Artificial intelligence (AI) is accelerating down the enterprise highway. But according to new research, governance is idling in the slow lane, and the gap is already raising breach costs, fueling shadow AI adoption—when employees use generative AI tools outside official oversight—forcing regulators to wave the caution flag. Global average data breach costs fell in 2025
The artificial intelligence (AI) revolution began with general-purpose models—the Swiss Army knife of intelligence. Handy, flexible, and able to do a bit of everything, they’ve answered customer questions, written and debugged code, and even nailed a wedding toast or two. Impressive? Absolutely. But when precision and compliance are on the line, these jack-of-all-trades models aren’t
As businesses take to large language models (LLMs) to power productivity-saving AI chatbots and content generation, the biggest hurdle is overcoming costs. According to a recent industry survey, enterprise cloud costs rose 30% in 2024, and 72% of IT and financial leaders said cloud costs were becoming unmanageable, all primarily due to AI.1 Pennies per
Data fuels our global digital economy. As AI and analytics applications proliferate, the value of critical data continues to rise, along with the urgency to protect it. Governments around the world are responding. Most nations now enforce data sovereignty laws that regulate how digital information is collected, processed, and stored within their borders. The result
We’ve reached a moment where AI is materially changing how breaches happen and how much they cost. IBM’s 2025 Cost of a Data Breach report shows the clearest signal yet: AI-driven security is lowering breach costs through faster containment, while ungoverned AI and shadow tools are quietly driving costs up. The balance between benefit and
Digital twins are virtual replicas of real-world systems that provide organizations with rich tools for experimentation, planning, optimization, and risk mitigation. Enabled by IoT sensors and IT/OT convergence, these simulations mirror their counterparts in a true-to-life fashion, with data flowing in real time from the physical location to its digital representation. Through digital twins, teams
AI technology is advancing faster than regulations can keep up, putting people and organizations at risk of extensive data collection and AI’s opaque decision-making and unpredictable behavior. This creates both opportunities and challenges for governance, risk, and compliance (GRC) practices. The lag between innovation and regulation is stark: ChatGPT reached 100 million users in two
How energy innovation is reshaping infrastructure for AI workloads AI is redrawing the global energy and infrastructure map. In the past year alone, energy demand from hyperscale AI data centers has surged so dramatically that innovators, operators, and governments are turning to unconventional solutions: redeveloping the site of America’s worst nuclear accident into an energy
Generative AI (GenAI) is likely the most significant technological breakthrough of the decade, yet the impacts are hard to quantify. Even detailed studies produce contradictory data, and while the evidence suggests a handful of hours saved per worker per week,1 there’s no clear correlation to revenue. How can we definitively say whether GenAI is worth
Long a sci-fi trope, the concept of “adversarial AI” has transcended fiction and emerged as a stark threat in the real world. As generative AI permeates across systems and applications, its potential for criminal mischief is multiplied by the emergence of agentic AI. More deployed AI models mean more targets and attack surfaces to exploit,
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