Overview
The urgency of the evolving data protection landscape is palpable. Traditionally, businesses have relied on data protection tactics driven by regulations. These measures focus on compliance with industry-specific requirements such as HIPAA for healthcare, PCI-DSS for financial services, or GDPR for organizations operating in the EU. However, the rapid pace of technological advancement, particularly in AI, is outpacing this traditional focus, necessitating a new approach.
As the world embraces emerging technologies like Artificial Intelligence (AI), traditional data protection tactics are becoming increasingly inadequate. A substantial 82% of C-suite and other senior executives consider scaling AI or generative AI use cases to create business value a top organizational priority. Furthermore, 45% of survey respondents identified data integration and compliance as the primary challenge in achieving AI readiness.[1] A narrow focus on "regulated data" often overlooks the broader implications of new data uses and emerging technologies within organizations. Depending on how the data is sliced, its intended use, and other factors, different regulations and combinations of regulations may apply.
Today, businesses can move beyond simply complying with regulations and embrace a proactive, technology-agnostic, data protection strategy. Companies that recognize that regulatory compliance is just one piece of their data puzzle will gain a competitive edge by implementing protection measures that scale at the pace of business. This proactive data strategy approach will be essential to ensuring long-term success and increased maturity of data management in the future, and to staying ahead of the curve in the evolving data protection landscape.
Evolving Data Protection Landscape
AI technologies can process vast amounts of data at incredible speeds, enabling businesses to gain previously slow or unattainable insights. However, this also means that data is being used in ways that regulations were not originally designed to address.
AI presents unique challenges for data protection:
- Machine learning algorithms require large datasets to function effectively, often involving collecting and processing personal information at scale.
- AI can make inferences about individuals based on seemingly innocuous data, potentially leading to unexpected privacy violations.
- Regulations are often slow to adapt to new technologies, leaving businesses in a constant game of catch-up.
This lag between technology and regulation underscores the need for businesses to take a more proactive approach to data protection. Forward-looking companies develop strategies anticipating future risks and opportunities rather than relying solely on regulations to guide their data protection efforts.
Companies can no longer afford to treat data protection as an afterthought or react to data challenges after they become headlining news stories. Instead, data protection benefits from being integrated into every aspect of their operations.
Moving from Data Tactics to Data Strategies
To effectively manage data risk, businesses need to think beyond compliance. For example, by considering the value of data anonymization and other methods, organizations can make data less risky to use but more valuable for gaining insights. While anonymizing data can significantly reduce the risk of privacy violations, it is not a one-size-fits-all solution.
Lauren Kaufman, Sr. Principal Product Manager for Clean Rooms at LiveRamp, advises clients to “carefully consider how selected privacy-preserving techniques will impact an organization’s ability to extract value from collected data while maintaining compliance. Advanced firms with use cases where granular or statistically significant analytical results are required often achieve this balance through some combination of pseudonymization and other privacy-enhancing techniques rather than anonymization. This approach helps to ensure that as regulations change, the firm’s technical strategy for managing data processing flows will remain robust while providing useful analytical insight.”
Businesses can reduce data risk by knowing what data they have, protecting it properly, setting clear policies, and training employees on data safety. This complete approach helps keep data secure from collection to deletion.
Another critical aspect of moving from data tactics to data strategies is understanding the relationship between data risk and business risk. Data breaches and privacy violations can have significant financial and reputational consequences for businesses, resulting in costly fines, legal fees, and a loss of customer trust. By proactively managing data risk, businesses can reduce the likelihood of these negative outcomes and protect their bottom line. According to IBM's Cost of Data Breach Report 2024, the average cost of a data breach reached a new all-time high of USD 4.88 million in 2024. This represents nearly a 10% increase from the 2023 cost of USD 4.45 million. [2]
"The era of reactive data protection is over. Organizations can either be proactive in introducing comprehensive strategies or risk being left behind with the rapid evolution of AI. Without robust compliance, privacy practices, and objective audits, the risk of sensitive data finding its way into models increases dramatically. Even a single instance of undetected private information in training data could propagate in a model, potentially exposing personal details in countless outputs. This critical moment demands that organizations not only implement robust data protection and governance frameworks but also subject their data and models to rigorous third-party audits and certification processes. These audits ensure compliance, validate data quality, and verify the ethical implementation of AI systems. Such practices are not just best practices – they are essential foundations for building trustworthy, impactful, and ethical AI systems that will shape our future while safeguarding individual privacy." - Timothy Nobles, Chief Commercial Officer, Integral
In an age where consumers are increasingly concerned about privacy, businesses prioritizing data protection will be better positioned to build strong, long-lasting customer relationships.
Summary
Traditional data protection tactics are no longer sufficient. As AI and other emerging technologies advance, businesses that evolve their data protection strategies to ensure compliance and maintain consumer trust, gain a competitive edge. This means moving from reactive data tactics to proactive data strategies considering data and business risks. A holistic approach to data protection involves complying with regulations, managing data risk, and considering the broader business implications of data protection, such as maintaining consumer trust. This comprehensive approach enables businesses to better safeguard their data, mitigate risks, and position themselves for success in an increasingly complex data protection landscape.
About Integral
Integral enables companies to safely leverage sensitive regulated data at unprecedented speeds by automating the data de-identification and compliance certification process, allowing our customers to stay agile and iteratively drive outcomes. www.useintegral.com
[1] MIT Technology Review, May 2024 Report
[2] IBM's Cost of a Data Breach Report 2024
