Ask Rickard Carlsson, co-founder and CEO of Detectify, whether AI is handing attackers new capabilities, and he'll correct the premise. It's not new vulnerabilities. It's not new techniques. It's the same attacks, running on a clock that used to be measured in days and now runs in minutes.
"It's not new vulnerabilities. It's just faster and easier to scale things," Carlsson said in an interview following Black Hat USA 2026. That distinction matters more than it sounds. If AI were only inventing novel attack vectors, defenders could study and prepare for a new category of threat. Instead, they're facing the same old exposures, arriving faster than most security programs are built to handle.
The clock that used to run in days now runs in seconds
Carlsson's clearest example starts before an attacker even shows up: certificate transparency logs. Every time an organization spins up a new website and issues an SSL certificate, that certificate becomes public record, visible to anyone watching, including attackers scanning for freshly deployed infrastructure. "From you actually spin up a new website, you can start to see attacks within a few seconds," Carlsson said.
The bigger shift is in how fast a new vulnerability gets turned into a working exploit. Reverse-engineering a CVE from a code diff in an open-source library used to take a skilled researcher days. An AI agent can now do it in minutes. "Zero day becomes weaponized much faster," Carlsson said, and that's the part he wants defenders to internalize. The vulnerability research that used to be the bottleneck between disclosure and exploitation has largely disappeared.
It's economics, not new techniques
Carlsson frames the shift as an economic one more than a technical one. Where a large-scale attack once required a small army, an AI-fluent individual with a handful of GPUs can now credibly compete. But he's careful to draw a line most coverage of AI-driven attacks blurs: AI does the prep work, not the whole attack. Once a vulnerability is identified, actually running an attack at scale, testing millions of endpoints, still happens through deterministic tooling, not AI. "You can't do that with AI, but the AI helps you identify the vulnerability. It helps you build the code that does the testing, and then it's the same old testing," he said. Targeted attacks against a specific organization are a different story, where AI stays continuously in the loop, iterating on signals rather than doing one-time prep work.
Patch everything, instantly
If speed is the attacker's advantage, Carlsson argues defense has to adopt the same posture, not as an experiment but as standard infrastructure. He described a shift he's hearing from a large European governmental institution moving away from staged patch rollouts (10 machines, then 100, then 1,000) toward immediate, full deployment. "You patch everything instantly, and then you take any issues that come with it. You have to solve that later," he said. The old model of gating a rollout to catch problems early assumes attackers are moving on a similarly staggered timeline. They aren't.
Killing alert fatigue with deterministic answers, not more noise
Security teams already drown in alerts, and Carlsson is blunt that adding AI carelessly just makes that worse. Detectify's approach leans on what he calls payload-based testing: rather than flagging a theoretical vulnerability, the platform actually attempts the exploit and gets a deterministic true-or-false answer. That's part of how the company holds its false-positive rate to roughly 0.3%, according to Carlsson. It's also, he argues, exactly the kind of clean, unambiguous data AI agents need to be useful rather than just another noise generator. "The more black and white data you could give them, the better it is," he said.
What developers are missing
Carlsson's sharpest point for engineers and architects isn't about attackers at all. It's about how AI evaluates code in isolation and misses system-level risk. Secure code, combined with a particular hosting setup, a DNS configuration, or an adjacent service handling serialization differently, can create a vulnerability that doesn't exist in any single component. "When the AI looks at a single component, it might not always understand the system risks," he said.
He also flagged a subtler risk: code maintained solely by AI over time can quietly degrade, as models optimize for whatever instruction came most recently and loosen constraints set earlier in the process.
Not everything is on fire
Carlsson pushed back on doom-laden framing of where this goes next. Yes, the pool of exploitable systems will grow. But every attack still needs a human on the other end collecting the payout, and plenty of vulnerable systems today simply aren't worth attacking. His actual worry is narrower and more specific: state-sponsored espionage against high-value targets, and ransomware crews finding it newly profitable to go after smaller organizations that never had the resources to keep up in the first place.
For developers shipping code that becomes part of that attack surface, his advice comes down to one shift in mindset: stop reasoning about code in isolation, and start thinking about the whole chain it runs inside.