In today’s digital age, industries are constantly seeking the next big thing in technology, in the search for a system that will streamline operations and promote efficiency. Since the recent surge in widespread use, AI has been at the forefront of innovations with its growing popularity in programmes such as ChatGPT and customer service chatbots. While AI has been a breakthrough, particularly for its proficiency in analyzing large data sets, it doesn’t come without risks.
The world of advanced technologies and artificial intelligence is constantly in a state of flux with businesses seeking to strategically harness these innovations to maintain a competitive edge and thrive in a saturated market. One of the key components in staying ahead in the realm of AI will be the switch from cloud-based AI systems to locally stored solutions. This alternative ensures that businesses not only comply with local data regulations but also provide reliability, security and faster response times, making it ideal for real-time applications.
The current state of the market
As AI becomes increasingly integral to modern technology, businesses are eager to introduce these technologies into processes. However, the commonly used cloud-based services are not equipped with sufficient computation to support such technologies, nor are they secure. Such cloud-based services require data to be sent to remote servers in the US or Europe for processing, often resulting in delays due to data transmission and server processing times. As the data is transmitted overseas, it can also be exposed to potential security vulnerabilities and compliance issues, such as the use of private data to train AI models.
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Security concerns
Since 2015, the proportion of corporate data stored in the cloud has doubled to 60 percent, including sensitive information such as employee details, customer data, corporate finances, and intellectual property. While cloud storage offers efficiency, it raises significant security concerns, with 80 percent of companies reporting at least one cloud-related security incident in the past year. The rise of AI heightens these concerns, as AI systems require large datasets for training, often repurposing personal data without clear consent.
Some pertinent examples of security breaches include an unreported data breach at OpenAI in 2023, where sensitive customer data was accessed through an undisclosed exploit, raising significant concerns over data security. Similarly, vulnerabilities in Google’s Gemini language model have been identified, which could potentially allow for system prompts to be leaked, previous responses to be accessed, and content restrictions to be bypassed by framing requests as fiction.
Additionally, cloud-based storage is becoming increasingly expensive, with costs rising by nearly 20 percent every year, making it less viable for businesses over the long term. The need for robust and efficient privacy protections is therefore critical as companies continue to leverage cloud and AI technologies.
The way forward
As a result, the shift towards local AI is gaining momentum, offering solutions that run directly on local devices. Through enhancing privacy and reducing latency, local storage ensures data security and operational independence. As local AI processes data on the device where it is generated, all computations from data analysis to machine learning model execution happen directly on the local hardware. This approach not only functions without continuous internet connectivity, making it reliable in environments with limited or unreliable network access, it also proves to be more cost-effective than relying on cloud-based solutions.
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An additional benefit is that users can choose specific hardware and software configurations that best meet the computational needs and constraints of their applications, allowing for tailored and optimized AI solutions. Local AI can therefore be applied across multiple domains. This includes healthcare, manufacturing, retail, and more, offering flexible solutions that can be adapted to a wide range of use cases and industry requirements. This can be used to generate unique GPTs for each company while ensuring all data input remains private.
Future-proofing AI
AI systems are becoming increasingly integrated into our daily lives and business operations. Therefore, it is crucial to ensure these systems operate efficiently and swiftly to support real-time applications. Cloud-based storage remains the most popular method for managing data. However, a growing number of businesses are now exploring the shift to local storage to regain control over their private data. Regardless of the approach you choose for data management, prioritizing local regulations, security, and efficiency should be at the forefront of every industry’s strategy.
About the author
Quentin Reyes is the CEO and founder of Hyperfusion and is based in Dubai.
Quentin has several years of experience in project leadership and investment, with a strong focus on fintech, Web 3.0, and high-performance computing.
His professional journey began in 2015 in Shanghai, China. In October 2021, he co-founded Alpha Praetorian Capital, a venture capital firm focused on early-stage and growth investments. Since May 2022, he has also served as the CEO of HyperFusion, one of the GCC region’s largest generative AI clouds.
Over his career, Quentin has been involved in transactions totaling over $400 million, with a strong focus on fintech, data centers, computing infrastructure, and startups.
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