With its exceptional flexibility to adapt – for instance to new processes and regulations – and its capability to learn from mistakes, AI has some serious potential up its sleeve to bring data governance to the next level.
Even though AI is still surrounded with a touch of magic and mystery, it’s important to keep in mind that AI subsets like machine learning or natural language processing (NLP) – no matter how powerful and sophisticated they might be – are in fact nothing more than mathematical algorithms. That said, many organizations are still in the dark about how these algorithms can be used to improve their strategic data governance workflow. Let’s see if we can shed some light on this…
Improved response time
First of all, the processing power of machine learning is particularly suitable to spot patterns and anomalies in data streams, and thus reveal security breaches and non-compliance issues. True, the same can be achieved using traditional tools, but not as quickly, nor thoroughly as when using AI. And as everybody knows, a short response time is crucial when an organization gets confronted with a data breach. In fact, a quick response could mean the difference between some discomfort or a heavyweight fine and dented public image.
Make smarter decisions
Furthermore, AI tools have the ability to process textual data. This function can be used to extract obligations from new regulatory texts and compare those with the current regulation that is documented in the organization. This way, you can make sure your IAM program is always up to date. Some AI tools even allow you to anticipate upcoming changes and approach IAM in a more proactive fashion.
And as AI solutions help you to be optimally informed at all times, you are empowered to make better and smarter decisions. This of course reduces the chance of incorrect decisions, which saves costs.
Empower your people
There is no denial that digital transformation brought many new opportunities to organizations. However, it also brought more data – IBM states that 90% of all data was created in the last two years –, more users and more responsibilities, which translates into a significantly larger workload in various areas in the organization. One of those areas is data governance, where the human driven approach is no longer sufficient to cope with the exponential growth of data and the related responsibilities.
One might conclude that it’s better to replace the data governance workforce altogether by an AI-ish solution, but that would be a mistake. The smart thing to do here, is to deploy humans in what they do best and assist them with AI. In fact, AI is not designed to replace people, but merely to assist them. So when you have an AI solution in place, your data governance team will be able to cope with more data and more responsibilities.
Align your security strategy
These days, it is common sense to distribute IAM responsibilities across the entire organization. And rightfully so. However, this is easier said than done, because understanding and correctly applying access management is often challenging for other departments in the organization.
AI can be a major help here, as it enables to ‘translate’ the often predominantly technical approach of access management into a more human approach – providing focus and contextual insights – that is understandable on all levels in the organization.
This also helps to better align and shape the security strategy and policies across the entire organization. AI could for instance learn that only HR people seem to have access to employee data and consequently suggest to create a new internal rule stating that only HR people are allowed to access employee data.
Is AI the perfect solution?
AI is a remarkably versatile and powerful tool which definitely earns a place in your IAM toolbox. However, this doesn’t mean that AI can simply replace the other tools. And neither is AI perfect. A typical AI challenge is that it’s sometimes hard to understand how and why the model makes certain decisions. And as the model gets more complicated, it also gets less transparent.
Another issue is that AI models can show unintended biases because of unexpected correlations in the data, which could lead to suboptimal decisions. It also occurs that AI models behave differently than originally intended, because the data that was used to train the model differs too much from real world data.
How Elimity approaches AI
Elimity uses machine learning technology to bring you valuable insights about the current identity and access state of everybody in your organization. In our approach, AI has an additive role where machine learning functions as the virtual assistant of a human expert. This assistant helps to dig quickly through large amounts of data and flags anything unusual for human review.
What we can do for you
Elimity Insights is a powerful, yet easy to use SaaS tool which uses machine learning technology to get in control fast and to stay on top of the ever-changing regulatory requirements.