Key risk governance practices for optimal data security
The rapid rise of new technologies has caused data storage to surpass more and more organizational and cloud boundaries. There are understandable concerns about the security challenges this could pose. These challenges are only made more complex by the volume of structured and unstructured data that is flowing in from internal and external sources.
As organizations increasingly operate on data-driven insights, they have to tackle the twin challenge of ensuring robust data security and managing the trust of all stakeholders. The security team can no longer just technically secure data for its organization. It is equally critical to provide a transparent oversight over the organization’s data assets across their entire lifecycle of acquisition, storage, access, archival, and ultimately, disposal.
Here are some elements that will be key to establishing robust risk governance:
a. Quantification of data risk
The organization needs to properly strategize its responses to different data exposure scenarios because the repercussions of exposing certain data elements versus others can be vastly different. It is necessary for the organization to develop the ability to quantify the risks accurately, thus paving the way for it to understand the potential damage and develop appropriate responses.
b. Regulatory compliance
From cybersecurity standards to policies around articulating data handling processes and providing transparent updates, the organization needs to clearly understand all of the compliance standards relevant to it.
In addition, it needs to make sure its regulatory readiness processes extend to not just internal compliance and risk management but also to compliance with regulations like General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA). This is especially important for heavily regulated industries such as banking, financial services, and technology, where many of the organizations’ business models are rooted in customer data.
Managing the data risk
To support the two elements above, the organization needs to undertake a sustained effort to seamlessly map out its data handling process across the stages of acquisition, storage, transformation, transport, archival, and disposal.
While quite a few organizations are starting to leverage advanced analytics and cognitive technologies to predict data risks and breaches, the transition towards unified, enterprise-wide data risk management processes is still ongoing. However, many business leaders across industries have already invested in efforts to overcome existing data silos—a key step in the right direction as consolidated information will be conducive to consistent security measures.
Key considerations for data risk management
As the organization looks to identify potential gaps in its data risk management practices, here are some key considerations it should keep in mind:
1. Managing data ownership
Given the complexity of the data storage and processing infrastructure required today, the organization needs to be able to accurately determine the ownership of data assets. This can be achieved through a system of record, but it can also be assigned to an individual who will own this on behalf of their business unit or data division.
2. Data classification and access alignment
Having understood the data it has and the necessary ownership structure, the organization needs to classify and align its data in terms of who and what can access it as well as how it can be consumed. Successfully done, data classification and access alignment will lead to a clear focus on processes that transform data critical to the organization.
3. Securing the data
After understanding the data in an organizational context, it is important to document the major gaps in data security to mitigate and eliminate potential risks. The organization needs to introduce the right capabilities, talents, and processes to eliminate internal silos, ensure data control, and prevent external threats.
This should ideally be achieved through a comprehensive security mechanism that encompasses everything from access control to encryption and hashing to data monitoring for pre-empting breaches and more. What’s more, the organization needs to secure its data in both production and non-production environments such as UAT, backup, archives, as well as any data in transit (e.g., accessed through a network or shared to a third party via an interface).
4. Identifying potential risks and breaches in data monetization
To effectively monetize its data, the organization should identify key attributes and determine their values in relation to specific business use cases. It must also assess the different consequences of data sharing in any form—even at an aggregate level—to safeguard information and eliminate potential breaches of regulations.
5. Adhering to a risk assessment framework
The cornerstone of a robust governance network lies in the organization’s capabilities to identify and optimize the entire data lifecycle. A reliable risk assessment framework should include the following:
a. Data management model
b. Organizational policies and processes for data governance and compliance
c. A secure process for data acquisition and distribution
d. Application of business intelligence, AI, and machine learning models to provide predictive insights on data and its integrity
e. Clearly documented data model, reference architecture, as well as standardized integration and access management with optimal interoperability and processing
f. Rigorous data classification, access control, and technical security definition
Data has long cemented its status as one of the key differentiators that fuel enterprise digital transformations today. While it promises, and has delivered, immense insights and benefits, the organization can face serious consequences if it fails to manage, access, and utilize data with care. Rigorous risk governance and management practices are more needed than ever to help the organization achieve the optimal level of data security.