A couple of weeks ago, the Chinese AI newcomer DeepSeek R1 sent shockwaves through the tech world with its amazing capabilities, compelling story, and incredibly low price point. The official tale claimed they pulled it off with just a little over $5.6 million—an amount that seemed too good to be true (and probably was). Nonetheless, the stock market reacted in its usual way: sensational headlines and panic led to plummeting prices for leading AI stocks on NASDAQ.
DeepSeek’s R1 thinking model rivals OpenAI’s top models but is offered at a mere fraction of the cost to end users. This performance has been corroborated by scientific papers, and because the model is open-source and readily accessible, people have tested it themselves. Of course, industry insiders had already caught wind of its earlier V3 version, although that particular release mostly flew under the radar.
This sudden disruption caused the major AI players to take notice. In response, OpenAI unveiled a new advanced reasoning model for the general public and even made its o3-mini available free of charge. Google, struggling in recent years to remain competitive despite its status as an AI trailblazer, released its newest offering a week after DeepSeek’s big news. Its pricing for Gemini 2.0 significantly undercuts R1. Clearly, an arms race has begun.
As you might expect, however, the story is more nuanced than initial headlines might suggest. Details are emerging daily that cast doubt on the $5.6 million figure, and there’s growing evidence that DeepSeek’s model lacks the necessary guardrails—particularly around Chinese censorship issues that might slip past a casual observer. Memes of DeepSeek awkwardly dodging sensitive inquiries have flooded the internet, harkening back to the similar troubles AI companies have faced (and continue to face) worldwide. Western models have their own censoring mechanisms, of course, but these tend to be subtler to Western audiences.
Why are these censorship measures necessary in the first place? Well, for the same reason you can cook on a gas stove in your kitchen, but wouldn’t keep a roaring bonfire in your living room: powerful, unrestrained forces can be dangerous. Large Language Models (LLMs) are incredibly potent, but their chaotic nature makes them tricky to manage. The same reasoning abilities that let them produce brilliant insights can also turn them into high-risk tools. You cannot guarantee correct answers due to "Model hallucinations". Also, we don’t want a language model casually handing out bomb-making blueprints, just because it “knows” how to do it.
This need for guardrails reminds me of the early web development days, when SQL injection attacks were a going concern. Back then, malicious users could manipulate a site’s database just by crafting the right kind of input. It could still be an issue but protection agains it is much easier to implement. Concepts like "input sanitization" were invented, the industry matured, learned from its mistakes, and now modern frameworks largely shield developers from those threats.
AI, on the other hand, is still quite new—thrilling, but immature. It’s unfamiliar territory for cybersecurity pros, who are used to dealing with tried-and-true threats. With the excitement around DeepSeek, security leaders are scrambling to catch up. For instance, WIZ launched “prompt airlines,” a playground for security experts to experiment with breaking GenAI chat systems. Once DeepSeek hit the headlines, professionals wasted no time testing its vulnerabilities—and their findings weren’t pretty: DeepSeek was discovered to have major security issues like leaking API keys, and it performed dismally against standard AI security benchmarks, ranking dead last among its contemporaries.
This leads us to the bigger picture of Generative AI security. Because enterprises are rapidly adopting AI, malicious actors see an opportunity in the suddenly expanded attack surface. Although AI can help combat new attack vectors—indeed, many startups have sprung up to deliver AI-driven security—it also hands attackers powerful new weapons to craft ever more sophisticated methods. Essentially, the cat-and-mouse game between hackers and defenders has shifted into high gear.
Phishing emails, for example, are no longer so easy to spot by clunky grammar and spelling errors; LLMs can generate polished prose that looks fully legitimate. A curious hacker might use an unprotected model to research new hacking methods. Meanwhile, AI tools are already being harnessed to generate elaborate attacks that weren’t feasible before.
Given all this, it’s no surprise that a wave of cybersecurity startups has risen to meet the AI moment—either by harnessing AI to protect organizations or by specifically targeting the new threats spawned by the AI revolution. One thing is certain: we’re all navigating unpredictable waters, and the stakes—both in potential gains and possible dangers—have never been higher.
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