Avoiding Legal Pitfalls Through Savvy Data Governance

The numerous hazards data governance is designed to circumvent are multifaceted. They include not only legal snafus, but also regulatory repercussions, nontrivial fees and penalty payments, loss of reputation, customer churn, and a marked inability to remain in business.

The paradox is that these undesirable results are juxtaposed with the limitless business value gained from disseminating organizational knowledge among users, departments, and organizations. Doing so fosters a spirit of collaboration and enablement—whether that’s for enterprise search, analytics, operational systems, or almost any other application. After all, KM is predicated on users accessing this information in order to profit from it.

Data governance ensures that such access is timely, necessary, and sanctioned by the appropriate parties to support the specific request. It encompasses facets of regulatory compliance, data privacy, and data security—to say nothing of data intelligence, data modeling, data quality, and lifecycle management. Here are some of the several dimensions of effective data governance:

♦ Classifications: Classifications are a corollary to data discovery, in which organizations identify what data they have, where it is, and what’s sensitive enough to require protection. They then tag it accordingly. Taxonomies and business glossaries are essential to provide accurate classifications.

♦ Policies: Many governance systems treat data governance policies as code, stored in source systems, for accessing content. Policies also involve organizations’ stipulations about how to safeguard particular types of content.

♦ Controls: Optimal governance solutions enable sharing knowledge between parties while simply restricting access to sensitive, or regulatory applicable, elements. For example, sensitive information may be obfuscated or redacted from a document to adhere to governance policies. Controls include masking, tokenization, encryption, redactions, and request denials.

♦ Language model governance: Increasingly, the need to monitor, govern, and control what information is accessed by language models—in real time—is important for generative AI deployments, particularly those involving retrieval-augmented generation (RAG).

“Today I was at a meeting with a law firm talking to them about an AI solution,” recounted Mind-Alliance CEO David Kamien. “They said the only way it would fly is if it had access control at a level that enabled them to uphold legal guidance rules and ethical roles separating different parts of their own law firm.”

Such expectations for data governance apply to data access in general. Moreover, they’re on their way to becoming a normative part of contemporary business practices.

Regulatory Compliance

Adhering to regulatory compliance is often the first step in avoiding legal pitfalls pertaining to sharing enterprise
knowledge. Regulations encompass vertical-specific mandates such as the Health Insurance Portability Accountability Act (HIPAA), data privacy standards such as the General Data Protection Regulation (GDPR), and security standards such as System and Organization Controls 2 (SOC 2). Data governance solutions can scan and classify organizations’ sensitive data according to its relevance to a particular regulation. Classifiers find specific data types of interest to specific regulatory entities. The discovery process is often automated via a combination of data profiling techniques, machine learning, and heuristics. However, regular expressions and bespoke business rules supplied by organizations are particularly useful for finding data with regulatory requirements.