A recent Gartner report on AI and privacy states: “using AI is a choice and never an obligation”.  

However, in the current enterprise landscape, the buzz around AI is leading to ever-increasing pressure on teams across organisations to adopt new forms of machine learning.  

The report confirms what security and privacy leaders already know: AI adoption amplifies privacy risk, especially when foundational privacy controls are weak. The challenge is that AI models are data-hungry, and that data is often full of sensitive, identifiable information.  

So how do you innovate responsibly? How do you unlock the value of AI without exposing your customers, and your organisation, to unacceptable risk? It starts by treating privacy as a foundational component, not an add-on.  

Here are five practical steps to bake privacy into your AI operations from day one. 

1. Start with proactive governance (not panic!) 

The rapid rise of AI has many boards demanding new, sweeping AI governance committees. A more pragmatic approach, as suggested by Gartner, is to integrate AI governance into your existing privacy programs. Don’t start from scratch; evolve what you already have.  

Your existing Privacy Impact Assessment (PIA) process is the perfect place to start.  

Evolve it into an “AI and Privacy Impact Assessment” (AIIA). This ensures that before any new AI model is deployed, your privacy and security teams have already asked the hard questions: What data was this model trained on? What is the data lineage? How will we monitor it for bias and drift?  

By embedding AI risk into proven workflows, you make governance a repeatable process, not a state of panic. 

2. Embed Privacy-by-Design (PbD) into every AI project

The core principles of PbD center around designing systems so that privacy isn’t a feature, it’s the architecture. 

That means: 

• Making privacy impact assessments part of your model development workflow 

• Documenting what data you collect, why it’s needed, and how long it’s retained 

• Treating privacy as a default setting, not a user-dependent option 

In practice, this could mean automatically de-identifying training datasets, restricting model inputs to non-sensitive attributes, or adding runtime enforcement to prevent unnecessary personal data exposure. 

3. You can use AI to implement and scale your privacy measures responsibly 

AI is a source of risk, but it can also be a remedy. The sheer scale of data in a modern enterprise makes manual governance impossible.  

This is where automation comes in. Gartner notes that AI can be leveraged to enhance your privacy program by automating data discovery, classification, and life cycle controls.  

It can also streamline the handling of Subject Rights Requests (SRRs) and help assess re-identification risk, freeing up your human experts to focus on high-level strategy and risk management. 

4. Equip yourself with the right PET for the job

Privacy Enhancing Technologies (PETs) are the building blocks of safely and securely utilising AI. Your strategy must involve selecting the right tool for the right use case. Are you training a model on historical data? Synthetic data might work. Are you performing broad statistical analysis for a public report? Differential privacy could be a fit.  

But what if you need to process live, sensitive, individual data? That requires a different, more robust solution, like Fully Homomorphic Encryption (FHE). 

PET	Best for	Example
Synthetic data	Model training & collaboration	Replace sensitive datasets with statistically valid, non-identifiable versions
Differential privacy	Analytics & reporting	Add noise to outputs to prevent re-identification
Privacy-Aware Machine Learning (PAML)	AI model training	Rules that prevent singling out individuals from large datasets
Confidential computing (like FHE)	High-sensitivity workloads	Protect confidentiality by processing encrypted data without needing to decrypt it

Choosing the right PET depends on data sensitivity, use case, and computational constraints. The wrong choice risks either overprotecting (reducing model utility) or under-protecting (exposing sensitive data).  

5. Strengthen your foundations before you scale 

Think of AI as an amplifier: if your privacy programme is strong, it enhances it. If it’s weak, it exposes it. 
 
Before training a model, make sure your organisation has mastered the basics: 

• Data minimisation and purpose limitation: Only collect what’s needed, and only use it for what’s declared 

• Clear retention and deletion policies: Know when and how to retire data 

• Impact assessments that matter: Don’t treat PIAs as tickbox exercises – they are your early warning system for hidden risk. 

If these aren’t in place, AI adoption will magnify every weak control, from insecure data pipelines to unclear lawful bases for processing. 

The bottom line: AI is a choice

The Gartner report ends with a reminder worth repeating: 

“Though pressure may feel intense to adopt every new form of AI technology available today, remember: using AI is a choice and never an obligation.” 

Choosing AI means choosing responsibility. Building privacy-safe AI isn’t just good ethics – it’s good engineering, good compliance, and good business. 

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 → 

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