As global data protection standards evolve, staying compliant is tougher than ever. Chief Information Officers (CIOs), legal teams, and compliance professionals face mounting pressure to make varied datasets easy to sanitize, access, and share across different functional domains. However, they must also fully adhere to relevant laws and regulations.
For most public and private sector organizations, the problem isn’t acquiring data—which is now internally and commercially available at scale—it’s staying compliant with increasingly complex and often conflicting data sovereignty laws.
If you’re responsible for data management at your organization, you’re likely aware that existing data governance solutions are limited when it comes to balancing data utility and efficiency with compliance. This is where AI can make a significant impact, offering a scalable, automated approach in an unpredictable regulatory landscape.
The complexities of data compliance
Data compliance is now a constant challenge, requiring organizations to untangle complex regulations, adapt to geopolitics, and maintain manual compliance workflows.
Interpreting varied data regulations
There is no universal framework for governing data movement globally. Countries and states have different laws dictating citizen data rights and how or whether information can be lawfully transferred beyond a jurisdiction’s borders.
Within this global patchwork of regulations, you’re responsible for navigating legal complications when laws overlap or contradict each other. This can be tricky to resolve until new agreements are created For example, the EU-US Data Privacy Framework aimed to resolve conflicting standards between the European Union and the US when it was enacted in 2023. However, some experts predict that the policy still has legal grey areas that are likely to require further revisions.
Navigating geopolitical tensions
Data sovereignty is closely linked with geopolitics, with data protection laws reflecting the values and agendas of the governments creating them. For example, the EU’s General Data Protection Regulation (GDPR) aims to balance individual privacy rights with economic interests, while US laws are defined more by free data flows promoting market activity. Geopolitical tensions and data sovereignty disputes can derail compliance strategies, especially with factors like Trump’s second presidency adding another layer of uncertainty.
Operating with manual compliance processes
Even if your organization prioritizes data protection laws, it may still struggle with compliance due to inefficient processes and infrastructure. At present, compliance efforts largely rely on manual strategies like data protection impact assessments (DPIA) to uncover legal risks and mitigation tactics for a catalog of use cases.
Taking action is also clunky and laborious. Compliance teams may need to rewrite documents to protect sensitive information on a case-by-case basis. Developers may be required to query, cleanse, and transfer data manually anytime it’s requested from a data-sovereign region. There’s also the matter of keeping up with regulatory changes and new datasets. For example, intelligence organizations in the US may violate laws like the Foreign Intelligence Surveillance Act (FISA) if they inadvertently acquire citizen data.
Ultimately, organizations are working with fragmented, inflexible, and inefficient data governance systems that rely heavily on person-hours to ensure compliance. Thankfully, AI has the potential to transform this dysfunctional status quo.
AI: the future of data compliance
We know that data sovereignty laws are here to stay and that existing data governance solutions aren’t a scalable approach to compliance. This is where artificial intelligence (AI) comes in: by training large language models (LLMs) on data privacy laws, legal documents, and other descriptive inputs, AI can interpret compliance requirements and compare them to your dataflows.
With natural language processing (NLP), models can continuously apply this knowledge to new data of varied formats. Crucially, this type of AI doesn’t need to be trained on user data to work effectively, keeping your information safe and secure. Let’s take a closer look at how AI can streamline compliance without sacrificing comprehensive regulatory oversight:
Legislation tracking
By training models on the latest data protection policies, AI can stay up-to-date with new and evolving regulations. This unburdens legal teams, CIOs, and other data administrators from staying updated manually—an overwhelming task, considering that over 80% of countries have enacted or are drafting data laws. AI can also interpret which regulations are relevant to your data-sharing activities and where conflicting standards could pose legal risks.
Scalable compliance monitoring
Rather than manually auditing thousands of documents, AI can review large datasets across different formats—like spreadsheets, reports, and commercially available data—and compare them to legislative requirements. Admins can then easily identify potential violations and review new datasets without significant human effort.
Informed compliance recommendations
Based on your organization’s dataflows and the laws relevant to them, AI can recommend actions to stay compliant. This could mean suggesting a redaction rule for categories of personally identifiable information that, if shared with another user, could violate applicable data protection laws.
AI for guidance, rules for implementation
With AI, organizations can evaluate data compliance and enforce controls with greater speed and accuracy. However, AI solutions shouldn’t be used to make compliance decisions autonomously.
Why? AI models are often opaque, making it hard to trace how they generate outputs. This raises regulatory concerns, as some laws require you to justify and report on data handling decisions. Models may also hallucinate when permitted to apply actions, which could result in unreliable or harmful outcomes.
According to Mike Anderson, Intlabs CTO, AI is best suited for analyzing data regulations and suggesting redaction rules, saving implementation for deterministic algorithms.
“Large language models are really good at identifying sensitive information in unstructured content,” he says. “What they’re not very good at is taking prescriptive rules around data sanitization and actually applying them. They’ll accidentally make things up, get things wrong, or apply the rule inconsistently.”
By combining AI’s analytical power with human oversight and rule-based enforcement, organizations can strike the right balance between automation and accountability.
Moving forward in your compliance strategy
If you’re unsure how AI fits into your data governance process, or even where you stand in terms of legal compliance, here are some actionable steps to consider:
- Audit your data activities. Document the types of data you store, who can access it, and where it’s shared.
- Identify relevant laws and regulations. This depends on factors like your operating areas, user locations, cloud service providers, and industry. For example, a US intelligence agency has different compliance obligations than a financial institution.
- Evaluate your existing compliance process. Does your organization have consistent processes to ensure data regulation compliance? How effective are they and what are their operational costs?
- Invest in a scalable compliance solution. Innovative solutions like ORIGIN by Intlabs are helping address manual compliance efforts, using AI to compare current regulations with your dataflows.
“We’ve taken AI and harnessed what it’s very good at: analyzing data regulations and determining what redaction rules you need to make your data activities compliant. Then, ORIGIN can apply those rules prescriptively. It’s an effective tool that addresses an urgent need we’ve heard identified by senior members of the government and other organizations handling sensitive data.” — Mike Anderson, Intlabs CTO
The bottom line
Data compliance is no longer a static obligation. Regulations are constantly evolving alongside unpredictable geopolitical factors. While some organizations have yet to establish a strong compliance culture, others are simply struggling due to the lack of effective data governance solutions.
The EU has led the charge in enforcing strict data sovereignty laws, and other countries are following suit. Organizations that fail to adapt risk falling behind as compliance standards become more rigorous. Through innovations like ORIGIN, AI offers a powerful and scalable way to streamline compliance.
Book a call with our team to learn how AI can transform your data governance strategy.