Governance, security & operational data concerns in the AI era
The way we work is changing. With the world’s richest man suggesting you use AI to check your contracts and the UK government reporting that AI soon could do work coaching and life admin; artificial intelligence is quickly taking over every area of our lives. However, AI is only as reliable, secure and ethical as the data and systems behind it. So, before you look at a rollout in your own company, what are the governance, security & operational data concerns in the AI era for your business?
Data foundations in an AI-driven workplace
All AI systems rely on clean, well-governed, accessible and secure data. Without all of these elements, responses are wrong, unhelpful or corrupted. Now that all sectors, from legal to e-commerce, are moving towards AI-augmented working, the pressure is on for database architecture experts like our team here at Wirebox to get it right the first time.
AI governance
AI-centric data governance means that metadata, lineage tracking and schema evolution are even more important. You want to embed, from day one, database development practices that support explainability, like version-controlled database schemas, automated data lineage and impact analysis across all your environments. Then, to keep it secure, you’ll need to have built-in policy enforcement using database-level constraints. Just a basic level of purpose-built governance can reduce AI model failures.
AI security
Now with more (and different) access patterns, security needs to evolve too. AI means more queries, more endpoints and more automated agents without a standard or formulaic behaviour profile. There are also new security vectors like prompt injection attacks and vishing to contend with, or automated AI agents with overly broad database permissions making unauthorised disclosures. Then you need to think about the risks of model training data leakage through unencrypted pipelines and how you’ll standardise access control and row-level security. Thankfully, there are a number of systems that can help you secure vulnerabilities, and we can advise you on best practices.
Operational AI
Since AI introduces high-volume, often unpredictable database usage patterns; and you can’t get around it by building in latency because of the need for real-time data to feed AI systems, you need event-driven architectures with hybrid transactional/analytical processing (HTAP), flexible schema and edge processing. That’s the only way to prevent heavy load performance dips and handle AI processing at scale. As we move towards autonomous database systems that are self-tuning and self-optimising, embedded AI governance policies at the database layer and real-time compliance monitoring should already be integrated into pipelines to reduce operational drag.
If you’re ready to realise the promise of AI, you need to start with strong governance, tight security and robust operational data practices from day dot. Working with a partner like Wirebox on your database development architecture and policies is a strategic backbone that will empower safe and effective AI.
Ready to start your AI journey? Get in touch and we’ll help you kick the tyres on what you’ve got now to see if it’s fit for purpose in the AI era.
