The game has changed, clearly. Attackers have AI, defenders have AI, and both sides are learning faster than anyone expected, or maybe the attackers are just a bit faster. What used to take hours now happens in seconds, and detection windows close before alerts even appear. It’s adaptation beyond automation, and no one gets to sit still.
But every promise of AI-driven defence comes with a price. The tools are expensive to train, maintain, and monitor. Mistakes cost more too. False positives drain teams, model drift hides real threats, and poisoned data turns protection into confusion. So now it’s not only about defending networks, it’s about defending the defenders themselves, from fatigue, blind trust, and automation gone wrong.
So how do you keep visibility when both sides use the same weapons? How do you detect intent when patterns look human but aren’t? How do you justify cost when failure still happens, just faster? Let’s explore what happens when algorithms face each other on both sides of the firewall, and what new defences emerge when speed alone is no longer enough. A closed conversation about a future where cybersecurity becomes an AI vs AI battle, and humans still have to win.
