In a few short years, the AI market has risen to over $600 billion in valuation, and it’s not slowing down. Instead, AI has brought in a new wave of fast-rising startups. Unlike the expensive and highly stressful traditional model of building a startup, AI has leveled the playing field such that even the average hobbyist can build something.
Thanks to AI, many more startups are trooping into the market. While some are mere adopters, others are most AI-centric.
What are AI Startups?
Simply speaking, AI startups are new and young companies that primarily use AI for their product or services. Typically, they offer AI solutions to clients, and in most cases, this involves AI automation of simple and complex processes.
Note that AI startups are different from businesses that simply adopt AI. While the latter uses AI as an add-on, actual AI startups have their entire business model centered on AI solutions.
Types of AI Startups?
Strictly, there are 3 main categories of AI startups, and they’re distinguished by the kind of AI solution they offer. They are:
1) Native AI Infrastructure:
These are AI startups like OpenAI that focus on building the foundational structures needed to run AI solutions. Think of them as the pace setters with immense physical and computing infrastructure that others rely on.
2) Agentic AI:
These start-ups focus on developing holistic AI solutions that are capable of independent thought and decision-making. They often require minimal supervision or human collaboration to assess workflow contexts and execute logical solutions.
3) Workflow Automation:
These are vertical integrators who build AI solutions that automate specific processes. In most cases, the products are niche-specific e.g., marketing data scraping and reorganization. They’re usually simpler, unlike agentic AI solutions that typically align with an all-in-one suite.
Looking at the 3 types, you’d notice a subtle hierarchy. Workflow automation AI is typically a subset of Agentic AI, which relies on native AI infrastructure to operate.
5 Cyber Risks in Building AI Startups
The beauty of building an AI startup is the fact that the foundation is pre-set. You can simply rely on existing AI infrastructure to build an AI solution for a specific market. Since most of the groundwork in machine learning and AI infrastructure has been done, building an AI startup has become a lot easier. Now, running the necessary market research and iterating a viable solution is as easy as typing a few prompts with existing AI tools.
As amazing as AI sounds, top AI startups are already sounding the alarm. As early as 2024, Cybernews reported on the risks of AI’s potentials being misused and the need for proactive regulation. Somehow, AI’s ability to iterate quickly poses moral and economic risks that cannot be ignored. Unsurprisingly, it’s the startups themselves that’ll be held responsible for any misuse.
So, before you consider building an AI startup, here are 5 cyber risks you should look out for:
1) Generic Code Vulnerabilities
Generally, AI startups are pretty audacious with their products. What will typically require a full team of product developers years to iterate gets force-fed into AI via prompt engineering, and voila – the product is ready. But the generated product is rarely foolproof.
Most times, AI-generated products lack the careful code fine-tuning that’s unique to products built from scratch. Often, a review of the backend code generated by AI shows that it’s a slapstick of generic code with a lot of security risks. Think of it this way, a team of masons carefully raises a building with brick and mortar, compared to a fast machine that simply pours mortar till it gets the desired structure. While both structures may look alike, they’re miles apart in durability and safety. This classically repeats itself with most AI startups who end up having to call in actual experts to review their AI-generated code for vulnerabilities.
2) Generic Design and Model Theft
A classic complaint about AI-generated products is that they’re often identical. Since most AI products rely on the same data and infrastructure, they’re often hard to distinguish, irrespective of the precision in prompt engineering.
While adding more brand resources and subsequent code reviews may help with product distinction, there’s also the security risk of model theft. This happens where a third party uses AI to reverse engineer an AI product to create a clone. With sufficient market campaigns, the clone passes off as the original and is used to scam unsuspecting clients.
3) Prompt Injection and Shadow Logins
A common security flaw of most AI products is that their security features are weak and can be easily bypassed. Unlike traditional product development that typically adopts multi-factor authentication methods, AI products often use fewer authenticators. In a way, the quick-spiritedness of AI and its workflow automation comes at the cost of bypassing serial authentications. This loophole gives attackers the perfect opportunity to manipulate your AI agent to either disregard safety guidelines or gain unauthorized access, or worse – execute malicious commands. As such, reputable outlets on cybersecurity like Cybernews believe that unsupervised AI access often frustrates the whole point of data privacy.
4) Data Poisoning and Loss of Integrity
Bad data is a major challenge for AI as it relies on data to function properly. In building an AI startup, data scraping and authentication cannot be overemphasized. Where an AI product is trained on bad data, it’s only logical that it’ll likely produce bad results.
But the real challenge isn’t in critically assessing the data being fed into the AI product. Instead, it lies in external attacks that introduce bad data. Seeing as most AI-generated products lack strong backend security, there’s a risk that bad actors may bypass authentication to inject bad data. When this happens, a seemingly decent AI product would fail.
5) Data Leak
This is similar to data poisoning and model theft, and all it takes is prompt manipulation. While most AI programs are reviewed to act within specific guidelines, a common issue is that users can and report bypassing these guidelines by carefully crafting their prompts. Highlighting the gravity of it all, Cybernews – a popular cybersecurity news channel noted that AI agents are exposing data faster than defenders can keep up, and what’s worse is that the leaks are hard to trace to the errant AI.
Conclusion
Although AI has leveled the playing fields for startups, there’s still a lot to be done. With traditional business development models, a lot is invested in research and careful development. But with AI, the whole process is automated, and this often skips over critical learning curves that are only encountered during careful development. As such, most AI startups only discover critical flaws in their processes after deploying their services/product. And this shows that no matter how advanced AI may get, the human review and worst-case scenario playbook cannot be replaced.