Few technologies in recent memory have disrupted the business landscape as profoundly as AI (artificial intelligence). AI’s ability to quickly generate new content, analyze data and information, and rapidly adapt to new circumstances has seen its investment grow as business leaders project its potential and future capabilities.
While AI can drive new efficiencies, foster innovation, and create new advantages for businesses, it’s important to keep in mind that for AI to truly add value to organizations, two elements cannot be overlooked: security and scalability.
AI that cannot quickly scale to meet demand will lead to inefficiencies and an inability to grow with your business. Similarly, unsecured AI can expose you to security breaches and inaccurate or corrupted AI models. Harnessing both principles will lead to AI initiatives that effectively solve problems and drive value.
Scalability refers to a system's ability to handle increased loads or expand in capacity without compromising performance. For AI models, which rely on the use of large datasets to make predictions, scalability is essential for supporting use and functionality. Building your AI model with scalability in mind ensures that expanding its use will not lead to a loss of computing power or accuracy with your algorithms and models.
Scalability is crucial for your AI implementation for three major reasons. The first is that building an AI model with scalability in mind will enable you to handle growing amounts of data. AI relies on large datasets to detect patterns and make accurate predictions. And it’s almost certain that models will require more data as they grow. Being unable to scale up to accommodate this increase could impair the future capabilities of your AI project.
Additionally, accounting for future computing needs is another element of scalability. Much of AI’s sophistication and success comes from the tremendous computing power behind it. It’s inevitable that your AI computing needs will increase with time. Accommodating computing scalability now will ensure the future operation of your AI.
Each of these examples showcase how scalability supports your business’ future growth. As your business grows, so will your data and computing needs. Factoring scalability into your current operations will enable your teams to match increasing demands on your AI.
It’s clear that scalability is integral to the current and future success of AI, but how can this be achieved? AI scalability depends on three major principles, using multiple data solutions, modular architecture, and distributed computing systems.
Using cloud platforms like AWS or Azure offers data storage system that can grow your business’ needs. Having multiple sources of data storage can also provide your teams with flexibility and on-demand resources that can be flexed for moments of high workloads.
Utilizing distributed computing offers similar benefits by building flexibility into your workflows and spreading computing needs across multiple machines. Distributed computing helps your system manage complex computations and large datasets by reducing the total burden for each individual machine or system.
Finally, modular architecture, or breaking your computing into individual components can help build scalability. With modular system, each individual component can be scaled according to demand, allowing you to adjust as your exact needs change.
Ensuring Security in AI
Security is paramount for AI, as compromised models can deliver inferior performance or even open damaging liabilities for your business. AI security involves protecting AI systems from threats and vulnerabilities that could compromise the integrity, confidentiality, and availability of AI data and operations.
AI intersects with several key security targets. Relying on datasets with mission-critical data, even sensitive or personally identifiable data, AI presents unique and innate security challenges. Preventing unauthorized access and maintaining the integrity of AI systems is not only essential for the successful use of AI, but also to protecting the overall security of your business.
Building a successful security environment for your AI requires implementing several key processes into your workflows. The main principles are data encryption, access controls, integrated cybersecurity, and consistent audits and monitoring of models.
Data encryption is an essential practice to protect data both at rest and at transit. It’s also a relatively simple process to implement across an entire organization. Encryption protects your data from unauthorized access, which is essential in cases where your data is compromised such as a large-scale breach. In these instances, powerful encryption can protect your data from being misused.
Access controls are another important factor in protecting your data and AI models. Strict access controls ensure that only authorized personnel can access sensitive information and can dramatically reduce the total number of potential threats to your models. Most security breaches are due to lapses in employee vigilance. Access controls help protect your models from employee mistakes and errors.
Additionally, integrated security ecosystems and regular audits are effective processes to protect AI models. Cybersecurity is most effective when it’s used at every stage of the development lifecycle. Proactively integrating security into planning, development, importing and exporting data, as well as reporting will create a secure foundation for your AI models.
AI as a technology is revolutionary and transformative, but it requires maintenance and care to ensure it meets its full potential. Factoring in two key design principles, scalability and security, will enable your AI to grow with your business and adapt to new challenges as they emerge. To drive value and innovation for your business, build an infrastructure prepared for the future.
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