AI readiness: Is your organisation AI-ready?
7/1/2024 | nmb@konfitech.com
So, you have determined that you will use AI, you have decided the use-cases, and areas of which it makes sense to use the AI for your strategy. Now you need to get ready, what is the level of your AI readiness?
What kind of new indirect risks and implications does the usage of AI bring to your organization? What are the things that you must think about before starting?
On a high-level artificial intelligence bring three overbearing pillars for you as a business leader to consider.
- Security concerns
- Data management concerns
- AI Values & Strategy
These will encompass most of the business leader level questions that an organisation must answer to determine if they are AI ready, and none of them are particularly technological.
The focus here is more on understanding, and knowing what world you are throwing your business into if you decide to use AI. Defining clear use cases and knowing what implications of those use-cases are.
AI readiness concern 1: Security concerns around AI.
Regardless of what, AI is a completely new technology for most businesses. Depending on where you chose to deploy them, towards customers or more internally facing, it will bring potential risk factors and new attack surfaces.
Thus, it is where clear use-case definitions and policies have the power to set the guardrails for where it makes sense to apply AI. As most likely companies will not develop their own models where they can control most aspects. Many will use proprietary models where the core business itself lacks part of the control.
The impact of AI will be mainly in two ways of attack. The direct attack, which is actively trying to misguide the AI model to give you wrongful information. Like saying your name is “credit card number”, and the AI mistakes the variable with the actual credit card number variable and leaks a credit card number.
Or the indirect way, which is when someone purposely misguides the AI to obfuscate information. Like hide illegal activities going on inside the system.
While living in an on-premises world IT got used to the most important part of the security grid to be the network. As one can move safely in a protected fortress.
Recently the cloud world shifted a lot of the importance of security over to identity and access management, making the user security more important than ever. Now the data travels over the internet, and users themselves have more power in their individual profiles. Making it extremely important to protect.
AI is another revolution in the world of security. You now must consider many other aspects of security that is around the AI. Such has how input is handled? Where AI gets information from? Who is authorized to ask questions? Where you can access the AI from?
AI Readiness concern 2: Data Management Concerns
This is particularly essential when using proprietary models such as Azure AI services, AWS Artificial Intelligence, or Googles AI services, and all others. As these are the most common ones.
Here it is vital to consider input data; Consider you are providing the vendor data that have associated data risks with them?
Is it safe that the data travels across the internet and possibly seeps into other people’s hands through data seepage in the LLMS?
Then you also need to consider the output of the data, what type of positions could it put you in if the data has biases, are too general, or inaccurate? As we have shown in one of our previous articles AI that is not purpose built suffer when they are asked non-generic questions. Is it worth it with the informational risk that you can spread misinformation to clients?
Define the AI principles
As we have discussed above AI is not a magic wand you can wave and only get upsides. It comes with significant think-about concerns. You need to align all the business leaders and stakeholders and work out clearly defined AI principles. These should encapsulate both the business strategy and elements of medium-term strategic decisions such as cloud strategy and data strategy. As these are vital elements to enable AI.
What this mean is that in this statement. We firstly give clear guidance and principles of where and where AI should be used is to be stated. Secondly, with what intent and information it can be shared.
Humans will be more and more involved in machines and visa versa. Therefore, you need to clearly state what types of interactions you should remove the machine element.
Possible solutions
If you’re sitting now in the end of this article terrified of AI and the downsides it could bring. Do not worry, there are plenty of solutions out there to navigate the landscape and threats.
One of the first things you can do is bring in experts to your teams, through tech team augmentation who can provide you with much of the expertise.
Another one can be completely outsourcing the model operations. Therefore move the risk to the third parties having them report on their success.
You can also build your own internal teams and solutions.
This will bring you the skills needed to develop sound expertise and learning internally to handle what AI brings.
After all this, are you interested in learning more about AI readiness?
To summarize, Konfitech delivers tech team augmentation, outsourcing services, and talent sourcing, reach out for a quick chat: https://www.konfitech.com/contact-us or here.
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