How AI is reshaping data privacy: A business-first approach to risk

With AI transforming how data is created, shared and secured, businesses face a growing challenge: how to stay competitive while safeguarding sensitive information. Every minute of system downtime can cost upwards of $9,000, and as more organizations embed artificial intelligence into their operations, the risks only grow.

What was once an IT issue is now a firmwide priority. AI-powered systems are redefining how personal and operational data is used, making traditional data protection approaches feel increasingly outdated. Leaders must rethink their privacy and governance strategies through the lens of evolving AI capabilities — and threats.

AI changes the risk equation

AI enables smarter automation, sharper insights and faster workflows. But it also introduces:

• Increased data exposure: AI systems often pull from vast, unstructured datasets — sometimes without clear permissions or visibility into data lineage.
• New vulnerabilities: From deepfake phishing to malware that learns from its environment, AI has become a tool for both innovation and exploitation.
• Loss of control: Employees may use generative AI tools without clear guidelines, feeding confidential or regulated data into systems the business doesn’t fully understand.

Meanwhile, the public is more aware than ever. Customers want to know how their data is being used — and companies that can’t answer confidently risk losing trust and business.

Rising downtime costs driven by AI involvement

AI-related systems can create dependencies that make outages harder to detect and recover from. Consider these statistics:

• Downtime costs businesses an average of $9,000 per minute.
• Certain sectors experience losses of up to $5 million per hour.
• After a major disruption, it takes 75 days on average for revenue to stabilize.
• Stock values can drop up to 9% following an incident.

Understanding the financial impact of a disruption is the first step toward managing it. A simple formula can help:

Downtime cost = (lost revenue + lost productivity + recovery costs) × duration

Breaking it down further:

• Operations: Delays, waste and overtime
• Sales: Lost transactions and leads
• Customer service: Reputation damage and churn
• Administrative functions: Idle hours and rework

AI makes this calculus more urgent — because failures happen faster, reach farther and are often harder to unwind.

A modern approach to data governance

As AI adoption accelerates, organizations need to modernize their privacy frameworks. That means moving from compliance checklists to full-scope governance. Start with:

• Mapping your data flows: Know what you collect, where it lives and how AI tools interact with it.
• Defining AI use policies: Outline approved tools, acceptable use cases and what must stay out of prompts or training datasets.
• Auditing regularly: Monitor AI model behavior for drift, bias or exposure. Review vendor policies for how they store and use your data.

Governance isn’t just about restriction — it’s about clarity and control. The more visibility you have into your AI systems, the more confidently you can innovate.

Industry-tested practices that work

Different industries are adopting practical, scalable ways to manage AI and data risk:

• Healthcare: Secure research environments allow anonymized access to data without exposing patient details.
• Finance: Layered access controls and real-time threat monitoring guard against transaction tampering.
• Manufacturing: Isolated operational networks and employee training reduce the risk of connected machine downtime.
• Retail: Smart data minimization policies and device-level monitoring help prevent unauthorized access.

These strategies are more than risk mitigation — they enable smarter growth. When businesses treat data protection as a strategic capability, they improve efficiency, resilience and trust.

Turning protection into performance

Investing in data privacy pays off beyond regulatory compliance. Companies that proactively manage AI-related data risk:

• Recover faster from disruptions.
• Build deeper customer loyalty.
• Stay ahead of evolving laws and expectations.
• Preserve intellectual property and brand equity.

AI isn’t going away — and neither is the need for strong data governance. The organizations that win won’t just move fast. They’ll move responsibly.

Where to go from here

You don’t need to tackle everything at once. Start by:

• Identifying your most critical data systems.
• Mapping where AI is in use (authorized or not).
• Setting clear rules and accountability across teams.

As AI becomes a core part of business infrastructure, companies must treat data privacy as a shared responsibility — and a driver of long-term value.

How Wipfli can help

Wipfli helps organizations implement AI with confidence. From risk assessments to data governance frameworks, we help you unlock the value of AI while protecting what matters most. Contact an advisor today to get started.