Compliance in the age of AI: What DBAs need to look out for

For a long time, databases were regarded as reliable but comparatively static systems: store, query, deliver results. But with the triumph of artificial intelligence, architecture and operation are changing rapidly. AI-supported query optimization and indexing as well as integrated machine learning models for predictive analyses are increasingly becoming standard. As a result, governance and compliance requirements are growing - and with them the remit of database administrators. From Redgate's point of view, they should take four measures to take advantage of the new AI functions without falling into regulatory pitfalls:

1. Establish data governance. A good framework for the data is the basis of any successful AI strategy. DBAs should define guidelines that ensure data quality, integrity and security. This includes clearly defining data responsibilities, regulating access rights and implementing procedures for stringent data lifecycle management. So-called data catalogs facilitate the classification and management of metadata and ensure a transparent data flow. The use of data masking techniques is also essential. They anonymize sensitive information before it is used in development, testing or other environments. Data is modified in such a way that unauthorized persons do not gain access to personal information, but the data can still be used for testing and analysis. This is particularly important in order to comply with regulations such as the GDPR or the US HIPAA.
2. Carry out regular data audits. Audits are as essential for databases as preventive medical check-ups are for health. However, they not only check compliance with regulatory requirements, but also reveal optimization potential. Special data observability tools are particularly helpful here. They make processes transparent and traceable, log every input and output of AI models, uncover possible distortions and ensure that the data used for a model can be traced at any time. Especially in the database context, AI is often perceived as a „black box“. When machine learning models are directly integrated - for predictive analytics or automatic optimization, for example - DBAs must ensure that decisions and results remain explainable. 
3. Consistently secure access. AI not only brings efficiency, but also new threats: prompt injection, data poisoning or the targeted exploitation of vulnerabilities in machine learning models can directly affect databases. Proactive protection concepts and continuous monitoring are therefore more important than ever. Multi-level security concepts are mandatory to prevent unauthorized access in the best possible way. Multi-factor authentication (MFA), role-based access control (RBAC) and regular checks of authorizations ensure that only authorized users with the minimum required rights work on databases. Transparent access logs also make it easier to provide evidence during audits and reduce the risk of costly data breaches. 
4. Automate reporting. If you want to prove compliance, you need complete documentation. DBAs should record in detail where data comes from, how it is processed and how AI models were developed. Automated, continuously updated documentation by the tools used is ideal. At the same time, the regulatory framework for AI is evolving dynamically, which is why DBAs should also seek out exchanges in specialist forums and associations. This is the only way they can adapt new regulations and best practices at an early stage. 

„With the increasing use of artificial intelligence, the profile of database administrators is also changing. Query tuners are becoming specialists who, in addition to their traditional tasks, also have an eye on governance, model monitoring and security. In future, knowledge of machine learning and a deep understanding of data protection guidelines will be just as important as traditional database expertise,“ says Oliver Stein, Managing Director DACH at Redgate. „Clear governance rules are therefore essential. This ranges from role and rights concepts to audit trails for machine-generated query plans and ML models to explainable AI in order to meet regulatory requirements - such as those arising from the GDPR or industry-specific requirements in the financial and healthcare sectors.“

Source: www.red-gate.com/de/

This article originally appeared on m-q.ch - https://www.m-q.ch/de/compliance-im-ki-zeitalter-worauf-dbas-achten-muessen/