2024 has been a year of rapid AI adoption, with many companies struggling to capitalize on the latest advances for fear of being left behind. However, despite significant investment, organizations often struggle to realize tangible benefits from their AI initiatives. In fact, reports suggest 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.
Arguably, the root of this problem lies in a lack of control. Organizations are struggling to implement AI tools in a way that not only provides benefits but also does not compromise the privacy of their data. In 2025, companies must ensure they choose the right AI tool for their work, while maintaining the control and privacy their data needs.
Before embarking on an AI initiative, it is essential to define clear objectives. What specific problem are you trying to solve? What value do you hope to get from AI? Is it threat intelligence, better decision making, or better customer experience? Only once these objectives are identified can a company know what type of AI it needs.
For this it is crucial to find the right tool for the job. The first step is to understand that while large language models (LLMs) have dominated the headlines and fueled the hype, they are not the only form of AI model. Instead, there are a number of different tools available that focus on specialized tasks and solutions that may not only be more suitable, but also more capable.
This is because specialized AI is designed to address a specific task rather than being trained to offer a solution for everyone, both professionally and personally. What’s more, unlike LLMs, which train on vast, often uncurated data sets, specialized AI models focus solely on relevant data, resulting in greater accuracy and efficiency. Finally, specialized AI models are more efficient in terms of computational resources and energy consumption, making them more cost-effective, less impactful on the environment, and faster to deploy.
It’s crucial to consider all options when looking for the right tool for the job to ensure you stay in control of your data and focus on the work, not the hype. After all, if you choose the wrong tool, you’ll lose control of your data the moment you log in.
The right data and the right privacy
A highly touted advantage of LLMs is that they are trained with large amounts of data and can therefore provide insights and generate content for organizations across industries and regions. However, while this is indeed a plus for people looking for a tool capable of providing such reach, in most business cases this is actually a negative.
This is because being trained on such large data sets can lead to a reduction in the quality, accuracy, and completeness of that data. What’s more, it’s often difficult to discover what data the LLM was specifically trained on in order to validate it. This is a particular challenge for companies that need a high degree of transparency and accuracy in 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.
Meanwhile, specialized AI tools can offer users the option to choose the data on which the model is trained, and the client can see and select those sources transparently. For example, a small language model (SLM) AI tool can receive various sources in the form of thesauruses so that it can accurately understand a user’s specific needs; This includes not only languages in a formal sense, but also the ability to understand the technical jargon and industry experience of a company, as well as that company’s own coded notations and shorthand. This can offer a very efficient approach for an organization when it comes to AI adoption, as it is the tool that adapts to the user, rather than staff having to be trained to use it.
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 their training and make it work for them is kept private and confidential and not shared externally. This is important not only to protect a company from breaches and keep its sensitive and confidential information secret, but also for regulatory and legal reasons, as many industries have strict control over many aspects of financial, health and PII data. . This also applies to data used as part of AI prompts and analysis once the tool is used, and any data passed through or subject to an AI tool must be secure and private.
For example, LLMs often require large amounts of data to be shared with external providers. This can pose significant risks to sensitive information, especially for companies operating in highly regulated industries. In contrast, private AI models, such as specialized AI, can be deployed within a secure, zero-trust environment, ensuring data remains confidential and protected from unauthorized access.
By opting for a private AI solution, organizations can safeguard their intellectual property and maintain control over their data, mitigating the potential for data breaches and reputational damage. Therefore, they can use AI on even their most sensitive and regulated data rather than having to limit it to publicly available material, thus maximizing the tool’s potential profits.
Integration, control and security
It is imperative that an organization has full control over how AI is implemented in its workflow and system with all data access strictly controlled and transparent. This is particularly important in industries that work with sensitive and regulated data, as they need to be able to report how that data has been used and who has had access to it.
The importance of this has been highlighted in 2024 by a series of surveys and reports revealing the prevalence of data exposure due to artificial intelligence tools. For example, research from Syrenis found that 71% of AI users regret sharing their data with AI tools after realizing the extent of what was shared, while a RiverSafe survey of CISOs found that one in five UK companies exposed sensitive corporate data as a result of employees using AI tools.
To put it bluntly, if an AI tool, or any other tool, collects a company’s data or shares its information externally, then that company is at risk of a breach and could be at risk of being out of compliance. of compliance.
When implementing new AI tools, pay close attention to how they integrate within a company’s existing architecture and make sure they don’t require data to be stored outside of your control. For example, if a company chooses to use a cloud-based AI tool, it is crucial to ensure that it has the ability to host that cloud structure on its own system or prevent third parties from accessing and protecting data. Cyberattacks 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, ensuring that your information is protected from less privileged access and that no one can access data they don’t need. Homomorphic encryption can also ensure that data can remain encrypted at rest, in transit and in use, with fully encrypted data being able to be searched and computed. However, while data security and privacy are crucial, it is also important to check the scalability and speed of the system to ensure that AI is capable of providing the real-time insights and services needed in today’s market. .
Final thoughts
Successful AI implementation depends on a balanced approach that prioritizes governance, data privacy, and security. By carefully selecting AI tools tailored to specific needs, prioritizing data quality and transparency, and implementing strong security measures, organizations can harness the power of AI while mitigating potential risks.
As the AI landscape continues to evolve, it is imperative to stay informed on emerging technologies and best practices to ensure that 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 while maintaining control.
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