Please note: This post was written by Highlander prior to their rebrand to FluidOne Business IT - Sheffield.
As artificial intelligence continues rapid mainstream adoption, proper data governance becomes pivotal – both to train high-quality AI models and to safeguard sensitive data AI tools may ingest or generate.
Take Microsoft’s innovative AI companion, Copilot. Having previously only been available to enterprise customers, it’s recently become an add-on for all Microsoft 365 users. Copilot helps create documents, write content, prepare presentations or more quickly locate files – effectively creating new efficiencies for your team and saving significant effort. Based on open-source training data spanning decades, it provides experience-driven recommendations aligned to industry best practices.
Of course, these issues aren’t exclusive to Copilot. Without governance, AI shortcuts cripple outcomes across the board, and issues around data access and integrity proliferate. Here are some key areas to monitor.
Curating Clean Training Data
Machine learning algorithms that power AI rely on vast training datasets (or even your own internal data, as with Copilot) to learn patterns needed for classification, prediction, personalisation, and automation capabilities. However, gaps, inaccuracies, duplication, or irrelevant samples in data pollute downstream AI models, mistaking noise for signal and skewing decisions. As a result, all data sources must be regularly checked and labelled accurately at scale before feeding systems.
Anonymising Personal Information
AI applications processing personally identifiable customer data including medical, financial or behavioural records must anonymise information through encryption or metadata removal to tokenise insights away from individuals. Protecting identities while still leveraging general trends warrants meticulous protocols: synthetic data generation through algorithms maintains confidentiality, while non-contextual models prevent reverse lookups.
Instituting Human Oversight
It’s important to recognise that employing AI in any form does not remove human or corporate accountability. While AI promises to automate tasks at unprecedented scale, putting blind faith in algorithms without ongoing human supervision is risky business. After all, AI ingests messy real-world data, so there’s always a chance something ‘off’ will be pulled through. To counter this relatively low but still very plausible risk, it’s recommended that you regularly review AI-influenced output to safeguard fairness and prevent consequential mistakes from creeping in.
Managing Access Controls
As more employees access data and interact through AI interfaces, their access must be monitored using robust identity and management policies tailored to usage. Like it or not, AI quickly becomes a vehicle for people to inadvertently (or even maliciously) access information buried inside folders and files that they have no business accessing. By employing and enforcing identity controls and access permissions around data sources, you can effectively ensure data protection while support effective AI use.
The upshot here is that with scale comes accountability. Data-centric AI governance establishes reliability in the face of complexity, and ensures businesses make the most from innovative tools such as Microsoft Copilot. By orchestrating people, processes and technologies, powerful AI tools and analytics can effectively be aligned with responsible oversight. For a no-obligation demo and discovery session covering all aspects of Copilot – including data security and governance considerations – get in touch with our experts today.
