Startups Love AI. Hackers Love That Too.
Startups have always been in survival mode.
Before AI, the fight was simple (but not easy): build something people actually wanted. Then AI came along, and the fight became about speed. Everyone was chasing the ability to build faster, ship faster, and grow faster.
But the goalpost is moving again. The next challenge for founders isn’t just building or shipping at speed. It’s keeping everything working and safe in a world where AI itself can be hacked, manipulated, or poisoned.
And it’s not theory — it’s already happening.
The reality check
In the last 2 weeks alone we’ve seen cyberattacks that show just how fragile modern systems are:
Jaguar Land Rover was hit by a massive cyberattack that forced production to stop, costing them around £50m a week in lost output.
Airports across Europe — from Heathrow to Brussels to Berlin — were paralysed when a single software supplier, Collins Aerospace, got hit with ransomware.
Nurseries in London had the data of 8,000 children stolen and leaked online after hackers broke into their systems.
These are not small players. They have IT teams, budgets, and processes. And yet, they still got taken down.
Now imagine what happens to startups with no CISO, no security team, and founders who are more focused on fundraising than firewalls.
Adopting and scaling AI is the difference between startups that lead — and those that get left behind. But adoption isn’t always a walk in the park.
How do you integrate new AI tools fast, without slowing your roadmap or putting your business at risk?
How do you prove your AI product is safe, credible, and ready to scale?
And how many hours has your team lost to back-and-forth before real onboarding even begins?
That’s exactly what governr was built for: a high-frequency risk management platform for AI-native startups. In just 60 minutes, we deliver an independent risk score for any AI tool or agent, with live monitoring as you scale.
With governr, you can assess suppliers, map third-party exposure, and prove control with live updates and readiness signals.
We can score your product in just 60 minutes (and probably already have). Join our sandbox to get access.
The new attack surface: AI itself
What’s different today is that AI isn’t just another tool — it’s a new attack surface.
Researchers just disclosed EchoLeak, a vulnerability in Microsoft Copilot that allowed attackers to exfiltrate data simply by sending a crafted email. No clicking, no interaction. Just the input itself was enough.
Google’s Gemini AI has been shown to be vulnerable to prompt injection attacks, where hidden instructions in text can trick the model into doing things it wasn’t supposed to.
Microsoft has publicly admitted that indirect prompt injection is a serious risk: if an AI system ingests data from the web or a document, and that data contains malicious instructions, the AI might follow them as commands.
Malwarebytes recently warned that AI-powered browsers can be hijacked by invisible text on web pages — white text on white background that humans can’t see, but AI interprets as instructions.
Think about what that means: attackers don’t need access to your servers anymore. They can manipulate your AI through the inputs you feed it — or through what your customers type into it.
For startups building products on AI APIs, or feeding customer data into AI tools, that’s a nightmare waiting to happen.
Founders are blind to this risk
Most startups trust AI vendors or open-source models without questioning what’s under the hood.
But how safe is it to build your product on OpenAI? How reliable are open-source models when anyone could have slipped malicious code into them?
I know one company working on military-grade AI for confusing drones using open-source image recognition AI. That’s not even a cyberattack — it’s hacking AI behaviour. It shows how easy it can be to manipulate models.
And that’s just what we know. If flaws in Microsoft, Google, and Meta are hitting the headlines, what about the countless smaller incidents that go undiscovered or never make the news?
Startups could already be leaking customer data or running compromised models without realising it.
Why startups are uniquely vulnerable
Unlike JLR, startups don’t get government bailouts when things go wrong. If you get hacked, you don’t have £1.5bn of support coming your way.
Founders often aren’t technical — and even if they are, they don’t have the resources to hire deep security talent early. Many are building products overnight with “vibe coding” tools like Lovable or Replit. That’s great for shipping MVPs, but the security debt is instant.
The truth is: the same speed that makes AI powerful for startups also makes them fragile. Attackers are already using AI to supercharge phishing, generate malware, and even clone investor voices for scams. Startups aren’t ready for that fight.
The founder survival problem
Speed is great. AI makes building faster than ever. But if the foundation is insecure, speed just means you’ll hit the wall quicker.
Cybersecurity used to be a “later problem.” Not anymore. With AI, it’s a survival problem from day one.
If JLR can bleed £50m a week, a single AI-driven breach can wipe out a startup overnight: investors walk, customers churn, trust is gone.
So what can founders do?
Audit your AI stack — know who and what you depend on.
Limit sensitive data you send into AI tools.
Don’t blindly trust outputs — validate and test them.
Have at least a simple incident plan. Even one page is better than nothing.
Because the next phase of the startup game isn’t about building or scaling fast. It’s about building and scaling safely.
✅ If this hit home, pass it on. There’s probably a founder in your circle who’s quietly taking shortcuts on AI security.
POLL TIME
(👉 Vote now — we’ll share the results in next week’s issue. All votes are anonymous.)
If you’re building with AI — how much do you think about cybersecurity?
INDUSTRY PULSE 🩺
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