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Cybersecurity framework for digital transformation

October is Cybersecurity Security Awareness month, so there is no better time to evaluate your organization’s data protection policies.

After all, in September’s blog post, we examined how companies are relying on AI and data to create efficiencies and new business models, but that state and federal regulations are compelling enterprises to ensure and prove ethical collection of data and to keep models free from bias.

This month, we are taking the discussion one step further to focus on data protection as it relates to digital transformation.  

When it comes to digital transformation, your entire organization will learn to work in new ways and with new processes. At the same time, they must also be trained to look for risks associated with the new, data-driven way of working. Knowledge workers with low awareness of risk can be the weak link in your risk management chain.

Having a low awareness of risk around how data is accessed and used can expose your entire organization to liabilities associated with new analytics and process-based tools. For example, the simple act of granting a user or partner the wrong access rights to data can open you up to loss of trust and revenue.

Besides humans, IT infrastructure is another area to examine for risk mitigation. Digital transformations require the deployment of new applications and infrastructure ecosystems while decommissioning old ones; however, IT staffs may lack the training and experience to sufficiently administer and patch vulnerabilities of the new environments. Plus, data protection methods used in test or development systems may not be consistent with those in production environments, opening you up to vulnerabilities that malicious characters can exploit. Finally, legacy systems that are not properly decommissioned can also be a target for hackers. If a piece of hardware or appliance for older environments is left off scheduled patching cycles, that hole could be exploited with malware, which could then proliferate across the entire data center.

Most companies already have a risk management foundation to address financial and regulatory risk, but that foundation must be enhanced to include the growing and changing list of regulations that impact digital transformation. A solid risk framework for digital transformation will include the following categories: 

  1. Identify new data and AI-based risks associated with your transformation and fit them into existing risk management framework. Be sure to articulate potential solutions that include risk owners, a risk matrix, resources, and a governance structure.
  2. Operationalize your hypotheses from the previous step. Recommended plans of action could include improving patching and upgrading cycles, educating more of the IT team on cybersecurity best practices, implementing new data access and software controls, and validating AI models. This is also the stage where an organization should start generating and socializing risk reports.
  3. Strengthen: In the final part of the plan, transformation teams scale the new components of the risk plan and reinforce existing frameworks with knowledge gained from the first two steps. Embedding this knowledge into talent management and invoking culture change occurs here. 

As analytics and digital transformation become necessary and commonplace, those companies that achieve the greatest success with digital transformation will be those that use it to meet their strategic goals while identifying and mitigating the risks that come with it.

If you are interested in learning more about how LRS can help you put governance around your data, build AI models and measure them for bias over time, and help you reduce risk from AI to your organization, please contact us to request a meeting.  If you are not using AI yet and don’t know how to get started, our strategic roadmapping services will help identify the gaps in your analytics approach that prevent you from achieving your business goals and recommend the capabilities you need to attain them.

About the author

Steve Cavolick is a Senior Solution Architect with LRS IT Solutions. With over 20 years of experience in enterprise business analytics and information management, Steve is 100% focused on helping customers find value in their data to drive better business outcomes. Using technologies from best-of-breed vendors, he has created solutions for the retail, telco, manufacturing, distribution, financial services, gaming, and insurance industries.