The scale of digital data creation is a problem for organizations. The number of messages generated is now measured by the minute rather than the hour. This speed and scale is particularly challenging for regulated industries, which need to ensure compliance across all communications. Technological advancements make these tasks more manageable, but are often overlooked and not optimized to their full potential.
Many information security solutions that claim to solve big data take advantage of buzzwords to describe their capabilities. For instance, we hear a lot about artificial intelligence (AI) and machine learning (ML). These are powerful solutions, but often the practicality and real meaning of that technology is lost in the repeat messaging. It’s time to shift the story and remember why AI/ML is more than just a buzzword.
ML in action
Automation is critical today because it relieves the burden of manual human review, driving efficiency. Not only can ML offer cybersecurity protection, but it can also drive business insights.
Our clients need to ensure compliance across their digital channels. For pharma, they need to capture all seller communications and record them in Veeva. Some reps create 125K+ meetings/day, which means they need to capture 2.5M messages a month. As our clients have tried to evolve with market needs, they have taken up WhatsApp and WeChat. Their reps generate 15x more messages/day on those channels.
That scale of data creation is too much for any human to manually review, assure compliance, and maintain full audit trails. With AI/ML, they reduce the amount of time an admin spends in front of the screen reviewing violations. The solution learns from reviewers’ activity over time, and prioritizes risks accordingly. Reviewers no longer have to spend time looking at events they have deemed unimportant.
In addition, the solution also offers suggestions on how to change the rules and government to increase efficiency. Now, instead of an admin testing a rule change and waiting a month to see the result, the system can offer insight into what the modification will do - whether it’s an improvement or a detriment.
Taking away the burden of manual process and making suggestions/predictions to system changes is the real power of AI/ML.
Case Study: How a Global100 pharmaceutical enterprise
automates WhatsApp security and compliance
ML models are not created equal
Not every AI/ML solution will effectively solve your business problems and create efficiency in business processes. The reason ML is successful in the example above is because the model was configurable to their organization’s specific needs. There is no way to adapt or evolve when a one-size-fits-all model is applied. If the model is a black box, then regulated industries are unable to use it because there is no chain of evidence in an audited environment. The ability to improve the confidence allows the solution to learn and grow.
AI/ML will continue to be a trendy topic, so it’s important to remember the value in the solution. It can be more than a buzzword - find a solution that drives business forward.
Contact us to see how we can start driving business insights for your organization.