AI fever continues to sweep through organisations across the globe and innovation in the space is accelerating faster than most governance frameworks can evolve. But according to Gartner’s recent AI and Privacy report, the answer isn’t to build new bureaucracies from scratch; it’s to embed AI governance into the privacy programmes you already have. 

This approach is faster, more pragmatic and builds on a foundation you’ve already laid.  

Here is your 3-step plan to make it happen. 

1. Evolve Your PIAs into A(I)IAs 

You already have a process for Privacy Impact Assessments (PIAs). This is your single most valuable governance tool. Instead of inventing a new “AI Review Board” that exists in a silo, simply expand your existing PIAs to include AI-specific risks. 

Gartner refers to these as “AI and Privacy Impact Assessments”. This evolution means adding a new section to your current review process that asks critical, AI-specific questions: 

• Data lineage: What data was this model trained on? Where did it come from? 

• Purpose limitation: What is the specific, limited, and defined purpose of this AI model? 

• Data minimisation: Does this model really need to access identifiable data, or can it function on de-identified or synthetic data? 

• Bias & risk: What is the process for monitoring and remediating model bias, drift, or “hallucinations”? 

• Lifecycle: How will the data and the model itself be securely managed and, eventually, destroyed? 

By baking these questions into a process your business units already understand, you make AI governance a feature of innovation, not a roadblock. 

2. Apply Privacy by Design (PbD) principles

The 7 Privacy by Design (PbD) principles, highlighted in the Gartner report, are the perfect framework for this new A(I)IA. They move your governance from a simple checklist to a true design philosophy. 

Instead of just keeping the principles on a poster, turn them into a scorecard for every new AI project. Two principles, in particular, are non-negotiable for AI: 

1. Privacy as the default setting: This must become an architectural mandate. Is the system built to expose data to the model by default, or is it built to protect data from the model by default? 

2. Full functionality: This is the principle that builds trust with your business units. It’s the promise that security and privacy won’t break the application or make it unusably slow 

If a proposed AI project scores poorly on these principles, it goes back to the architects. 

3. Enforce governance with technology, not just policy

Here is the most important shift: A policy in a binder has never stopped a data breach. A checklist doesn’t stop an AI model from “learning” and leaking sensitive information. 

The best governance is automated, embedded, and enforced by the infrastructure itself. 

This is where Privacy-Enhancing Technologies (PETs) become your primary governance tool. Instead of just asking a developer if data is protected, you can use technology that guarantees it. 

The ultimate expression of this is using Fully Homomorphic Encryption (FHE) for your AI workloads. When your AI model processes data that is never decrypted, your governance policy (“this model must not see raw PII”) becomes a cryptographic, quantum-secure certainty. 

This turns your governance from a reactive, paper-based audit function into a proactive, infrastructure-enforced reality. 

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At Optalysys we’re developing the future of secure AI through pioneering the use of optical computing to accelerate Fully Homomorphic Encryption. Get in touch with us to find out how we can accelerate your FHE use case →