Why Does GenerativeAI Visibility Matter For Enterprises?

Generative AI: You Cannot Harness What You Cannot See

One of the most commonly quoted lines in IT and cybersecurity is “you cannot manage what you cannot see”. It speaks to the idea of visibility as being foundational to any information technology or security program. If it is your job to manage IT systems then you must start with knowing what systems or assets you have in the first place. The same is true for security and any other discipline for that matter. You cannot manage, secure, operationalize, orchestrate, inventory, analyze, or do much of anything, to what you cannot see! 

This idea also applies to Generative AI. Generative AI (GenAI) has proven to be an incredibly impactful technology in a very short span of time. In a short few months since November of 2022 we have gone from a time where most serious enterprises were blocking its use, to a time when companies are racing as fast as possible to incorporate it into their business. The primary driver behind this change of attitude can be summarized in one word: Productivity! 

Find out more about the evolution of enterprise attitudes towards generative AI in our blog ‘Enterprise AI Adoption: The Rapid Shift In Attitudes Towards GAI.

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The Power of Generative AI Enterprise Implementation 

The productivity gains from GenAI are bigger than anything we have seen in a very very long time. The last few decades of enterprise productivity optimization have yielded single digit improvements in productivity. GenAI has blown them out of the water. McKinsey estimates that, on average, the use of GenAI improves human productivity by a whopping 40%. Whether or not GenAi introduces risk into the enterprise, the productivity gains from it are going to propel it along anyway! So our best bet is to very quickly figure out how to use it in a secure and responsible manner, and then get out of the way as the workforce surges forward into this brave new world!

And since we cannot harness what we cannot see, the first step to building this foundation is: Visibility.

Visibility in Generative AI refers to knowing what GenAI is being used in the organization, by who, and for what purpose. It includes knowing what is being fed into AIs and what the AIs are feeding back into the organization. It also includes knowing where GenAI is fulfilling its intended purpose and where it needs to be improved, knowing who is being successful in their use of AI and why, and also who is failing to see the benefits. It includes knowing whether prompts introduce risk and the nature of those risks, knowing whether the use of GenAI is violating company policy, whether the policy is adequate given what is really going on and so on.

Only when you have visibility, can you truly utilize AI to its full potential while minimizing risks. This makes Visibility the single most important element of your GenAI program, followed closely by Generative AI governance.

Invest in it. Today. Tomorrow might be too late.

The Need for Generative AI Visibility

Visibility matters for three really important reasons:

1. Strategy
2. Risk Mitigation
3. Responsibility

Each of these is a focus area for the Enterprise C-Suite and Board. In the wild Wild West that is GenAI today, visibility is going to be the only thing that provides an anchor for strategy, for mitigating risk, and for being responsible citizens of the world. Let’s have a quick look into why.

1. Strategy: Understanding Enterprise Wide Generative AI Visibility

Every company who wants to harness the power of GenAi needs to have a game plan, a strategy. But the paradigm is so new that it is virtually impossible to create a strong strategy at the get go. The strategy has to be a living thing, and that living strategy needs to feed on information. Information about what employees are trying and where it’s working. About who is pushing the limits and whether or not it is paying off. About which AIs are more effective for what types of work. What types of prompts are more effective than others and so on.

This data, this visibility can feed that living strategy and refine it at the speed of AI itself, into a powerful engine that produces dominating competitive advantage. This is only possible when the foundation of strategy is built on visibility.

2. Risk Mitigation: The Hidden Risks of Unseen AI

Generative AI is fraught with Risk. With all the opportunities it presents, it brings with it tremendous amounts of exposure. Exposure comes in the form of model errors also called hallucinations, model bias, data exposure during model training, loss of sensitive data to external AIs, risk of exposing intellectual property, compliance and regulatory risk, data security and data privacy risk, and finally the risk of exponential and accelerated error propagation – as small errors in models and usage get amplified during learning cycles and used billions of times each day by hundreds of millions of users and applications across the world. 

So how can you mitigate risk? Well, you can’t mitigate something you do not know about and so we come back to the idea of visibility. Seeing exactly how AI is being used and the impact it is having, positive or negative, along all the dimensions mentioned in the preceding paragraphs, produces insight that can drive mitigation controls. 

Mitigation controls include data loss prevention, incident management, audit and compliance, support for investigation, employee education, policy creation and distribution, training, promotion engineering, AI access control and several others. You canna mitigate what you do not know about and therein lies the power of visibility.

3. Responsibility: The Ethical Considerations Of AI

What is the enterprise responsible for when it comes to AI? Responsibility in the world of GenAI is much like responsibility anywhere else – it is all about the prevention of harm to individuals and to the world overall. Now it’s not about enterprise risk mitigation. It is about avoiding bad outcomes for humans in a micro sense and the human race overall. 

Responsibility includes making sure that the elderly who are not AI savvy, do not feed their passports into ChatGPT, that models do not produce instructions for committing crimes, that if there are dangerous actions resulting from the use of GenAI, those are visible and squashed as early as possible, and so on.

Once again, it is evident that at the root of responsibility lies visibility. You cannot be responsible when you do not know what you are dealing with. If you know who is using GenAI and for what as well as how this usage changes over time, in all the details described earlier, you can create a GenAI program that has a strong foundation and that grows with your business.

Eager to find out more about responsible GenAI usage? Read our CXO Guide for Generative AI Governance & Responsible Use today.

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