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The rise of generative AI (GenAI) has ushered in a transformative era for businesses, much like the early days of cloud computing. With many organizations implementing AI projects and many more recognizing AI’s potential to boost productivity, the integration of large language models (LLMs) and other GenAI models into daily operations is no longer a novelty, but a necessity. However, every new tool added along the adoption trajectory expands the organization’s attack surface, introducing new vulnerabilities that malicious actors are eager to exploit.

Drawing parallels from the introduction of cloud services, it’s apparent that traditional cybersecurity strategies must evolve with the technology. Just as the cloud revolution required robust, provider-agnostic security solutions, today’s considerably more diverse AI landscape demands a similar approach to secure GenAI models. In the early 2010s, businesses transitioned to the cloud, enjoying benefits like scalability and cost-efficiency, but soon realized the complexities and challenges of securing such environments, especially under a multi-provider strategy.

The Shared Responsibility Model emerged during the cloud era, identifying security as a mutual endeavor between providers and customers. This model remains relevant as organizations continue to use multiple cloud services and have added a variety of AI models—from large to small, to internal and external, to LLMs and multimodal, to proprietary, public, and open source—from a variety of providers—OpenAI, Cohere, Anthropic, etc.—to automate or enhance a huge variety of tasks—from customer service to document preparation to internal data analysis. And each model introduces unique risks. 

Clearly, the need for a comprehensive security strategy that offers visibility and control across all models is more of a certainty than an option.

A provider- and model-agnostic security solution, such as CalypsoAI, ensures uniform safety standards, protecting data and aiding compliance across all platforms without subjecting the organization to vendor lock-in. This solution addresses the security challenges posed by multiple models and adapts to evolving regulatory compliance requirements.

As organizations across the spectrum embed AI ever deeper into their operational fabric, it’s imperative they keep in mind lessons learned in the past. The journey of cloud security provides invaluable observations about and practices for handling new technology integrations, many of which are directly applicable to today’s AI-driven transformations. Ensuring security solutions are robust, scalable, and flexible will protect against current threats and prepare the business world for tomorrow’s challenges. 

For more details about the security strategies and technologies discussed above, refer to our newly updated white paper, “From the Cloud to GenAI: The Need for Provider-Agnostic Security Solutions.”  

 

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