Getting AI right by 2025: control, control, control
2024 has been a year of rapid AI adoption, with many companies doing their utmost to take advantage of the latest developments for fear of being left behind. However, despite significant investments, organizations often struggle to realize tangible benefits from their AI initiatives. In fact, reports show that while 68% of large companies have integrated AI, a quarter of IT professionals regret the rapid adoption of AI, and two-thirds wish they had chosen technologies more carefully.
The root of this problem undoubtedly lies in a lack of control. Organizations are struggling to implement AI tools in a way that not only delivers benefits but also doesn’t compromise their data privacy. In 2025, companies must ensure they choose the right AI tool for the job while maintaining the control and privacy their data needs.
Before embarking on an AI initiative, it is critical to define clear objectives. What specific problem are you trying to solve? What value do you expect to get from AI? Is it about threat intelligence, improved decision making or an improved customer experience? Only once these goals are identified can a company know what type of AI it needs.
Crucial here is finding the right tool for the job. The first step is to understand that while Large Language Models (LLMs) dominate the headlines and fuel the hype, they are not the only form of AI model. Instead, there are a number of different tools available that focus on specialist tasks and solutions that are not only more suitable, but also more capable.
This is because specialized AI is designed to tackle a specific task, and not trained to provide a solution for everyone – professional or personal. Furthermore, unlike LLMs, which are trained on massive, often uncurated datasets, specialized AI models focus only on relevant data, resulting in higher accuracy and efficiency. Finally, specialized AI models are more efficient in terms of computing resources and energy consumption, making them more cost-effective, lower environmental impact, and faster to deploy.
It’s critical to consider all options when looking for the right tool for the job so you stay in control of your data and can focus on the job and not the hype. After all, if you choose the wrong tool, you will lose control of your data as soon as you log in.
The right data and the right privacy
A much-touted advantage of LLMs is that they are trained on massive amounts of data and can therefore provide insights and generate content for organizations from all sectors and regions. While this is indeed a benefit for those looking for a tool that offers such capabilities, in most business cases it is actually a negative.
This is because training on such massive amounts of data can cause a reduction in the quality, accuracy and integrity of that data. Additionally, it is often difficult to discover what data the LLM has been specifically trained to validate. This is a particular challenge for companies that require a high degree of transparency and accuracy with their results, as LLMs have been shown to be prone to hallucinations and biases as a result of learning from such vast and varied data.
Specialized AI tools, meanwhile, can give users the ability to choose the data the model is trained on, with the customer able to transparently see and manage these sources. For example, a Small Language Model (SLM) AI tool can be provided with a number of resources in the form of thesauruses so that it can accurately understand the specific needs of a user – this includes not only languages in a formal sense, but also the ability to understand the technical jargon and expertise of a company’s industry, as well as that company’s own annotations and coded shorthand. This can provide a very efficient approach for an organization when it comes to AI adoption, as it is the tool that is customized to the user, and not the staff that needs to be trained on the tool.
Another aspect to consider is the privacy of that data. It is critical that any data an organization provides to an AI tool to tailor training and make it work for them remains private and confidential and not shared externally. This is important not only to protect a business from breaches and to keep sensitive and confidential information secret, but also for regulatory and legal reasons, with many industries having strict control over many aspects of financial, health and PII data . This includes data used as part of prompts and AI analysis once the tool is used, where all data passing through or subjected to an AI tool must be secure and private.
For example, LLMs often require large amounts of data to be shared with third-party vendors. This can pose significant risks to sensitive information, especially for companies operating in highly regulated industries. In contrast, private AI models, such as specialist AI, can be deployed within a secure, zero-trust environment, ensuring data remains confidential and protected from unauthorized access.
By choosing a private AI solution, organizations can protect their intellectual property and maintain control over their data, reducing the risk of data breaches and reputational damage. They are therefore able to use the AI with even their most confidential and regulated data, rather than having to limit it to publicly available material, maximizing the tool’s potential benefits.
Integration, control and security
It is imperative that an organization has full control over how the AI is implemented into their workflow and system, with all data access tightly controlled and transparent. This is especially important in industries that work with sensitive and regulated data, as they need to be able to report on how that data has been used and who has had access to it.
The importance of this has been highlighted in 2024 by a number of studies and reports exposing the prevalence of data exposure due to AI tools. For example, research from Syrenis shows that 71% of AI users regret sharing their data with AI tools after realizing the extent of what was being shared, while a RiverSafe survey of CISOs found that one in five UK companies exposed sensitive business data as a result of employees using AI tools.
To put it bluntly, if an AI tool, or any tool for that matter, collects a company’s data or shares that information externally, that company is at risk of a breach and could be at risk of not meeting compliance requirements .
When implementing new AI tools, pay close attention to how they integrate into a company’s existing architecture and ensure data doesn’t need to be stored outside of your control. For example, if a company chooses to use a cloud-based AI tool, it is critical to ensure that they have the ability to host that cloud structure on their own system, or restrict third-party access to the data and protect it against cyber attacks such as ransomware. This can be achieved by combining the cloud provider’s infrastructure with your own decentralized storage, for example blockchain, and implementing strict access control and encryption.
These same encryption and access measures can also ensure that you have control over what data is accessed and by whom, so that your information is protected by least privileged access, where no one has access to data they don’t need. Homomorphic encryption can also allow data to remain encrypted at rest, in transit, and in use, allowing search and computation on the fully encrypted data. While the security and privacy of the data are crucial, it is also important to check the scalability and speed of the system to ensure that the AI is able to provide the real-time insights and services needed in today’s market .
Final thoughts
The successful implementation of AI depends on a balanced approach that prioritizes control, data privacy and security. By carefully selecting AI tools tailored to specific needs, prioritizing data quality and transparency, and implementing robust security measures, organizations can leverage the power of AI while mitigating potential risks.
As the AI landscape continues to evolve, it is imperative to stay abreast of emerging technologies and best practices to ensure AI is used responsibly and ethically. By taking a proactive and strategic approach, organizations can unlock the full potential of AI and drive innovation, while safeguarding their interests by maintaining control.
We recommended the best AI website builder.
This article was produced as part of Ny BreakingPro’s Expert Insights channel, where we profile the best and brightest minds in today’s technology industry. The views expressed here are those of the author and are not necessarily those of Ny BreakingPro or Future plc. If you are interested in contributing, you can read more here: https://www.techradar.com/news/submit-your-story-to-techradar-pro