Securing AI in Business - From Data Culture to Best Implementation Practices

The Dangers of “Shadow AI” in Business

The rapid and often unsupervised adoption of artificial intelligence tools is creating a worrying new phenomenon: “shadow AI.” A recent Cyberhaven study reveals that the volume of corporate data placed into AI tools by employees increased by 485% between March 2023 and March 2024, exposing organizations to considerable risks (Source). This uncontrolled use of tools like ChatGPT through personal accounts represents a major vulnerability, as these applications are not subject to the same security measures as company-approved technologies.

To counter these risks, organizations must implement a clear policy on AI tool usage and educate their employees about the potential dangers. As recommended by the Quebec government in its best practices guide, it is crucial to “report any information that could suggest a data leak” and to be “careful in choosing AI tools” (Source).

Local LLM Hosting: A Solution for Data Security

Facing growing concerns about data privacy, local hosting of language models (LLMs) is emerging as a secure alternative to cloud-based SaaS solutions. On-premise deployment allows businesses to maintain full control over their infrastructure and sensitive data (Source).

Organizations that opt for local deployments can implement their own security protocols, including firewalls, encryption, and customized access controls (Source). Services like Paradigm by LightOn now enable businesses to easily deploy their LLM models on their own infrastructure, providing a solution tailored to privacy requirements (Source).

Data Culture: An Essential Prerequisite for Any AI Strategy

Before diving into AI technology implementation, businesses must first develop a strong data culture. This foundation involves establishing rigorous procedures for data collection, storage, and governance, as well as training teams on information management best practices.

A mature data culture makes it possible to clearly identify which information can be processed by AI and which must remain strictly protected. It also ensures that the data used to train or feed AI systems is relevant, high-quality, and compliant with regulatory frameworks.

The Canadian Centre for Cyber Security emphasizes the importance of an integrated security approach throughout the AI system lifecycle, from design to maintenance (Source). This holistic approach is only possible if the organization already has a well-established data culture.

Blue Fox’s Take

At Blue Fox, we help organizations transition to a secure and effective use of AI. Our approach prioritizes establishing a strong data culture as the foundation of any artificial intelligence strategy. We firmly believe that security should never be sacrificed for innovation, but rather integrated at every step of the process. Our team of experts can help you navigate this new technological ecosystem, balancing agility with the protection of your company’s information assets.

#ArtificialIntelligence #AICybersecurity #DataCulture #LLM #DigitalTransformation #DataSecurity

Sources:

Quebec Ministry of Cybersecurity and Digital – Generative AI Best Practices Guide

Canadian Centre for Cyber Security – Guidelines for Secure AI System Development

Quebec Economy – The Rise of “Shadow AI” Threatens Business Security

Reglo.ai – LLM SaaS vs Local: Advantages and Disadvantages

Xite.AI – On-Premise LLM: Ensuring Data Privacy & Control

LightOn – Hosting Large Language Models