Privacy’s Grey Area: The Implications of AI Agents

As AI agents become part of everyday workflows, a new privacy grey area is emerging. This post explores how autonomous data sharing between agents challenges traditional privacy safeguards, and what it means for governance in practice.

When Data Moves Faster Than Governance

AI tends to steal the headlines. But the longer you work with it, the clearer it becomes that data, not intelligence, is where the real complexity lies.

Where there is data, there is data governance. And where there is governance, there is privacy.

Most organizations already know this. They have policies in place, compliance teams monitoring activity and refresher courses that remind employees of their responsibility to process and protect personal data appropriately. Everyone’s trained. Everyone’s signed.

All good. We’re all on the same page.

Until we’re not.

Efficiency’s Hidden Trade-Off

A recent article by Shumaker titled “Chatty Chatbots: Why AI Agents are the Silent Threat to your Company’s IP” drew attention to a subtle but serious issue: how agentic AI systems (AI agents that take action, independently but collaboratively) can quietly expose a company’s intellectual property through over-sharing of information.

That caught my attention for another reason. Because if the same patterns apply to personal data, we may be looking at a new kind of privacy exposure: one that today’s regulation doesn’t explicitly address.

Imagine you’re planning a corporate event. You decide to hand part of the process over to an AI agentic team.

The Marketing Manager Agent directs the operation. It instructs the Venue Agent to find and book a suitable location based on criteria like budget and accessibility. Once that’s done, the Invitation Agent designs an experience-rich invitation. Then the Copywriter Agent drafts the announcement and passes it to the CRM Agent, who selects invitees and schedules the send.

There may still be a human in the loop for sign-off, but most of the heavy lifting is done.

It’s efficient. It’s fast. It’s scalable.

But within that efficiency lies a hidden problem.

To ensure nothing goes wrong, each agent hands over more information than is strictly necessary, “just in case” the next one might need it. This context dump (where an AI passes all related data rather than only what’s needed) gives the next agent everything it could possibly require to complete its task.

That’s good design in engineering terms.

But in privacy terms, it’s a leak.

The Slow Leak of Personal Data

If any of those agents touch personal data, say, client details from the CRM or notes from a past meeting, the over-sharing creates a privacy blind spot.

Unlike a database breach, this isn’t a single moment of loss. It’s a slow, invisible seepage of data through internal hand-offs and shared memories.

AI agents operate through retrieval, drawing on stored context to interpret and act. Each memory fragment they store may contain pieces of personal information that later reappear in unrelated contexts. Over time, fragments combine and resurface in ways no human ever intended.

That’s not a coding flaw; it’s a governance flaw.

Privacy risk isn’t confined to where data sits anymore. It now lives in how data moves.

A Privacy-by-Architecture Perspective

To understand this better, I spoke with Vincent Labuschagne, Head of Privacy Compliance at Pétanque NXT. He helps organisations align privacy architecture with emerging AI applications, extending Pétanque NXT’s process-thinking approach into the digital ecosystem.

Here’s how he unpacked it.

“The same hand-offs that make agentic workflows efficient also make them privacy-fragile. Each agent passes more data than necessary, which expands the exposure surface. It’s not a breach in the classic sense. It’s a failure of data minimisation and purpose limitation.”

Vincent also points out that privacy risk isn’t confined to where the data sits anymore. It’s now embedded in how data moves.

“Agent memories (short-term caches, embeddings, retrieval logs) are new layers of untracked storage. They hold fragments of personal data long after the original purpose has expired. We’re used to managing retention in static databases, but agent systems regenerate data as they operate. You can’t delete what you don’t know is there.”

His perspective shifts privacy from compliance to architecture.

“Policy isn’t enough. Privacy has to be coded into how the system behaves. Segregate agent memories by sensitivity. Expire credentials and data contexts at task completion. Filter personal identifiers before passing information along. Policy-as-code is the only scalable safeguard.”

He’s equally clear on the human element.

“Human oversight still matters, but it’s not about approving outputs. It’s about screening inputs. It’s about making sure the data that enters the agentic process is anonymised or redacted. The control point moves upstream.”

And finally, on the regulatory side:

“GDPR wasn’t written for autonomous systems exchanging data on their own. It assumes a clear chain of controllers and processors. Agentic architectures dissolve those boundaries. Regulators will eventually need to move from ‘privacy by design’ to ‘privacy by delegation’, making whoever configures the agentic system accountable for what its agents do with data.”

Contain the Context, Keep the Value

That phrase, privacy by delegation, captures the shift that business leaders now face.

Integrating AI agents into workflows isn’t a plug-and-play decision; 

• It’s a redesign of your data ecosystem.

• Every integration point is a potential disclosure moment.

• Every memory is a possible archive.

Leaders who design for privacy at the architecture layer will future-proof both compliance and trust.

AI governance isn’t just about responsible use policies or staff awareness. It’s about rethinking how data moves through the system and ensuring the same principles that safeguard data in a database (minimisation, retention control, lawful purpose) apply equally to autonomous hand-offs.

That’s where privacy meets engineering.

Contain the context. Keep the value.

That’s the quiet challenge at the heart of AI’s next chapter.

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Before you deploy your next agentic system, ask:

• Do we know where our data goes? 

• Do we know who, or what, touches it along the way?

This is where AI Governance starts.