What goes into ensuring a secure digital transformation from the word goes with AI/ML solutions?
4 min read

What goes into ensuring a secure digital transformation from the word goes with AI/ML solutions?

Organizations are moving towards automation and digitization in an accelerated manner than ever before, the pandemic giving rise to the urgency for new products and services. New products are being used to adapt to the new norm of business as soon as possible with security taking a backseat
What goes into ensuring a secure digital transformation from the word goes with AI/ML solutions?

Organizations are moving towards automation and digitization in an accelerated manner than ever before, the pandemic giving rise to the urgency for new products and services. New products are being used to adapt to the new norm of business as soon as possible with security taking a backseat. Products with good security may seem slow and expensive at the beginning but are worth the wait and cost.

It is not advisable to rush in as risks multiply faster as organizations scale up their AI(Artificial Intelligence) and digital transformation process.

With the increase in volume and processing demands the need for cloud usage also goes up and so also the vulnerability. Adding to the challenges is the fact that cybersecurity as a function is using more and more AI solutions.

  1. Several of the AI solutions and platforms lack enterprise scalability
  2. Some offers scale while missing on tracking and auditing
  3. If some offer audit, the majority missed key data encryption. As data encryption reduces the smooth interaction of data and turnaround time for the application.
  4. Several other challenges include enterprise-grade user authentication, single sign-on, automated policy implementation like one user one login across cloud and on-premise uses
  5. Keeping a balance of security and reliability providing their value adds with their respective software

Due to urgency in these disruptive times, even experienced companies are not following regular tests and audits as good security practices.

There are 3 sets of information/data required for ML and AI services

  • Data to create a predictive model / to train
  • Data to test the models created / to test
  • Live data to put the model to actual work / UAT before production

All three types contain valuable data which is both sensitive and critical. Apart from the above, the system deals with various types of data on a daily basis, which accelerates the risk if a proper security system is not implemented.

The people creating the model are always data hungry but organisations should know clearly at the onset how much data is needed any extra data can add to the security risks for customers. For e.g. any personal data like Pan number or Aadhar card data which does not effect the working of the model should be avoided during training and test. Instead synthetic data/fake data could be generated for these purposes with ease. Even if using true data, enable it in a safe environment, with controlled access. CEO of Keito, Amal pointed out that planning before the implementation is key for any data/business transformation journey for medium and large businesses. Planning involves, Effective (Current AS-IS Process Mapping + Mapping for Data Requirements + Building Blocks for Future Scalable Solution). We effectively plan to minimise the risk of our customers as we work towards a scalable solution,

On the other hand, depending on the industry, ML and AI systems also require relevant data fields which give them insights into consumer behaviour, buying habits, etc. This data is also what cyber criminals are looking for and therefore increases the security risks and any incidence of breach and cost the organisation not only a monetary loss but also damages the Brand image.

Hence it is critical that all digital initiatives are developed using core security principles, there are regular checks and audits conducted which include testing and red teaming.

With the advent of open-source code, small firms have also started adopting digital services which have new-formed codes and some of them developed by persons not trained as security engineers. Each of these products which are available for data/digital transformation requires expert oversight. Not just any expert, it should be under the supervision of enterprise product leaders.

Further adding to the woes is the fact that there are yet no best practices that can be adopted for writing AI algorithms and a dearth of experts in this field.

The AI products which lack proper security can give away information easily during a cyber attack. These are key areas where SaaS Cloud players and other data transformation AI players can focus for the first cut of their security check-list

  1. Back end data
  2. Third-party information
  3. Authentication systems
  4. User end or interface

Companies, as part of good practices, should ensure a robust security system that will ensure safety seven while transferring or deleting.

Today, it had become comparatively easy for organisations to start using AI products and services which are cloud-based, but as the scale increases the required security systems may not be available in-house and then resorting to outsourcing may lead to delays and lapses in security.

This gap in security and not making security at the initial stages itself is now being realised by a few of the AI and ML vendors and they are educating their customers with regards to the issues and clear benefits of making security an integral part of their digital transformation or data management process.