The Two-Front Battle. How Privacy Teams Manage Today’s Work While Preparing for AI Governance

Privacy teams entered 2025 with more responsibility than ever. They manage daily privacy incident response, regulatory updates, risk assessments, vendor reviews, training, reporting, and cross-functional requests that never slow down. Now they face something even bigger. Across industries, privacy leaders are increasingly being asked to “own AI governance.”

In conversations with privacy officers, compliance leaders, and security executives across Europe and the United States, a clear pattern has emerged. Privacy teams are fighting a two-front battle. They manage today’s operational work while preparing for the sweeping requirements introduced by the EU AI Act, US state-level AI rules, and global digital governance frameworks such as the EU Digital Omnibus.

This moment is reshaping expectations, redefining governance, and accelerating the need for modern privacy management solutions capable of handling both privacy risk and emerging AI oversight. And because regulations vary widely across regions, multinational companies must navigate yet another layer of complexity. AI governance obligations differ depending on whether an organization operates in the EU, the US, or elsewhere.

To understand why this shift is happening. We need to examine the forces pushing AI governance toward privacy teams, the risks this creates, and how future-ready organizations are preparing.

Why Privacy Teams Are Being Asked to Own AI Governance

Across hundreds of organizations, privacy leaders echo the same underlying truth. AI is not just a technology problem. It is a data, risk, transparency, and accountability problem. Those are areas that privacy teams already oversee, supported by privacy software for compliance officers, privacy incident management software, and privacy risk assessment tools.

Three forces drive the AI governance handoff.

1. AI Regulations Are Closely Linked to Privacy Law

AI regulatory frameworks mirror privacy structures. In the EU, the AI Act directly intersects with GDPR. In the US, new state AI laws draw from privacy statutes. This creates a default assumption. If governance involves data and risk, privacy should manage it.

2. Privacy Is Already the Most Cross-Functional Team

Privacy routinely collaborates with legal, security, engineering, procurement, IT, and product teams. They understand operational governance, documentation, and cross-functional alignment. That makes privacy a logical, if overloaded, home for early AI oversight.

3. AI Introduces Regulatory, Legal, and Reputational Liabilities

Organizations need teams with compliance expertise, a documentation mindset, and experience interacting with regulators. Privacy teams already use tools like vendor risk assessment solutions and HIPAA incident response tools to operationalize similar workflows.

But this is where tension emerges. Privacy teams are being asked to lead AI governance not because they have spare capacity, but because they are the only ones with a governance muscle.

The Real Problem: Privacy Teams Are Overextended

Privacy officers describe their day as nonstop. Continuous incident triage. Breach notifications. Vendor risk assessments. DPIAs and TIAs. Training. Audit preparation. Board reporting.

These duties already require strong workflows and software support, such as privacy management solutions, privacy incident management software, and tools for privacy analysts.

AI governance adds entirely new expectations.

• AI model inventories
• Use-case assessments
• Explainability requirements
• Bias monitoring
• Human oversight structures
• Lifecycle documentation
• Vendor AI disclosures
• Cross-functional accountability
  

These do not map cleanly to existing privacy skill sets or bandwidth. At conferences in Brussels and Washington, privacy leaders expressed the same concern. “We barely have time for today’s obligations. AI governance adds a level of risk we cannot absorb alone.”

This marks the privacy profession’s most significant stress test since GDPR.

Why AI Governance Cannot Live Inside Privacy Alone

AI risk is not just privacy risk. It touches operations, security, ethics, procurement, and enterprise risk. Privacy cannot answer all the hard questions.

Organizations that struggle the most share three patterns.

1. They assign privacy as the default owner of AI governance simply because no one else volunteers to take on that role.
2. They force-fit AI oversight onto privacy checklists, relying on DPIA templates that fail to meet regulatory scrutiny under the AI Act.
3. No one owns critical questions about risk scoring, vendor validation, model accountability, or evidence retention.

This is why leading organizations are forming AI governance councils with legal, privacy, security, procurement, engineering, data teams, and product leadership. AI governance is not a function. It is an operating model.

The New External Pressure. AI Rules Are Evolving Faster Than Companies Can Adapt.

A major insight from recent AI governance conferences is that regulations across regions are moving at different speeds and in different directions.

In the EU. Simplification Plus Stricter Guardrails

The EU Digital Omnibus reorganizes digital law to reduce friction, enable responsible AI development, and clarify the use of personal data in AI training. It works alongside the EU AI Act to increase clarity around compliance, transparency, and governance. But simplification does not mean less work. The internal tasks privacy teams must execute become more complex.

In the US. Fragmentation Plus Federal Ambitions

US companies face rapidly evolving state-level AI laws. Meanwhile, federal regulators seek to standardize AI oversight while courts begin hearing claims about AI misuse and hallucination harm. The result is uncertainty. No one knows which rules will apply or which agencies will enforce them.

For global organizations, this divergence creates a governance challenge. You cannot build a program for a single law. You must build one for continuous change.

The Hidden Factor. AI Governance Is Becoming Part of TPRM.

One of the fastest-growing trends in 2025 is the merging of third-party risk, privacy risk, vendor governance, and AI model risk. Organizations now add AI checks to vendor questionnaires, DPAs, and risk scoring workflows. Many now evaluate vendors using privacy-first tools, such as vendor risk assessment platforms and privacy risk assessment tools.

The problem. Most TPRM frameworks were not built for AI. They do not address model drift, explainability, hallucination impacts, or AI dependencies across a vendor ecosystem.

This is where the two-front privacy challenge becomes untenable.

The Ah-Ha Moment. Governance Is Expanding

It may look like privacy is expanding. In reality, governance is expanding. AI governance blends privacy, risk, security, data governance, and vendor oversight into one ecosystem.

Governance used to focus on documentation, approvals, and training. Now it requires transparency, risk scoring, monitoring, data lineage, lifecycle oversight, and audit-ready evidence.

Privacy teams are strong at governance, but cannot carry the full load.

What Future-Ready Privacy Leaders Are Doing Now

Through dozens of conversations, several patterns have emerged.

1. They Invent AI Before Regulating It

They map every model, use case, vendor, data flow, and checkpoint. You cannot govern what you cannot see.

2. They Redefine Governance Roles Across Functions

Privacy manages documentation and regulatory work. Legal handles liability. Security manages access and agent controls. Risk manages scoring. Procurement evaluates vendor AI. Engineering handles lifecycle controls. Product governs user impact.

AI governance becomes everyone’s responsibility.

3. They Integrate Documentation Into Workflows

Leading organizations stop treating documentation as a year-end chore. They embed evidence collection into workflows using modern privacy management solutions and software for privacy analysts. This ensures organizations produce audit-ready proof rather than policy.

In a world shaped by the AI Act, evolving EU digital rules, and unpredictable US regulations, proof becomes the new currency of compliance.

Preparing for 2026. What Privacy Teams Should Do Next.

1. Form a cross-functional AI governance committee and assign owners.
2. Conduct a full AI inventory, including shadow AI and vendor tools.
3. Define responsibilities across privacy, legal, security, risk, procurement, engineering, and product.
4. Update incident response playbooks to address AI-specific incidents, including harmful outputs, model misuse, vendor failures, and hallucination harm.
5. Design governance for adaptability. EU laws will shift. US laws will fragment. Vendors will change their AI models without notice.
   

The Future Belongs to Flexible, Evidence-Based Governance

2025 was the year organizations realized AI governance was unavoidable.

2026 will be the year they learn how to operationalize it.

Privacy teams sit at the center of this transformation. But the organizations that win will be those that treat governance as a cross-functional operating model, supported by purpose-built tools such as privacy incident management software, privacy management solutions, and vendor risk assessment platforms.

Privacy is not expanding. Governance is evolving. And the companies that adapt fastest will define the next decade of digital trust.

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