challenges posed by a constrained budget, CIOs and CISOs must prioritize AI model security in their budget allocations. The statistics show that organizations that invest in AI security and risk framework and allocate budgets to the CIO office enjoy greater success in AI projects. It’s the concept of securing the model from the inside out rather than putting more layers in your IT or cloud infrastructure. Managing AI risks not only safeguards the organization but also leads to positive business outcomes.
As AI technology continues to revolutionize various industries, it is crucial to address the security risks associated with AI models. The prevalence of AI privacy breaches and security incidents demands a shift towards more robust security measures. Secure Enclaves offer a promising approach, enabling organizations to protect their AI assets through code, model, and data encryption. By investing in AI security and incorporating Secure Enclaves, CIOs and CISOs can mitigate risks, ensure regulatory compliance, and derive greater value from AI projects.
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