Most CISOs I talk to are tired of hearing how “AI will revolutionize security.” What they need isn’t another pitch — it’s clarity. The truth is simple: attackers are using AI because it works. It scales social engineering. It mutates code in real time. It overwhelms analysts with volume and variation. And it’s only getting better at all of the above.
We’re not entering a new era of threats. We’re already living in it. And we’ve built most of our security stacks for a world that no longer exists.
Attackers Don’t Sleep and They Don’t Think Like Us
Over the past year, according to the Threatscape Report, we analyzed 16 million cyberattacks deployed through deception-based infrastructure across 30 countries. Here’s what we found:
- A 470% increase in new, never-before-seen payloads.
- A 4100x spike in credential stuffing attacks targeting VPNs.
- A 14x jump in time-based SQL injection and a drop in brute-force methods.
What does this tell us? That attackers are shifting from brute force to finesse. That they’re exploiting regional gaps in infrastructure. And that they’re testing what automation can do against a defense model still reliant on signatures and alert queues. According to recent IBM research, integrating AI into cybersecurity workflows can improve detection rates by up to 95% and cut response times in half, one more evidence that smarter systems aren’t just helpful, they’re necessary.
The Case Against Reactive Security
Security teams aren’t falling short because of lack of skill. They fail because they’re playing catch-up. Tool fragmentation. Alert fatigue. Manual triage. Static playbooks. All of this slows them down while attacks move faster. AI doesn’t solve this magically, but it changes the physics. It stops treating detection and response as separate events. It doesn’t wait for an alert to escalate. It doesn’t stop to ask permission. The point of AI in security isn’t just better math, it’s better structure. We’re talking about autonomous systems that:
- Investigate threats as they emerge.
- Write their own incident summaries.
- Recommend mitigations.
- Do all of this on-prem, with no reliance on cloud APIs.
Deception one of the Most Honest Tool We Have
Here’s something I’ve come to believe: if your system never lies to an attacker, it’s probably not defending you very well. AI makes deception scalable. We’ve deployed environments where fake credentials, cloned services, and sandboxed infrastructure are indistinguishable from the real thing. When an attacker hits them, we know instantly and we get to watch their methods play out in a safe space. The data collected from these traps helps the system better distinguish legitimate requests from malicious ones, improving both accuracy and responsiveness over time.
This isn’t a “nice to have.” It’s how you stop lateral movement. It’s how you detect unknown zero-days. It’s how you buy time.
The Edge Is the Most Vulnerable Place in the Network — and the Most Ignored
As the number of connected devices soars toward 32 billion by 2030, IoT security has become a buzzword, but few people actually want to talk about the real challenge: these devices can’t run traditional agents. They don’t have the memory, the computer, or the battery budget. Also, transmitting data is expensive, often incurring ongoing costs for SIM cards and network traffic. These devices need a special solution, for example, a 2MB AI firewall that runs directly on edge, including ARM-based hardware, without compromising performance. Not because it’s a trend, but because in many sectors (energy, transport, healthcare), a small unprotected device is all it takes to bring down the system.
If we don’t build AI to run at the edge, we will never close the gap.
Cloud-Native Is Great — Until You Work in a Regulated Industry
Sending live security data to external AI systems, including LLMs, might make for a great product demo, but in practice it’s a nonstarter for anyone dealing with patient data, financial transactions, or national infrastructure. This is why on-prem AI matters. It gives enterprises the speed and adaptability of agentic intelligence without losing sovereignty over their telemetry. It’s slower to build. But it’s the only way forward in critical industries.
Looking Ahead
Cybersecurity doesn’t need more hype. It needs structural reform. AI can’t just be layered on top of outdated processes — it has to replace them.
We need:
- Fewer dashboards, more decisions.
- Less orchestration, more autonomy.
- Fewer reactive alerts, more proactive moves.
The platforms that will lead this space aren’t the ones making the biggest claims — they’re the ones that quietly reshape how defense is done. AI is not the strategy. But it’s the only way your strategy scales.
So how is AI transforming cybersecurity strategy? It’s replacing linear workflows with autonomous reasoning. It’s shifting detection from pattern matching to behavior analysis. It’s bringing protection to the edge, embedding defense where attacks actually happen. And most importantly, it’s helping teams focus on what matters — because in security, speed and clarity are the difference between resilience and regret.
