Why Trustworthy AI Isn’t Optional—It’s the Foundation

In the rapidly evolving landscape of generative AI, data security and data privacy are not just compliance requirement - they are the bedrock of trust and innovation. 

After all who would want to waste time with a dodgy and fake AI?

While data security focuses on protecting AI models and datasets from breaches, tampering, or unauthorised access (through measures like encryption, access controls, and secure APIs), data privacy ensures that the data powering these models is collected, processed, and used ethically and legally. This distinction becomes particularly critical in grounding techniques, where AI models are anchored to external knowledge bases, APIs, or real-time data sources. That is what grounding is. Loosely speaking grounding is connecting your database to AI. Without robust security, grounded AI systems risk exposing sensitive data or being manipulated through techniques like prompt injection or data poisoning. Without privacy safeguards, they may inadvertently violate regulations like GDPR or CCPA by misusing or retaining personal data embedded in their knowledge sources.

For data architects and AI engineers, grounding introduces unique challenges. When an AI model queries external databases, APIs, or data lakes, every interaction must be both secured (e.g., using TLS 1.3, OAuth 2.0, or zero-trust architectures) and privacy-preserving (e.g., via differential privacy, federated learning, or data anonymization). For example, if you’re grounding a large language model (LLM) in a relational database or Delta Lake, you MUST ensure that:

Security: The connection is encrypted, access is role-based (e.g., RBAC in Snowflake or Azure Synapse), and queries are logged for audits.

Privacy: The underlying data is scrambled, anonymised or pseudonymized, and the model only retrieves data it’s authorised to use—aligning with the principle of least privilege. Tools like Python’s ‘faker’ or SQL’s dynamic data masking can help strip PII from responses, your grounding frameworks MUST enforce strict data access policies.

The Future: Trustworthy Grounding in AI

The future of AI hinges on trustworthy grounding - where models don’t just perform well but also respect data sovereignty and user consent. As you design AI systems that interact with databases, data lakes, or lakehouses, prioritise :

Privacy-by-Design: Embed consent checks into API calls and ensure data minimization.

Security-by-Default: Encrypt vectors in your vector databases and enforce strict access controls.

If you’re experimenting with grounding, start by auditing your data sources: Are they secure? Are they privacy-compliant? The answers will define whether your AI is not just smart, but also trustworthy.