DIGITAL LIFE

Why the future of AI depends on trust, safety, and system quality

When Daniel Graham, an associate professor in the University of Virginia School of Data Science, talks about the future of intelligent systems, he does not begin with the usual vocabulary of cybersecurity or threat mitigation. Instead, he focuses on quality assurance and on how to build digital and physical systems we can trust.

"We are moving toward a world where software does not just live in the digital space," said Graham, who earned bachelor's and master's degrees in engineering at UVA. "It's embodied in cars, robots, medical devices and public infrastructure. Once systems can act in the real world, the cost of failure becomes physical. So, the question is not only 'Is it smart?' but also "Is it safe, reliable and high quality?'"

Graham joined the School of Data Science in 2025 after teaching computer science for seven years. The move, he said, was a chance to refresh, collaborate with new colleagues and teach in smaller, more engaged classroom environments.

The intersection of security and safety...Graham's research explores secure embedded systems and networks, particularly those that directly interact with the physical world, including medical devices, water treatment systems, autonomous vehicles and other forms of operational infrastructure.

Early in his career, Graham saw firsthand how vulnerabilities in software could translate into real-world consequences. Over time, this led him to view security not as a defensive activity, but as a measure of system quality and safety.

"We already know how to build incredibly powerful smart systems," he said. "What we need now is assurance." He emphasized that as society increasingly relies on intelligent systems to manage hospitals, transportation networks, power grids and military hardware, those systems must be dependable.

He believes the model already exists. Just as a professional engineer must sign off on the safety of a bridge, he says, future data and AI systems should require comparable review, oversight and certification.

"We have strong regulatory norms for physical infrastructure," he said. "But the digital infrastructure that increasingly runs everything does not yet follow comparable accountability standards. That has to change."

A public voice on responsible system evaluation...Graham also writes and teaches widely on secure systems evaluation and penetration testing. His book, released this year, "Metasploit: The Penetration Tester's Guide (Second Edition)," introduces readers to professional methods for testing and auditing complex systems. Its reach is global, with planned translations in Mandarin, Korean, French and Russian.

"Penetration testing is the digital equivalent of financial auditing," he said. "Just as organizations require audits to ensure the integrity of their financial systems, critical digital and embedded systems should be routinely evaluated for quality and resilience."

Framing cybersecurity in this way, Graham translates a highly technical concept into terms the public can easily understand.

"People understand quality," Graham said. "They understand the difference between something that is built well and something that is built carelessly. We should expect the same quality from the systems that run our world."

Looking ahead...As data science extends further into automation, embedded intelligence and decision-making systems, Graham hopes to help shape how future practitioners view their responsibilities.

"The most important systems of the century ahead will be intelligent, networked and physical," he said. "The people building them must think carefully about safety, reliability and impact. Quality is not optional. It is the foundation of trust."

The future of Artificial Intelligence (AI) does not depend on a single factor, but on a complex combination of technical advancements, physical resources, regulation, and human acceptance. Recent research indicates that the evolution of AI in the coming years (2026 and beyond) will be shaped primarily by:

Energy and physical infrastructure: The future of AI depends on megawatts of energy, not just faster chips. The demand for sustainable energy and data centers is one of the biggest bottlenecks, often exceeding hardware production capacity.

Autonomous agents (The Next Wave): The transition from Generative AI (creating content) to autonomous agents (performing tasks and acting independently) is the central trend.

Data quality and volume: The availability of high-quality data to train models is crucial, driven by the use of Big Data and advancements in processing.

Regulation and ethics: The creation of regulatory frameworks (such as the EU AI Act) is fundamental to managing risks, ensuring privacy, and defining security standards. Ethical governance is crucial for public trust.

Human talent and skills: Organizations' ability to develop and implement AI depends on reskilling the workforce, focusing on "humans in charge" (human-in-the-loop).

Transparency (Explainable AI): The future demands overcoming the "black box" problem, making AI systems more explainable and auditable.

In short, AI is moving towards becoming a "basic work infrastructure," where deep integration, energy efficiency, and ethical accountability will determine who leads the market.

Provided by University of Virginia