AI’s influence, data compliance, and non-production vulnerabilities give businesses ample reasons to evolve their data governance procedures
This article was originally published on the Delphix website
here February 21, 2024.
As businesses continue to amass vast amounts of data, the need for robust data governance procedures has become more critical than ever. Examining data governance procedures has long been a crucial practice for businesses that collect data because it ensures that collected data is managed, stored, and utilized in a secure, compliant, and efficient manner. It also enhances data quality, risk mitigation, and better decision making.
Yet data governance practices aren’t always in lock-step with data collection practices. According to a 2021 Drexel University study, only 64% of surveyed companies had data governance programs in place. Moreover, only 43% of organizations had at least a defined data governance program, with less using metrics and dashboards and even fewer having a continuous improvement program in place.
Over the past several years, businesses across industries have witnessed rises in data privacy laws, attacks on non-production environments, and artificial intelligence (AI) influence. These ongoing trends affect industries in innumerable ways— but they also present compelling reasons for businesses to scrutinize and evolve their data governance procedures.
1. The Soaring Importance of Data Privacy Compliance
The rise of data breaches and growing number of regulations across the world have both contributed to data privacy compliance’s meteoric rise in significance over the past decade. As of December 2021, 137 countries around the world had instituted data privacy and protection legislation, according to the United Nations Conference on Trade and Development. These laws include the European Union’s General Data Protection Regulation (GDPR), Brazil’s General Data Protection Law (LGPD), the United States’ Gramm-Leach-Bliley Act (GLBA) for financial institutions, and the California Consumer Privacy Act (CCPA).
Ensuring compliance with these regulations is a key reason to evolve data governance practices, since non-compliance can result in hefty fines and the erosion of customer trust. For instance, each GDPR violation can cost up to 4 percent of a company’s annual global revenues or €20 million (about $22 million)— whichever is highest. GLBA carries steep individual penalties for indicted directors within infringing companies, such as fines of up to $10,000 and up to 10 years of jail time.
Enterprise software developers play a pivotal role in implementing robust security measures, encryption protocols, and access controls to safeguard user information. Therefore, an effective data governance plan provides a structured framework for handling sensitive data, which ensures compliance with privacy regulations. By prioritizing data privacy in their development processes, enterprises can build a resilient foundation for long-term success.
2. The Hidden Liability in Non-Production Environments
Much attention is rightfully directed towards securing live production environments— but the data within non-production environments often remains a key neglected vulnerability. These non-production environments, such as testing and development databases, can harbor sensitive information and create significant vulnerabilities if not adequately protected. Moreover, non-production environments constitute up to 80% of an enterprise’s attack surface area in terms of data, endpoints, and privileged user accounts, according to Delphix data— making them a large target for hackers, too.
Inadequate data governance standards in non-production environments poses a substantial risk. Unauthorized access, data leaks, and mishandling of information in these environments can result in dire consequences. Enterprise software developers must scrutinize and fortify these often overlooked areas to ensure end-to-end data protection.
Implementing stringent access controls, anonymizing sensitive data, and regularly auditing non-production environments are essential steps in mitigating this hidden liability. By doing so, enterprises can preemptively address potential security breaches and fortify their overall data governance posture.
3. The Ascendance of AI in Shaping Data Governance
As AI’s influence continues to sweep across industries, its impact on data governance has become undeniable. AI presents new challenges in safeguarding data, but it also offers innovative solutions to streamline and enhance data governance procedures. For instance, tasks such as data classification, privacy compliance checks, and anomaly detection, have traditionally been time-consuming and error-prone. But AI-powered tools and automation now allows them to be executed far more swiftly and accurately than ever before.
Enterprise software developers need to harness the power of AI to augment their data governance capabilities. By integrating AI-driven solutions into their data governance frameworks, enterprises can achieve unparalleled efficiency. From predictive analytics that identify potential risks to automated compliance checks that adapt to evolving regulations, AI empowers developers to create dynamic, responsive, and resilient data governance systems.
Key Data Governance Considerations for Business Leaders
The triad of data privacy compliance, the hidden liabilities within non-production environments, and the transformative influence of AI have made it imperative for business leaders to examine and enhance their data governance procedures. Embracing these challenges head-on not only safeguards the integrity of an organization's data but also positions it as a data-driven leader in an era where information is paramount.
As enterprises embark on the journey to examine and evolve their data governance procedures, business leaders should keep three key considerations in mind:
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Holistic Approach: Data governance should be treated as a holistic endeavor, encompassing both production and non-production environments. A comprehensive strategy ensures that data is safeguarded at every stage of its lifecycle.
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Education and Training: Equip your teams with the knowledge and skills needed to navigate the evolving landscape of data governance. Regular training on data protection regulations, emerging technologies, and best practices is essential for maintaining a resilient data governance framework.
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Continuous Evaluation: Data governance is not a one-time effort but an ongoing process. Regularly evaluate and update procedures to adapt to changing business landscapes, regulatory environments, and technological advancements.
Enterprises can no longer afford to overlook the importance of robust data governance procedures. The time to act on revamping data governance procedures is now, as data governance becomes not just a compliance necessity but a strategic imperative for enterprises navigating the complexities of the digital landscape.
By offering a combination of data masking, subsetting, versioning, auditing, access control, virtualization, and integration capabilities, the Delphix Data for DevOps Platform helps organizations address data governance challenges in non-production environments effectively. This ensures that data remains protected, compliant, and easily manageable throughout its lifecycle. Click here to learn more.