AI has altered the physics of cybersecurity. Threats now move faster than policies can adapt, and defences evolve in real time. The traditional idea of resilience – static controls, periodic reviews, and defined perimeters – no longer fits.
In an era where many students invest over £60,000 in a university education, the question of value for money has become a defining factor in university choice. But “value” is not universal.
Enterprise networks have reached an inflection point. Hybrid working is now embedded, cloud applications proliferate, and data moves continuously between users, devices and locations. UK organisations now manage more cloud identities than traditional endpoints, and more than half of enterprise traffic originates outside the office. As a result, network reliability, security and performance have shifted from operational concerns to board-level priorities.
AI is now woven into every part of cybersecurity operations. It analyses behaviour, identifies anomalies, and increasingly, takes action on its own. It’s faster, tireless, and in many cases more consistent than any analyst could be. But with that progress comes a harder question: how much decision-making are we prepared to give away?
There’s no escaping the squeeze. Demand is rising, budgets are flat, and no one’s in the mood for another “game-changing” announcement. People have heard so many promises of ‘once in a generation’ transformation that hype doesn’t hold much weight anymore.
There’s a moment every organization faces when it has to stop pretending the cracks aren’t there. The systems that don’t quite talk to each other. The processes everyone works around. The decisions that were right once — but no longer are.
In the context of limited investment, socio-economic disruption, and accelerated demands, the CIO mandate is clear: do more, deliver faster, spend less. Clear. But impossible.
AI has become the defining force in cybersecurity, not because it’s new, but because it’s everywhere. It’s now threaded through every layer of the threat landscape: attackers use it to accelerate their operations, while defenders rely on it to hold the line.
Emerging technology has an irresistible pull. Every CIO knows the feeling: a new platform promises efficiency, a new algorithm claims intelligence, a new vendor insists you’re falling behind. The pressure to adopt can be immense – from boards, from peers, from the market itself.
AI is arriving in universities at remarkable speed, but deciding how it should be used is proving far harder to match.
That was one of the clearest messages from the recent DIG25 roundtable hosted by Roc Technologies and the University of Reading – a conversation that surfaced not just what AI is doing in Higher Education, but what AI is demanding of it.