The idea stage of implementing generative AI is exciting, from determining use cases to testing pilot projects. But it’s not the end game. There are plenty of additional thrills and spills ahead including model tuning, AI security and ethical implementations, platform customizations, and app integrations. What’s more, addressing each step in an ad hoc or piecemeal manner can lead to disjointed or poor performance.
Fortunately, a comprehensive AI platform can help steer generative AI projects and apps from concept to consumption in an orchestrated, automated, and streamlined way. The platform can also help ensure the project is completed responsibly, securely, and in compliance. Here’s what to look for in ideal AI platform.
Mindful AI
This is a responsible approach to designing, building, and deploying AI that focuses on the entirety of AI, not just its parts and not for just a moment in time. It aims to weed out biases and other forms of intentional or unintentional harm to humans while enhancing social and human rights fairly across all demographics, psychographics, races, cultures, languages, and genders.
It is not enough to make policies declaring the intent of acting responsibly with AI. There must also be a means to evaluate and govern AI at the massive scales this technology operates. Transparency and accountability must also be baked into the process. An AI platform can help perform these duties via built-in guardrails.
Data privacy and security
A rapidly growing number of regulations carrying serious penalties shouldn’t be the only reason to safeguard data privacy and security. Customers expect it, constituencies demand it, and cyber criminals provoke it.
Large language models (LLMs) like generative AI, can be a major threat to both data privacy and data security. For example, according to the United States Department of Health and Human Services’ Health Sector Cybersecurity Coordination Center (HC3) guide (pdf) on artificial intelligence, AI can be used to craft and distribute, at scale, numerous flawless phishing emails. According to a Verizon report, 74% of data breaches in 2023 were caused by social engineering (phishing), errors, and misuse while 50% were due to social engineering (phishing) alone.
AI threats can only be combatted with other AI. Either the AI application is secured, or the AI model is trained to detect and stop adversarial AI attacks. A good AI platform must aid both the efforts to secure AI applications and build secure AI models that can be trained for security duties, if desired.
Effortless customization
Topping the list of must-haves in AI platforms are increased efficiency and the ability to customize the platform endlessly to suit specific goals and use cases.
But there are other pressing issues that should also be addressed in the platform. For example, experienced AI professionals often complain about the difficulties in integrating generative AI applications with enterprise systems.
For the benefits to be fully realized, generative AI needs to be integrated with automation systems, security programs, governance and compliance add-ons and layering, import and export of capabilities or specific outputs to and from other applications, and so on. The right AI platform can seamlessly automate integrations so that this issue is reduced or eliminated.
The bottom line
Success beyond the pilot project requires planning. An AI platform can guide and automate generative AI models and apps – from concept to value transformation.