Cisco has released new findings showing that artificial intelligence is rapidly reshaping how organizations manage data privacy and governance worldwide. It shows that privacy and governance are no longer viewed as compliance obligations but as critical foundations for deploying AI at scale.
The company’s 2026 Data and Privacy Benchmark Study is based on a survey of 5,200 IT and security leaders across 12 markets. As enterprises accelerate AI adoption in business-critical environments, the study signals that mature data governance is becoming a decisive factor in trust, competitiveness, and long-term innovation.
The study finds that AI is now the primary driver behind expanded privacy initiatives, with 90% of surveyed organizations reporting growth in their privacy programs. Nearly all respondents – 93% – expect to increase investment further as AI systems become more complex and regulatory scrutiny intensifies. Financial commitment is also rising sharply: 38% of organizations spent at least $5 million on privacy programs in the past year, a significant increase from 14% just two years earlier.
What distinguishes this year’s findings is the strategic framing of privacy. Rather than viewing governance as a regulatory burden, organizations increasingly recognize it as a catalyst for agility and innovation. According to Cisco, 96% of respondents say robust privacy frameworks accelerate AI innovation, while 95% consider privacy essential for building trust in AI-driven products and services. Almost every organization surveyed reported at least one tangible benefit from privacy investments, ranging from faster deployment cycles to stronger customer loyalty.
However, the report also exposes a gap between ambition and execution. While three-quarters of organizations say they have established a dedicated AI governance body, only 12% describe these structures as mature. As AI models draw on increasingly distributed and heterogeneous datasets, 65% of respondents struggle to access relevant, high-quality data efficiently. Cisco executives argue that explainability and accountability must extend across all data types, not just personal information, to ensure AI systems can scale responsibly.
The survey also sheds light on the growing tension between global AI deployment and regional data regulations. Although 72% of respondents express general support for data privacy laws, 81% report rising pressure to localize data. These requirements come at a cost: 85% say localization increases operational complexity and risk, while 77% believe it limits their ability to deliver seamless, always-on services across borders. As a result, enterprises are increasingly favoring technology partners with global footprints, with 82% agreeing that large-scale providers are better equipped to manage cross-border data flows securely.
Notably, confidence in the assumption that locally stored data is inherently more secure is declining. The share of respondents who hold that view dropped from 90% in 2025 to 86% in 2026. In parallel, 83% of organizations now advocate for harmonized international data standards, reflecting a growing consensus that global consistency is critical for AI-driven growth.
Cisco concludes that organizations seeking long-term success in the AI era must move beyond reactive compliance. Building resilient data infrastructure, embedding privacy and security into AI lifecycles, and investing in workforce training are emerging as decisive factors in maintaining trust while scaling innovation globally.
Executive Insights FAQ
Why is AI driving higher privacy investment now?
AI systems require large volumes of high-quality data, increasing the need for governance, transparency, and risk mitigation.
How much are organizations spending on privacy programs?
More than one-third now spend at least $5 million annually, reflecting rising complexity and regulatory expectations.
Are privacy frameworks seen as barriers to innovation?
No, the majority view them as enablers that improve agility, trust, and AI scalability.
What challenges do companies face with AI governance?
Many lack mature governance structures and struggle to manage distributed, heterogeneous datasets effectively.
Why are global data standards becoming more important?
Fragmented localization rules increase cost and complexity, limiting the ability to scale AI services across markets.
