Global Privacy eBook Executive Summary

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The environment of privacy, data governance, and artificial intelligence is changing the way organizations build resilience and earn trust. Advancements in technology, coupled with new compliance mandates, are forcing companies to assess and rethink how they operate, innovate, and manage risk. Regulators are adapting to this new environment by going beyond policy reviews to test privacy, consent, and governance controls, making these areas strategic imperatives for organizations. The Global Privacy, AI, and Data Governance for Resilience and Trust eBook covers critical areas of importance for organizations as they navigate an evolving operational landscape.

Three Focus Areas

Privacy

Understanding privacy vs. security, building a strong privacy program, and embedding privacy by design

Data

Addressing controls, establishing continuous monitoring, and complying with regulatory requirements

AI

Managing AI risk, recognizing AI’s impact on privacy, and promoting innovation responsibly

Privacy

Privacy has become a core business priority, and the challenges organizations face in both regulatory compliance and earning stakeholder trust continue to grow. Responding to these challenges requires a comprehensive program that enables companies to identify risks and manage them decisively. A mature privacy program aligns critical areas of the organization, enabling an effective, timely response. 

Mature privacy program alignment graphic

Creating a comprehensive program that ties together each of these components is essential for taking privacy from policy to action. Governance is now judged by outcomes that have reshaped privacy into an operational endeavor, with effort alone no longer sufficing to meet regulatory standards. Organizations are now expected to demonstrate the tools they’re using, what data they’re collecting, where that data is stored, and how they’re implementing users’ choices over data.

Dark Funnel

The “dark funnel” refers to customer and client touchpoints that occur beyond the scope of traditional, measurable marketing channels. It can be any combination of peer-to-peer conversations, dark social communication, AI-generated research, and other low-visibility touchpoints that offer limited or no attribution. Because of its opaque nature, the dark funnel inherently creates a privacy compliance issue, leaving organizations with almost no way to track what data they’ve collected, how they’ve connected that data, what inferences they’ve drawn from it, and other privacy requirements that regulators are increasingly requiring.

Data

Data is the essential building block of operations for modern organizations. It underpins everything fueling innovation to enabling AI functionality. Because of the critical role data plays, there are three essential steps organizations should take in managing this vital asset.

  • Position data as a strategic asset: Organizations can improve operational efficiency, generate new growth opportunities, spur innovation, and reduce exposure to regulatory risk by viewing their data as part of their overall strategy.
  • Prioritize data quality before scaling: Data quality is critical for gaining accurate insights, enabling effective automation, and supporting successful AI use. Before organizations scale their use of data-reliant tools, they must first ensure that the data they rely upon is clean, accurate, and well governed.
  • Establish continuous data governance: Data governance should be a continuous process, helping organizations maintain data integrity and meet regulatory requirements. As compliance standards increasingly mandate that organizations can demonstrate effective controls in real operating environments, continuous data governance allows companies to be prepared to respond to audits and business needs.

AI

AI’s potential has only expanded as the technology has advanced — but so too have the risks. While early privacy concerns and model bias remain important considerations, reputational risk, workforce disruption, over-automation, and the loss of institutional knowledge have also emerged as concerns.

Comprehensive AI governance frameworks and security measures are vital to responsible innovation and addressing risks from the outset. This includes the push for agentic AI integration throughout business operations and the awareness that there must still be human oversight and visibility into how AI operates and makes decisions.

AI risk isn’t a technology problem —- it’s a leadership blind spot. If boards don’t know what questions to ask, they can’t govern effectively. Organizations need more than a head of AI; they need a governance model that ensures leadership is fluent in the right risk language, and is supported by independent oversight on ethics, bias, and accountability. Without that, optimism becomes exposure.
Kirstie Tiernan
AI Leader and Member of Board of Directors, BDO USA