generative AI, GenAI

We’re only just beginning to understand the transformative power of GenAI; businesses are already using it to drive better, more efficient processes throughout the organization, from marketing to finance to IT. However, one little publicized application of GenAI is its ability to automate and simplify compliance, adhering to a growing list of data privacy requirements that vary from state-to-state and country-to-country. 

Two good examples of the data privacy regulations that global organizations must comply with include the EU’s GDPR, one of the strictest and most well-enforced privacy regulations in the world, and California’s CCPA – modeled on GDPR – one of the toughest data privacy laws in the U.S.

GDPR continues to evolve, as the EU added new regulatory developments to both strengthen and streamline data protections in July 2023. The “new and improved” GDPR ensures procedural rights for individuals and businesses involved in enforcement actions or investigations and promises to facilitate cooperation and information sharing among member states, amongst other additions.  

The U.S. is slowly following the EU’s lead with state-level privacy laws in California, as well as Colorado, Connecticut, Utah and Virginia, which have already implemented their own “GDPR-like” statutes. 

However, this patchwork of laws makes it very difficult for organizations’ compliance teams to keep up with new and changing regulations and laws, which puts them at risk for non-compliance and the potential for legal action and significant fines. For example, companies that violate the CCPA can be fined up to $2,500 and even more for intentional volitions, at $7,500 per infraction. In February 2024, DoorDash agreed to pay $375,000 to resolve allegations that it had sold personal customer information without providing notice or the opportunity to opt-out of the CCPA and the California Online Privacy Protection Act. 

Transforming Compliance With GenAI.

Among all the useful ways GenAI can be applied to improve efficiencies in the workplace, it can also be a powerful tool that enables organizations to keep up with the ever-evolving data privacy regulations ecosystem, mitigating the risk of non-compliance and the potential for lawsuits and/or fines. 

Four ways GenAI can improve the data privacy compliance process include:

  • Automating and monitoring changes in global data privacy laws and regulations: Specialized software or services that provide automated alerts about changes in legislation as they occur are now available. They can be programmed to track specific keywords or jurisdictions, then notify companies of updates or amendments to data privacy laws. This requires continuous updates and regular review to incorporate relevant updates into GenAI’s training data to ensure it remains up-to-date with the latest regulations.
  • Assisting in drafting data privacy policies: GenAI can analyze existing data privacy policies, regulations, and best practices to generate a draft policy to fit the specific needs of an organization based on its industry, policies and business strategy. As with all compliance-related processes, this requires human intervention by legal experts to make sure the policies are suitable for specific industries, applications and organizational objectives. GenAI can consider factors such as the type of data collected, the jurisdiction in which the organization operates, and any industry-specific regulations. It can also conduct a compliance check against relevant laws and regulations such as GDPR, CCPA, or HIPAA and highlight areas where the drafted policy may fall short and where adjustments are needed.
  • Categorizing sensitive data: A breach could potentially reveal consumers’ personal data which is why categorizing personally identifiable data (PII) is so important. Incorporating privacy considerations into the design of AI systems from the start can ensure PII is handled responsibly throughout the data lifecycle. GenAI can first identify PII within the dataset. This can include names, social security numbers, email addresses, phone numbers, biometric data, and more. Natural language processing (NLP) can be used to parse text data and identify entities that match known PII patterns. Once PII data is identified, GenAI can apply techniques such as masking, encryption, redaction, or anonymization to protect sensitive information while still allowing for analysis. This ensures that the data remains useful for analysis purposes while protecting customer PII and minimizing privacy risks.
  • Automating the data breach response process: GenAI can automate certain aspects of the data breach and cybersecurity response process, such as incident detection, notification, and containment. One example of this is using machine learning algorithms to detect anomalous behavior the company’s network, automatically triggering alerts when potential breaches are detected. GenAI is also useful in helping to ensure compliance with data breach notification requirements in GDPR, HIPAA, and CCPA. It can help organizations understand their legal obligations, determine the appropriate timeline for notifying affected parties, and even be used to draft notification letters that meet regulatory requirements.

The Automated Future of Compliance 

While GenAI makes processes more efficient, scalable and less of a heavy lift, it does require human collaboration to ensure effectiveness and accuracy. Once that is understood, organizations should not hesitate to leverage GenAI’s vast capabilities to manage data privacy compliance, automating many facets and streamlining what can be a highly complex process. Not only can GenAI optimize data privacy compliance, but it can also minimize and mitigate risks, improve business processes, and build trust with customers, partners and other stakeholders.